Unmanned aerial vehicle cluster dynamic task allocation method based on ant lion optimization

Abstract

The invention provides an unmanned aerial vehicle cluster dynamic task allocation method based on ant lion optimization, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: enabling each unmanned aerial vehicle to carry out the information interaction with other unmanned aerial vehicles when scene information changes, generating a bidding matrix according to the bidding price of each unmanned aerial vehicle for a plurality of tasks, each unmanned aerial vehicle initializes a plurality of individuals in an ant lion optimization algorithm based on segmented logarithm fitting, the position of each individual is used for representing the auction price of the unmanned aerial vehicle for a plurality of tasks, each unmanned aerial vehicle updates the auction price in the auction matrix through the ant lion optimization algorithm based on segmented logarithm fitting, a target auction matrix is obtained, and the target auction matrix is sent to the unmanned aerial vehicle; according to a task allocation scheme corresponding to the target bidding matrix, multiple tasks are allocated to multiple unmanned aerial vehicles, an objective function in an ant lion optimization algorithm based on segmented logarithm fitting is used for solving an individual with an optimal fitness value, and an optimal task allocation scheme generated by each unmanned aerial vehicle for multiple tasks is correspondingly solved.

Description

Technical Field

[0001] The present application relates to the field of drone technology, and in particular to a method for dynamic task allocation of drone clusters based on antlion optimization. Background Art

[0002] In recent years, the rapid development of UAV technology has attracted widespread attention around the world. As a technology with great potential, UAVs have shown great application prospects in the military, civil and commercial fields. With the advancement of technology and the reduction of costs, UAVs are gradually developing from single platforms to clustered applications. Clustered UAV systems refer to systems in which multiple UAVs work together to complete complex tasks by sharing information and assigning tasks. Clustered UAV systems face a key problem, namely how to reasonably assign tasks to achieve optimal system performance.

[0003] The current dynamic task allocation of drone clusters has the following problems: 1. Most UAV task allocation problems are based on the assumption that the task environment is static and deterministic, without considering dynamic scene change factors such as changes in UAV information and task information. As a result, the "optimal" task allocation plan under predetermined conditions often fails to achieve the expected results or even cannot be completed.

[0004] 2. The task allocation plan determined by each drone through multiple rounds of bidding is easily affected by the initial bid and requires too many negotiations. Therefore, it has poor flexibility when facing the problem of dynamic task allocation of drone clusters, and the resulting task allocation plan is often unsatisfactory.

[0005] 3. A centralized architecture is usually used for task allocation, which not only requires real-time acquisition of global information, but also requires the central node to completely re-allocate tasks when the drone information or target information changes, which is very costly. Summary of the invention

[0006] In view of this, an embodiment of the present application provides a method for dynamic task allocation of drone clusters based on antlion optimization to overcome the above problems or at least partially solve the above problems.

[0007] A first aspect of an embodiment of the present application provides a method for dynamic task allocation of a drone cluster based on antlion optimization, the method comprising: When the scene information changes, each drone in the drone cluster exchanges information with other drones, wherein the information includes the bidding price of each drone for multiple tasks and the position of each drone; Generate a bidding matrix according to the bidding price of each drone among the multiple drones for the multiple tasks; For each of the drones, a plurality of individuals in the ant lion optimization algorithm based on piecewise logarithmic fitting are initialized respectively, and the position of each individual is used to represent the bidding price of the drone for the plurality of tasks; Each of the drones updates the bidding price in the bidding matrix by using the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a target bidding matrix; Allocating the multiple tasks to the multiple drones according to the task allocation scheme corresponding to the target bidding matrix, wherein the task is allocated to the drone with the highest bidding price; Among them, in the antlion optimization algorithm based on piecewise logarithmic fitting, the objective function in the antlion optimization algorithm based on piecewise logarithmic fitting is used to solve the individual with the best fitness value among the multiple individuals, which corresponds to solving the optimal task allocation plan generated by each drone for multiple tasks in the dynamic task allocation problem of the drone cluster.

[0008] Optionally, each of the drones updates the bidding price in the bidding matrix by using the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a target bidding matrix, including: Calculating the fitness value of each individual in the plurality of individuals, wherein the plurality of individuals include a plurality of ants and a plurality of ant lions; The individual with the best fitness value among the multiple ant lions is selected as an elite ant lion; Each of the plurality of ants selects an ant lion from the plurality of ant lions as a target ant lion by roulette, so that the ant randomly walks between an upper bound and a lower bound according to the selected target ant lion and the elite ant lion, and the upper bound and the lower bound correspond to the maximum competitive value and the minimum competitive value of the drone for the task, respectively; In the process of the ant's random walk, the upper bound and the lower bound are updated using a boundary shrinkage method based on logarithmic fitting; Calculate the fitness value of each ant based on the updated position of the ant; When there is an ant whose fitness value is better than the fitness value of the target ant lion selected by the ant, the position of the target ant lion is updated to the position of the ant that selected the target ant lion, and the ant lion with the best current fitness value is used as the elite ant lion; The above process is re-executed until the maximum number of iterations is reached, and the bidding matrix is ​​updated based on the bidding prices of multiple drones corresponding to multiple elite ant lions for multiple tasks to obtain a target bidding matrix.

[0009] Optionally, for each individual in the multiple individuals corresponding to each drone, the fitness value is used to evaluate the task allocation scheme corresponding to the intermediate bidding matrix, where the intermediate bidding matrix is ​​generated according to the bidding prices of the drone corresponding to the current position of the individual during the random walk for the multiple tasks and the bidding prices of other drones in the information for the multiple tasks; The fitness value is determined based on the number of the drone cluster, the number of tasks, the decision variables of each drone's task assignment to each task, the relative distance between the task and the assigned drone, the speed of the assigned drone, and the time required for the assigned drone to perform the task; Among them, the decision variables for the task allocation of each drone to each task represent: whether the drone is allocated the task in the task allocation scheme corresponding to the intermediate bidding matrix; the allocated drone is the drone to which the task is allocated in the task allocation scheme corresponding to the intermediate bidding matrix.

[0010] Optionally, before calculating the fitness value of each ant, the following steps are also included: Set a random number, and determine whether the position of the ant needs to be updated again according to the random number, the current number of iterations and the maximum number of iterations; When the position of the ant needs to be updated again, the position of the ant is updated according to the golden sine strategy, wherein the probability of updating the position of the ant again decreases as the current number of iterations increases.

[0011] Optionally, the process of determining whether the position of the ant needs to be updated again is: When the random number meets , it is determined that the position of the ant needs to be updated again; in, rand represents the random number, rand∈ (0,1); t represents the current iteration number, and T represents the maximum iteration number.

[0012] Optionally, the process of updating the position of the ant according to the golden sine strategy is: ; in, Indicates the number before the update i The position of the ants, Indicates the updated i The position of the ants; c represents the upper bound of the ant, d represents the lower bound of the ant, the i Dimension refers to the first dimension among multiple individuals. i Individual.

[0013] Optionally, the updating of the upper bound and the lower bound by using a boundary shrinkage method based on logarithmic fitting includes: Setting a proportionality coefficient, wherein the proportionality coefficient is obtained according to a logarithmic fitting function, and the value of the proportionality coefficient increases with the increase of the number of iterations; Based on the proportional coefficient, the upper bound of the ants, and the lower bound of the ants, an upper bound and a lower bound in each round of iteration are determined.

[0014] Optionally, the calculation formula for updating the upper bound and the lower bound is: ; in, represents the proportionality coefficient, represents the upper bound during the t-th iteration, It represents the lower bound in the tth iteration process.

[0015] Optionally, the calculation formula of the proportionality coefficient is: ; in, represents the proportionality coefficient, m represents the first coefficient in the logarithmic fitting function, b n represents the second coefficient in the logarithmic fitting function; t represents the current number of iterations, and T represents the maximum number of iterations; m ∈{1, 2, 3, 4, 5}, n ∈{1, 2, 3, 4, 5}.

[0016] Optionally, each of the drones updates the bidding price in the bidding matrix by using the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a target bidding matrix, including: Each of the drones updates the bidding price in the bidding matrix by using the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a first target bidding matrix; Each of the drones respectively updates the bidding price in the first target bidding matrix by using the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a second target bidding matrix; Determine whether the task allocation scheme corresponding to the second target bidding matrix is ​​the same as the task allocation scheme corresponding to the first target bidding matrix; When the task allocation scheme corresponding to the second target bidding matrix is ​​the same as the task allocation scheme corresponding to the first target bidding matrix, determining the second target bidding matrix as the target bidding matrix; When the task allocation scheme corresponding to the second target bidding matrix is ​​different from the task allocation scheme corresponding to the first target bidding matrix, each drone re-updates the bidding prices of multiple drones for multiple tasks in the second target bidding matrix through the ant lion optimization algorithm based on piecewise logarithmic fitting, and obtains a third target bidding matrix; When the task allocation scheme corresponding to the third target bidding matrix is ​​the same as the task allocation scheme corresponding to the obtained second target bidding matrix, allocating the multiple tasks to the multiple drones according to the task allocation scheme corresponding to the target bidding matrix includes: determining the third target bidding matrix as the target bidding matrix, and allocating the multiple tasks to the multiple drones according to the task allocation scheme corresponding to the updated third target bidding matrix.

[0017] Beneficial effects of this application: The present application provides a method for dynamic task allocation of a drone cluster based on ant lion optimization, the method comprising: when scene information changes, each drone in the drone cluster exchanges information with other drones, the information comprising the bidding price of each drone among multiple drones for multiple tasks and the position of each drone; generating a bidding matrix according to the bidding price of each drone among multiple drones for multiple tasks; for each drone, respectively initializing multiple individuals in an ant lion optimization algorithm based on piecewise logarithmic fitting, the position of each individual is used to represent the bidding price of the drone for multiple tasks; each drone, respectively, updates the bidding price in the bidding matrix through the ant lion optimization algorithm based on piecewise logarithmic fitting to obtain a target bidding matrix; according to the task allocation scheme corresponding to the target bidding matrix, the multiple tasks are allocated to the multiple drones, and the tasks are allocated to the drone with the highest bidding price; wherein, in the ant lion optimization algorithm based on piecewise logarithmic fitting, the objective function in the ant lion optimization algorithm based on piecewise logarithmic fitting is used to solve the individual with the best fitness value among the multiple individuals, which corresponds to solving the optimal task allocation scheme generated by each drone for multiple tasks in the dynamic task allocation problem of the drone cluster.

[0018] Through the technical solution of the present application, when the scene information changes, the drones exchange information in real time, and then each dynamically adjusts the bidding price through the ant lion optimization algorithm, so that the task allocation plan can quickly adapt to the changes in the task environment, solves the problem of the task allocation plan failing due to dynamic changes in the environment, and improves the adaptability and flexibility of task allocation. In addition, each drone dynamically adjusts the bidding price of the drone through the ant lion optimization algorithm, and then updates the bidding matrix to obtain the target bidding matrix. This method can not only reduce the impact of the initial bid on the result, but also reduce the number of communications required for negotiation between drones, thereby improving the task allocation efficiency of the drone cluster. In addition, since each drone uses the ant lion optimization algorithm to adjust the bidding price, this distributed architecture reduces the reliance on global information and reduces the cost of reallocation when the task environment changes, thereby significantly improving the task execution capability of the drone cluster in a complex dynamic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application.

[0020] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the description of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 It is a flow chart of a method for dynamic task allocation of drone clusters based on antlion optimization provided by an embodiment of the present application; Figure 2 It is an overall framework diagram of a method for dynamic task allocation of drone clusters based on antlion optimization provided in one embodiment of the present application; Figure 3 It is a change diagram of the classic I value and the I value based on the logarithmic fitting function provided in an embodiment of the present application; Figure 4 It is a schematic diagram of a framework of a drone cluster dynamic task allocation device based on antlion optimization provided by an embodiment of the present application; Figure 5 It is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other.

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0024] Figure 1 is a flowchart of a method for dynamic task allocation of drone clusters based on antlion optimization provided by an embodiment of the present application. Figure 2 This is an overall framework diagram of a method for dynamic task allocation of drone clusters based on antlion optimization provided in one embodiment of the present application.

[0025] refer to Figure 1 , an embodiment of the present application provides a method for dynamic task allocation of drone clusters based on antlion optimization, the method comprising steps S11 to S15: Step S11, when the scene information changes, each drone in the drone cluster exchanges information with other drones, wherein the information includes the bidding price of each drone for multiple tasks and the position of each drone.

[0026] In this embodiment, reference Figure 2 This technical solution is applicable to the dynamic task allocation process of the drone cluster after each scene change. Scene information refers to information related to the drone cluster and the task. In the dynamic task allocation of the drone cluster, when the scene information changes (such as the position, status and / or number of drones, the location of the target corresponding to the task, the priority of the task, the number of tasks, etc.), each drone will interact with other drones in the drone cluster to transmit its own information and receive information from other drones. In this process, the information exchanged within the drone cluster includes the bidding price of each drone for multiple tasks and the position of each drone, so as to ensure that all drones can obtain the latest task and environmental information in real time, thereby providing accurate data input for subsequent task allocation.

[0027] Step S12: generating a bidding matrix according to the bidding price of each drone among the multiple drones for the multiple tasks.

[0028] In this embodiment, each drone will bid for multiple tasks, and a bidding matrix is ​​generated according to the bidding price of each drone in the multiple drones for the multiple tasks. The bidding matrix can be understood as the initial bidding matrix before dynamic task allocation, and the bidding prices in the initial bidding matrix are generated according to the bidding prices obtained by multiple drones through information interaction when the scene information changes.

[0029] For example, assume that the drone cluster contains two drones, namely, the drone with UAV number 1 and the drone with UAV number 2 (hereinafter referred to as drone 1 and drone 2). The bidding matrix (initial bidding matrix) is shown in Table 1, where each row represents the bidding price of a drone for multiple tasks, each column represents the bidding prices of multiple drones received for a task, and the element value in the matrix represents the bidding price of the drone for the corresponding task.

[0030] For example, in the bidding matrix, the bidding prices of the UAV with UAV number 1 (in the first row of the bidding matrix) for the five tasks with task sequence 1 to task sequence 5 are: 0, 300.74, 0, 0, 381.98 respectively; the bidding prices of the UAV with UAV number 2 (in the second row of the bidding matrix) for the five tasks with task sequence 1 to task sequence 5 (hereinafter referred to as Task 1 to Task 5) are: 417.38, 0, 388.92, 449.17, 0 respectively.

[0031] It should be noted that when the scene information changes, the bidding price in the initial bidding matrix is ​​obtained based on the relative position between the drone and each task and the maximum bidding value. For example, if the maximum bidding value is 500, it means that the bidding price generated by the drone cannot exceed 500. When the relative position between the drone and the task is 100, the bidding price of the drone for the task is 500-100=400. Therefore, the closer the relative position of the drone to the target of the task, the higher the corresponding bidding price, and the more likely the drone will be assigned the task.

[0032] The bidding rule is to assign tasks to drones with higher bids. Therefore, as an example, the task allocation scheme corresponding to the bidding matrix (initial bidding matrix) is: assign tasks 2 and 5 to drone 1, and assign tasks 1, 3, and 4 to drone 2. At the same time, since drone 1's bid for task 5 is higher than its bid for task 2, drone 1 has a higher priority for executing task 5 than for executing task 2. The same is true for drone 2's task execution priority, which will not be repeated.

[0033] It should be noted that the task allocation plan generation process of the bidding matrix (initial bidding matrix) is only an example. In fact, the bidding prices in the bidding matrix (initial bidding matrix) need to be adjusted, so there is no need to generate a task allocation plan for the bidding matrix (initial bidding matrix).

[0034] Table 1

[0035] Step S13, for each of the drones, a plurality of individuals in the ant lion optimization algorithm based on piecewise logarithmic fitting are initialized respectively, and the position of each individual is used to represent the bidding price of the drone for the plurality of tasks.

[0036] In this embodiment, for each drone, multiple individuals in the antlion optimization algorithm based on piecewise logarithmic fitting need to be initialized respectively. The antlion optimization algorithm is an optimization algorithm based on nature inspiration, which simulates the behavior of antlions preying on ants, thereby dynamically adjusting the bids of drones for tasks. When individuals randomly wander in the search space, the positions of individuals will change, and the positions of individuals can represent the bidding prices of the corresponding drones for multiple tasks, and for a solution in the search space.

[0037] The initialization process uses the chaotic mapping population method (Logistic mapping) to give each individual in the population a random initial position, thereby increasing the diversity of the initial solution and preventing the algorithm from falling into the local optimum.

[0038] Specifically, the process of chaotic mapping population is as follows:

[0039]

[0040] Each drone will run the Ant Lion optimization algorithm on its own. The algorithm iteration is based on the population. There are multiple individuals in the population. Indicates i dimensional individuals, and the individuals in the population need a numerical value as the initial position of the individual.

[0041] In the formula, is the initialization parameter. Depend on Decide, therefore, to randomly generate After that, you can directly generate a series of and through Go to Generate , thereby increasing the diversity of individuals in the population.

[0042] Step S14: Each of the drones updates the bidding price in the bidding matrix through the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a target bidding matrix.

[0043] In this embodiment, each drone dynamically updates the bidding price in the bidding matrix through the antlion optimization algorithm based on piecewise logarithmic fitting, and finally obtains the target bidding matrix. The updating process includes individual (ant) random walk, boundary shrinkage strategy and golden sine strategy, etc. These mechanisms work together to help drones optimize bidding strategies in a dynamic environment and gradually approach the optimal task allocation solution.

[0044] Among them, in the process of individual random walk, the random movement of ants on the edge of the antlion trap is simulated to explore new solution space, which corresponds to exploring the better bidding price of drones for multiple tasks. The boundary shrinkage strategy is to dynamically adjust the upper and lower bounds of the search range through piecewise logarithmic fitting, so that the antlion optimization algorithm can focus on the optimal solution in the later stage. The golden sine strategy increases the diversity of solutions through the nonlinear characteristics of the golden ratio and the sine function, avoiding the antlion optimization algorithm from falling into the local optimum during the boundary shrinkage process.

[0045] For example, the ant lion builds a trap and the ants are trapped in the ant lion trap and walk randomly. The random walk method is as follows: ; In the formula, t is the current iteration number, T is the maximum iteration number, X(t) represents the random walk position, cumsum represents the cumulative sum of random walk step lengths, and r is the generating function of the random walk step length, which is calculated as ,in rand Represents a random number between (0,1).

[0046] Step S15: allocating the multiple tasks to the multiple drones according to the task allocation scheme corresponding to the target bidding matrix, wherein the tasks are allocated to the drone with the highest bidding price.

[0047] Among them, in the antlion optimization algorithm based on piecewise logarithmic fitting, the objective function in the antlion optimization algorithm based on piecewise logarithmic fitting is used to solve the individual with the best fitness value among the multiple individuals, which corresponds to solving the optimal task allocation plan generated by each drone for multiple tasks in the dynamic task allocation problem of the drone cluster.

[0048] In this embodiment, after the target bidding matrix is ​​obtained, multiple tasks are respectively allocated to the drones with the highest corresponding bid prices in the target bidding matrix according to the task allocation scheme corresponding to the target bidding matrix.

[0049] Through the technical solution of the above embodiment, when the scene information changes, the drones exchange information in real time, and then each dynamically adjusts the bidding price through the ant lion optimization algorithm, so that the task allocation plan can quickly adapt to the changes in the task environment, solves the problem of the task allocation plan failing due to dynamic changes in the environment, and improves the adaptability and flexibility of task allocation. In addition, each drone dynamically adjusts the bidding price of the drone through the ant lion optimization algorithm, and then updates the bidding matrix to obtain the target bidding matrix. This method can not only reduce the impact of the initial bid on the result, but also reduce the number of communications required for negotiation between drones, thereby improving the task allocation efficiency of the drone cluster. In addition, since each drone uses the ant lion optimization algorithm to adjust the bidding price, this distributed architecture reduces the reliance on global information and reduces the cost of reallocation when the task environment changes, thereby significantly improving the task execution capability of the drone cluster in a complex dynamic environment.

[0050] In combination with the above embodiments, the present application further provides another method for dynamic task allocation of drone clusters based on antlion optimization. In this method, the step S14 of "each drone, respectively, updates the bidding price in the bidding matrix through the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a target bidding matrix" specifically includes steps S14-1 to S14-7: Step S14-1, calculating the fitness value of each individual in the multiple individuals, where the multiple individuals include multiple ants and multiple ant lions.

[0051] In this embodiment, for each drone, when executing the ant lion optimization algorithm, it is first necessary to calculate the fitness values ​​of multiple individuals (including ants and ant lions). The positions of these individuals represent the different bidding prices of the drone for multiple tasks, and the position of each individual corresponds to a fitness value. When the position of the individual changes, the fitness value will change. The fitness value is used to measure the bidding price of the drone for multiple tasks in the bidding matrix. The bid price is dynamically adjusted to: the bid price of the individual position corresponding to the multiple tasks, and the task allocation scheme corresponding to the updated bidding matrix is ​​obtained.

[0052] In the process of individual random walk, the task allocation plan corresponding to the updated bidding matrix can be understood as follows: the bidding price of the drone executing the antlion optimization algorithm is updated, while the bidding prices of other drones in the initial bidding matrix remain unchanged, thus obtaining a new task allocation plan.

[0053] For example, the objective function of the dynamic task allocation problem of a drone swarm is to minimize the time required for each drone to complete the task from the time it receives the task to the time all drones complete the task. The fitness value can be used to determine the pros and cons of the new task allocation plan by evaluating the length of time corresponding to the new task allocation plan, and provide a basis for the dynamic optimization of subsequent algorithms.

[0054] Step S14-2: taking the individual with the best fitness value among the multiple ant lions as the elite ant lion.

[0055] In this embodiment, after calculating the fitness values ​​of all individuals, the individual with the best fitness value is selected from multiple ant lions as the elite ant lion. The elite ant lion represents the best strategy for the drone to allocate tasks during the iteration of the current algorithm, and the position of the elite ant lion (i.e., the bidding price) is the core goal of the algorithm optimization. By selecting elite ant lions, other individuals (ants) can be guided to move to a better solution in the search space, thereby gradually approaching the global optimal solution.

[0056] Step S14-3, each ant among the multiple ants selects an ant lion from the multiple ant lions as a target ant lion through roulette, so that the ant randomly walks between an upper bound and a lower bound according to the selected target ant lion and elite ant lion, and the upper bound and the lower bound correspond to the maximum competitive value and the minimum competitive value of the drone for the task, respectively.

[0057] In this embodiment, after the elite ant lion is selected, each ant selects an ant lion from multiple ant lions as a target ant lion through a roulette mechanism. The roulette mechanism is a probability-based selection method that can assign different selection probabilities to ant lions according to their fitness values. The higher the fitness value, the greater the probability of the ant lion being selected. After selecting the target ant lion, the ant will randomly walk between the upper bound and the lower bound in the search space under the guidance of the target ant lion and the elite ant lion.

[0058] It should be noted that in the scenario of dynamic task allocation of drone clusters, the upper and lower bounds of multiple individuals corresponding to the drones are the same. The upper and lower bounds are used to define the scope of individual search. The upper and lower bounds correspond to the maximum and minimum bidding values ​​of the drone for the task, respectively. The maximum bidding value indicates the highest value that the current drone can bid for the task, and the minimum bidding value indicates the lowest value that the current drone can bid for the task. In this way, individuals can explore different bidding prices within a given range.

[0059] Step S14-4, during the random walk of the ants, the upper bound and the lower bound are updated using a boundary shrinkage method based on logarithmic fitting. In this embodiment, during the random walk of ants, the ant lion optimization algorithm dynamically updates the upper and lower bounds using a boundary contraction method based on logarithmic fitting. The contraction speed of the search range is controlled by a piecewise logarithmic function. As the iteration proceeds, the upper and lower bounds gradually approach each other, causing the search range to gradually shrink. This dynamic adjustment mechanism enables the algorithm to perform a global search in the early stages and focus on local searches in the later stages, thereby improving the accuracy and efficiency of the search. Through boundary contraction, the algorithm can more finely adjust the bidding price of the drone and approach the optimal task allocation solution.

[0060] Step S14-5, calculating the fitness value of each ant according to the updated position of the ant.

[0061] In this embodiment, each time an ant randomly walks and updates its position, the algorithm recalculates its fitness value based on the ant's new position. The update of the fitness value reflects the quality of the ant's task allocation under the new bidding price strategy. By comparing the updated fitness value with the previous value, the algorithm can evaluate whether the ant's movement has brought about the optimization effect of the updated task allocation scheme.

[0062] Step S14-6, when there is an ant whose fitness value is better than the fitness value of the target ant lion selected by the ant, the position of the target ant lion is updated to the position of the ant that selected the target ant lion, and the ant lion with the best current fitness value is used as the elite ant lion.

[0063] In this embodiment, when the fitness value of an ant is better than the fitness value of the target ant lion it selected, it means that the ant has found a better task allocation solution. At this time, the position of the target ant lion is updated to the position of the ant, and the elite ant lions in the current population are re-evaluated, and each ant re-selects the target ant lion. In this way, the algorithm continuously updates the position of the ant lion, pushing the entire population to move to a better solution space, ensuring the dynamic adjustment ability of the algorithm and avoiding the algorithm from falling into a local optimal solution.

[0064] Step S14-7, re-execute the process of steps S14-1 to S14-6 until the maximum number of iterations is reached, and each drone selects the final elite ant lion through multiple iterations of the ant lion optimization algorithm, and each drone will once again exchange information on its bidding prices for multiple tasks. Based on the bidding prices of multiple drones corresponding to multiple elite ant lions for multiple tasks, the bidding prices of each drone in the bidding matrix are updated according to the bidding prices of the drones corresponding to the elite ant lions for multiple tasks, and the target bidding matrix is ​​obtained.

[0065] Through the technical solution of the above embodiment, the antlion optimization algorithm based on piecewise logarithmic fitting can effectively solve the complex problems in the dynamic task allocation of drone clusters. In this technical solution, only two information interactions are required between drone clusters to obtain the optimal task allocation solution, which can not only adapt to the dynamic changes of the task environment, but also reduce the impact of the initial bid on the allocation results, reduce the number of negotiations, and improve the flexibility and efficiency of task allocation.

[0066] In combination with the above embodiments, the present application also provides another method for dynamic task allocation of drone clusters based on antlion optimization. In this method, for the fitness value of each individual among multiple individuals corresponding to each drone, the fitness value is used to evaluate the task allocation scheme corresponding to the intermediate bidding matrix. The intermediate bidding matrix is ​​generated based on the bidding price of the drone for multiple tasks corresponding to the current position of the individual during the random walk and the bidding prices of other drones for multiple tasks in the information.

[0067] That is to say, every time an individual (ant) walks, the change in position corresponds to a change in the bidding price of the drone. We update the changed bidding price in the bidding matrix, replace the original bidding price of the drone with the changed bidding price, and keep the bidding prices of other drones unchanged, thereby obtaining an intermediate bidding matrix. The fitness value is used to measure the quality of the intermediate bidding matrix to evaluate the task allocation plan corresponding to the intermediate bidding matrix. In addition, the intermediate bidding matrix can be understood as a product of the execution process of the ant lion optimization algorithm, and will not interact with other drones, thereby ensuring the number of negotiations between drone clusters.

[0068] The fitness value is determined based on the number of the drone cluster, the number of tasks, the decision variables of each drone's task assignment to each task, the relative distance between the task and the assigned drone, the speed of the assigned drone, and the time required for the assigned drone to perform the task; Among them, the decision variables for the task allocation of each drone to each task represent: whether the drone is allocated the task in the task allocation scheme corresponding to the intermediate bidding matrix; the allocated drone is the drone to which the task is allocated in the task allocation scheme corresponding to the intermediate bidding matrix.

[0069] Among them, the fitness value F The calculation formula is:

[0070] In the fitness value F In the calculation formula, is the number of drones, is the number of tasks, It is i A drone against j The decision variable for task assignment of tasks is {0,1}. If the drone i On Task j If the task is executed on ,otherwise , It's a drone i Arrival Mission j The distance you need to fly, It's a drone i speed, It's a drone on a mission j The time required to perform the task.

[0071] That is to say, according to the bidding prices of multiple drones for each task in the intermediate bidding matrix, when a drone has the highest bidding price for a task, the decision variable of the task assignment of this drone to this task is expressed as the drone is assigned the task, then , while other drones are .

[0072] It should be noted that according to the formula, the smaller the fitness value F is, the better the fitness value is. In addition, it should be distinguished that in the fitness value F In the calculation formula i The i Victoria i Different fitness values F In the calculation formula i Only refers to i A drone.

[0073] In combination with the above embodiments, the present application also provides another method for dynamic task allocation of drone clusters based on antlion optimization. In this method, before calculating the fitness value of each ant in step S14-5, steps S21 to S22 are also included: Step S21, setting a random number, and judging whether the position of the ant needs to be updated again according to the random number, the current number of iterations and the maximum number of iterations.

[0074] In this embodiment, before calculating the fitness value of each ant, in order to effectively increase the diversity of solutions and prevent the algorithm from falling into a local optimum, a mechanism is used to determine whether the ant position needs to be updated again by setting a random number.

[0075] Specifically, the algorithm will determine whether the ant's position needs to be updated again based on the random number, the current number of iterations t and the maximum number of iterations T, so that the ant's position update not only depends on the evaluation of the fitness value, but is also affected by random factors and iteration progress. As the number of iterations increases, the probability of updating the ant's position again will decrease, making the ant more inclined to global search in the early stage, and gradually reducing randomness in the later stage, focusing on local search, so as to improve the convergence speed and accuracy of the algorithm.

[0076] Step S22, when the position of the ant needs to be updated again, the position of the ant is updated according to the golden sine strategy, wherein the probability of updating the position of the ant again decreases as the current number of iterations increases.

[0077] In this embodiment, when the position of the ant needs to be updated again, the algorithm will be updated according to the golden sine strategy. The golden sine strategy combines the optimization strategy of the golden ratio and the sine function, and through its nonlinear characteristics and dynamic adjustment capabilities, it can effectively increase the diversity of solutions and avoid the algorithm from falling into the local optimum.

[0078] In an optional embodiment, the process of "determining whether the position of the ant needs to be updated again" in step S21 is specifically step S21-1: Step S21-1, when the random number satisfies , it is determined that the position of the ant needs to be updated again; in, rand represents the random number, rand It is a value between 0 and 1; t represents the current number of iterations, and T represents the maximum number of iterations.

[0079] In this embodiment, as the current number of iterations t increases, The probability of updating the ant position will become smaller and smaller. Therefore, as the number of iterations increases, the probability of updating the ant position again will decrease. This probabilistic judgment mechanism based on random numbers and iteration progress provides a flexible dynamic adjustment capability, which enables the ant position update to be adaptively adjusted according to the actual situation during the iteration process, thereby achieving a balance between global search and local search. Through this mechanism, the solution space can be quickly explored in the early stage, and gradually focused on the optimal solution in the later stage, improving the optimization efficiency and solution quality.

[0080] In combination with the above embodiment, in an optional embodiment, optionally, the process of updating the position of the ant according to the golden sine strategy is: ; in, Indicates the number before the update i The position of the ants, Indicates the updated i The position of the ants; c represents the upper bound of the ant, d represents the lower bound of the ant, the i Dimension refers to the first dimension among multiple individuals. i Individual.

[0081] In combination with the above embodiment, in an optional embodiment, the step S14-4 of "updating the upper bound and the lower bound using a boundary shrinkage method based on logarithmic fitting" specifically includes step S14-4-1 and step S14-4-2: Step S14-4-1, setting a proportional coefficient, wherein the proportional coefficient is obtained based on a logarithmic fitting function, and the value of the proportional coefficient increases with the increase in the number of iterations.

[0082] In this embodiment, in the method for dynamic task allocation of drone clusters based on antlion optimization, in order to more efficiently adjust the bidding price of drones for tasks, a boundary shrinkage method based on logarithmic fitting is used to dynamically update the upper and lower bounds of ants.

[0083] Specifically, a proportional coefficient is first set. The proportional coefficient is used to shrink the upper and lower bounds. The proportional coefficient is obtained through a logarithmic fitting function, and its value increases with the number of iterations. That is, a larger search range is maintained in the early stage for global exploration, and the search range is gradually narrowed in the later stage to focus on local optimization. In this way, the algorithm can balance the capabilities of global search and local search at different stages and improve optimization efficiency.

[0084] Step S14-4-2, based on the proportional coefficient, the upper bound of the ants, and the lower bound of the ants, determine the upper bound and the lower bound in each round of iteration.

[0085] In this embodiment, in each round of iteration, the algorithm determines a new upper bound and a new lower bound based on the above-mentioned proportionality coefficient and the current upper bound and lower bound.

[0086] Specifically, the new upper and lower bounds are adjusted according to the proportional coefficient, so that the search range gradually shrinks. This shrinking method not only helps the algorithm to quickly explore the solution space in the early stage, but also allows for more fine-tuning of the drone's bidding price in the later stage, thereby approaching the optimal task allocation solution. By dynamically updating the upper and lower bounds, the algorithm can better adapt to changes in the task environment and reduce the situation where the task allocation is not ideal due to initial bids or dynamic changes in the environment.

[0087] Optionally, the calculation formula for updating the upper bound and the lower bound is: ; in, represents the proportionality coefficient, represents the upper bound during the t-th iteration, It represents the lower bound in the tth iteration process.

[0088] In combination with the above embodiment, in an optional embodiment, the calculation formula of the proportionality coefficient is: ; in, represents the proportionality coefficient, m represents the first coefficient in the logarithmic fitting function, b n represents the second coefficient in the logarithmic fitting function; t represents the current number of iterations, and T represents the maximum number of iterations; m ∈{1, 2, 3, 4, 5}, n ∈{1, 2, 3, 4, 5}.

[0089] Specifically, m and b n is the coefficient of the logarithmic fitting function. The specific value of I at each node can be obtained from the I value calculation formula of the classic ALO. The I value formula of the classic ALO is as follows:

[0090] The values ​​of w are as follows:

[0091] Figure 3 is a graph showing the variation of the classic I value and the I value based on the logarithmic fitting function provided in one embodiment of the present application. Figure 3 As can be seen from the figure, the I value of the classic ALO ( Figure 3 The curve above in the figure increases with the increase of the number of iterations t, and when at a node, the value of I will change in a jumpy way. The sudden change of the value of I will cause the range between the upper bound and the lower bound to suddenly shrink when at each node, resulting in a sharp reduction in the search boundary. This sudden change may cause the algorithm to focus too much on the local area at certain nodes, thereby affecting the ability of global search. The technical solution of this embodiment adopts the technical concept of piecewise logarithmic fitting. The value of I in this embodiment ( Figure 3 The change of I value at the node in each stage is smoother, which avoids the problem of abrupt reduction of the search boundary due to sudden change of value and improves the global search capability, stability and adaptability of the algorithm.

[0092] For the fitting of the I value in this embodiment, each segment of the fitting curve m and b n The value of can be determined through each node. When t is 0.1T / 0.5T / 0.75T / 0.9T / 0.95T / T, I is 1 / 50 / 750 / 9000 / 95000 / 1000000 respectively. Then, according to the above values, the curve can be fitted through the points (0.1T, 1) and (0.5T, 50). ; Fit the curve through the points (0.5T, 50) and (0.75T, 750) ; Fit the curve through the points (0.75T, 750) and (0.9T, 9000) ; Fit the curve through the points (0.9T, 9000) and (0.95T, 95000) ; Fit the curve through the points (0.95T, 95000) and (T, 1000000)

[0093] In combination with the above embodiment, in an optional embodiment, the step S14 of "each drone, respectively, updates the bidding price in the bidding matrix through the ant lion optimization algorithm based on piecewise logarithmic fitting to obtain a target bidding matrix" specifically includes steps S31 to S36: In step S31, each of the drones updates the bidding price in the bidding matrix by using the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a first target bidding matrix.

[0094] In this embodiment, reference Figure 2 , each drone updates the bidding price in the initial bidding matrix through the ant lion optimization algorithm based on piecewise logarithmic fitting to obtain the first target bidding matrix. This process involves mechanisms such as ant random walk, boundary shrinkage and golden sine strategy to dynamically adjust the bid of the drone for the task. This process is similar to the process of obtaining the target bidding matrix mentioned above.

[0095] Step S32: Each of the drones re-updates the bidding prices in the first target bidding matrix through the ant lion optimization algorithm based on piecewise logarithmic fitting to obtain a second target bidding matrix.

[0096] In this embodiment, after obtaining the first target bidding matrix, in order to ensure that the obtained task allocation scheme is stable, each drone needs to pass through the ant lion optimization algorithm based on piecewise logarithmic fitting again, and update the bidding price in the first target bidding matrix to obtain the second target bidding matrix. In order to optimize the task allocation scheme, the bid of the drone for the task is closer to the optimal solution. That is to say, each drone needs to execute the ant lion optimization algorithm at least twice, and the process of obtaining the second target bidding matrix is ​​similar to the process of obtaining the target bidding matrix, except that the updated bidding matrix is ​​the first target bidding matrix obtained.

[0097] Step S33, determining whether the task allocation scheme corresponding to the second target bidding matrix is ​​the same as the task allocation scheme corresponding to the first target bidding matrix.

[0098] In this embodiment, it is determined whether the task allocation scheme corresponding to the second target bidding matrix is ​​the same as the task allocation scheme corresponding to the first target bidding matrix. If they are the same, it means that the task allocation scheme has stabilized and can be determined to be the optimal solution.

[0099] Step S34: when the task allocation scheme corresponding to the second target bidding matrix is ​​the same as the task allocation scheme corresponding to the first target bidding matrix, the second target bidding matrix is ​​determined to be the target bidding matrix.

[0100] In this embodiment, when the task allocation scheme corresponding to the second target bidding matrix is ​​the same as the task allocation scheme corresponding to the first target bidding matrix, the second target bidding matrix is ​​determined as the target bidding matrix. This means that the task allocation scheme has converged, and the task allocation of the drone cluster is performed according to this task allocation scheme.

[0101] Step S35, when the task allocation scheme corresponding to the second target bidding matrix is ​​different from the task allocation scheme corresponding to the first target bidding matrix, each drone re-updates the bidding prices of multiple drones for multiple tasks in the second target bidding matrix through the antlion optimization algorithm based on piecewise logarithmic fitting, and obtains the third target bidding matrix.

[0102] In this embodiment, when the task allocation scheme corresponding to the second target bidding matrix is ​​different from the task allocation scheme corresponding to the first target bidding matrix, each drone needs to re-go through the antlion optimization algorithm based on piecewise logarithmic fitting, and iterate multiple times to update the bidding prices of multiple drones for multiple tasks in the second target bidding matrix to obtain the third target bidding matrix. The process of obtaining the second target bidding matrix is ​​similar to the above-mentioned process of obtaining the target bidding matrix. The third target bidding matrix is ​​generated by updating the bidding matrix obtained by the last execution of the antlion optimization algorithm, and the third target bidding matrix is ​​obtained by executing the antlion optimization algorithm at least three times.

[0103] Step S36, when the task allocation scheme corresponding to the third target bidding matrix is ​​the same as the task allocation scheme corresponding to the obtained second target bidding matrix, allocating the multiple tasks to the multiple drones according to the task allocation scheme corresponding to the target bidding matrix, including: determining the third target bidding matrix as the target bidding matrix, and allocating the multiple tasks to the multiple drones according to the task allocation scheme corresponding to the updated third target bidding matrix.

[0104] In this embodiment, when the task allocation scheme corresponding to the third target bidding matrix is ​​the same as the task allocation scheme corresponding to the obtained second target bidding matrix, the ant lion optimization algorithm is no longer executed in a loop, and the third target bidding matrix is ​​determined as the target bidding matrix. According to the task allocation scheme corresponding to the updated third target bidding matrix, multiple tasks are allocated to multiple drones, ensuring that the task allocation scheme is stable.

[0105] Through the above embodiment, by judging the stability of the task allocation scheme, the algorithm can avoid frequent changes in the task allocation scheme caused by randomness and dynamic adjustment in the iteration process. When the task allocation scheme is stable, it is considered that the algorithm has found a reliable solution. In this embodiment, the obtained task allocation scheme is verified to be stable by executing the ant lion optimization algorithm at least twice in a row, thereby ensuring the reliability of the task allocation of the drone cluster obtained in the end.

[0106] As an example, the pseudo code of the antlion optimization algorithm based on piecewise logarithmic fitting is as follows:

[0107] Based on the same inventive concept, another embodiment of the present application further provides a drone cluster dynamic task allocation device based on antlion optimization. Figure 4 This is a schematic diagram of a framework of a dynamic task allocation device for drone swarms based on antlion optimization provided by an embodiment of the present application, with reference to Figure 4 , the device comprises: An information interaction module 11, configured to enable each drone in the drone cluster to interact with other drones when scene information changes, wherein the information includes the bidding price of each drone for multiple tasks and the position of each drone among the multiple drones; A bidding matrix generating module 12, for generating a bidding matrix according to the bidding price of each drone among the plurality of drones for the plurality of tasks; An initialization module 13 is used to initialize, for each of the drones, a plurality of individuals in the ant lion optimization algorithm based on piecewise logarithmic fitting, wherein the position of each individual is used to represent the bid price of the drone for the plurality of tasks; The bidding price updating module 14 is used for each of the drones to update the bidding prices in the bidding matrix by using the ant lion optimization algorithm based on piecewise logarithmic fitting to obtain a target bidding matrix; A task allocation module 15 is used to allocate the multiple tasks to the multiple drones according to the task allocation scheme corresponding to the target bidding matrix, and the task is allocated to the drone with the highest bidding price; Among them, in the antlion optimization algorithm based on piecewise logarithmic fitting, the objective function in the antlion optimization algorithm based on piecewise logarithmic fitting is used to solve the individual with the best fitness value among the multiple individuals, which corresponds to solving the optimal task allocation plan generated by each drone for multiple tasks in the dynamic task allocation problem of the drone cluster.

[0108] Optionally, the initialization module 13 includes: A calculation unit, calculating the fitness value of each individual in the plurality of individuals, wherein the plurality of individuals include a plurality of ants and a plurality of ant lions; An elite ant lion determining unit, configured to select an individual with the best fitness value among the plurality of ant lions as an elite ant lion; a target ant lion selection unit, for each of the plurality of ants, to select an ant lion from the plurality of ant lions as a target ant lion by roulette, so that the ant randomly walks between an upper bound and a lower bound according to the selected target ant lion and elite ant lion, wherein the upper bound and the lower bound correspond to the maximum competitive value and the minimum competitive value of the drone for the task, respectively; A boundary shrinkage unit, used to update the upper bound and the lower bound by using a boundary shrinkage method based on logarithmic fitting during the random walk of the ants; A fitness value calculation unit, used to calculate the fitness value of each ant according to the updated position of the ant; A target ant lion position updating unit, configured to update the position of the target ant lion to the position of the ant that selected the target ant lion when there is an ant whose fitness value is better than the fitness value of the target ant lion selected by the ant, and to use the ant lion with the best current fitness value as the elite ant lion; The iteration unit is used to re-execute the above process until the maximum number of iterations is reached, and the bidding matrix is ​​updated based on the bidding prices of multiple drones corresponding to multiple elite ant lions for multiple tasks to obtain a target bidding matrix.

[0109] Optionally, the device further comprises: A judgment module, used to set a random number before calculating the fitness value of each ant, and judge whether the position of the ant needs to be updated again according to the random number, the current iteration number and the maximum iteration number; The ant position updating module is used to update the position of the ant according to the golden sine strategy when the position of the ant needs to be updated again, wherein the probability of updating the position of the ant again decreases with the increase of the current iteration number.

[0110] Optionally, the judging module includes: The judgment unit is used to determine when the random number meets the , it is determined that the position of the ant needs to be updated again; in, rand represents the random number, rand∈ (0,1); t represents the current iteration number, and T represents the maximum iteration number.

[0111] Optionally, the boundary shrinking unit includes: A setting subunit is used to set a proportional coefficient, wherein the proportional coefficient is obtained according to a logarithmic fitting function, and the value of the proportional coefficient increases with the increase of the number of iterations; The boundary determination subunit is used to determine the upper bound and the lower bound in each round of iteration based on the proportional coefficient, the upper bound of the ants, and the lower bound of the ants.

[0112] Optionally, the bidding price updating module 14 includes: A first target bidding matrix acquisition unit, used for each of the drones to update the bidding prices in the bidding matrix by using the ant lion optimization algorithm based on piecewise logarithmic fitting to obtain a first target bidding matrix; A second target bidding matrix acquisition unit is used for each of the drones to re-update the bidding prices in the first target bidding matrix through the ant lion optimization algorithm based on piecewise logarithmic fitting to obtain a second target bidding matrix; A first judgment unit is used to judge whether the task allocation scheme corresponding to the second target bidding matrix is ​​the same as the task allocation scheme corresponding to the first target bidding matrix; a first determining unit, configured to determine that the second target bidding matrix is ​​the target bidding matrix when the task allocation scheme corresponding to the second target bidding matrix is ​​the same as the task allocation scheme corresponding to the first target bidding matrix; A third target bidding matrix acquisition unit is used for, when the task allocation scheme corresponding to the second target bidding matrix is ​​different from the task allocation scheme corresponding to the first target bidding matrix, each drone re-updates the bidding prices of multiple drones for multiple tasks in the second target bidding matrix through the ant lion optimization algorithm based on piecewise logarithmic fitting for multiple iterations to obtain a third target bidding matrix; An execution unit is used to determine that the third target bidding matrix is ​​the target bidding matrix when the task allocation scheme corresponding to the third target bidding matrix is ​​the same as the task allocation scheme corresponding to the obtained second target bidding matrix; and the multiple tasks are allocated to the multiple drones according to the task allocation scheme corresponding to the target bidding matrix, including: determining the third target bidding matrix as the target bidding matrix, and allocating the multiple tasks to the multiple drones according to the task allocation scheme corresponding to the updated third target bidding matrix.

[0113] Based on the same inventive concept, another embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described in any of the above embodiments.

[0114] Among them, electronic equipment refers to Figure 5 , Figure 5 Schematic diagram of an electronic device provided by an embodiment of the present application. As shown in the figure, the electronic device 500 includes: a memory 510 and a processor 520, the memory 510 and the processor 520 are connected via a bus communication, the memory 510 stores a computer program, and the computer program can be run on the processor 520, thereby implementing the steps in the method for dynamic task allocation of drone clusters based on ant lion optimization disclosed in the above embodiment of the present application.

[0115] Based on the same inventive concept, another embodiment of the present application also provides a computer program product, including a computer program, which is executed by a processor as the method for dynamic task allocation of drone clusters based on antlion optimization as described in any of the above embodiments.

[0116] Based on the same inventive concept, another embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, wherein when the program is executed by a processor, the method for dynamic task allocation of drone clusters based on antlion optimization as described in any of the above embodiments is implemented.

[0117] As for the device, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0118] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0119] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, devices, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0123] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0124] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0125] The above is a detailed introduction to a method for dynamic task allocation of drone clusters based on ant lion optimization provided by the present application. In this article, specific examples are used to illustrate the principles and implementation methods of the present application. In order to make the description concise and clear, the drone information, task information, objective function and constraints in the actual application scenario are not described. However, as long as the method for dynamic task allocation of drone clusters based on ant lion optimization is adopted, it should be considered as the scope of the present application. In particular, the key point is the boundary shrinkage method based on piecewise logarithmic fitting in the ant lion optimization algorithm, the combination of the ant lion optimization algorithm and the auction strategy and the encoding method of individuals in the ant lion optimization algorithm, and the use of the ant lion optimization algorithm to update the auction price of each drone. As long as the method and the conclusion are used, the method for dynamic task allocation of drone clusters based on ant lion optimization should be considered as the scope of the present application. The description of the above embodiments is only used to help understand the method and its core idea of ​​the present application; at the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for dynamic task allocation of drone clusters based on antlion optimization, characterized in that: The method comprises: When the scene information changes, each drone in the drone cluster exchanges information with other drones, wherein the information includes the bidding price of each drone for multiple tasks and the position of each drone among the multiple drones; Generate a bidding matrix according to the bidding price of each drone among the multiple drones for the multiple tasks; For each of the drones, a plurality of individuals in the ant lion optimization algorithm based on piecewise logarithmic fitting are initialized respectively, and the position of each individual is used to represent the bidding price of the drone for the plurality of tasks; Each of the drones updates the bidding price in the bidding matrix by using the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a target bidding matrix; Allocating the multiple tasks to the multiple drones according to the task allocation scheme corresponding to the target bidding matrix, wherein the task is allocated to the drone with the highest bidding price; Among them, in the antlion optimization algorithm based on piecewise logarithmic fitting, the objective function in the antlion optimization algorithm based on piecewise logarithmic fitting is used to solve the individual with the best fitness value among the multiple individuals, which corresponds to solving the optimal task allocation plan generated by each drone for multiple tasks in the dynamic task allocation problem of the drone cluster.

2. The method for dynamic task allocation of drone clusters based on ant lion optimization according to claim 1 is characterized in that: Each of the drones updates the bidding price in the bidding matrix by using the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a target bidding matrix, including: Calculating the fitness value of each individual in the plurality of individuals, wherein the plurality of individuals include a plurality of ants and a plurality of ant lions; The individual with the best fitness value among the multiple ant lions is taken as an elite ant lion; Each of the plurality of ants selects an ant lion from the plurality of ant lions as a target ant lion by roulette, so that the ant randomly walks between an upper bound and a lower bound according to the selected target ant lion and the elite ant lion, and the upper bound and the lower bound correspond to the maximum competitive value and the minimum competitive value of the drone for the task, respectively; In the process of the ant's random walk, the upper bound and the lower bound are updated using a boundary shrinkage method based on logarithmic fitting; Calculate the fitness value of each ant based on the updated position of the ant; When there is an ant whose fitness value is better than the fitness value of the target ant lion selected by the ant, the position of the target ant lion is updated to the position of the ant that selected the target ant lion, and the ant lion with the best current fitness value is used as the elite ant lion; The above process is re-executed until the maximum number of iterations is reached, and the bidding matrix is ​​updated based on the bidding prices of multiple drones corresponding to multiple elite ant lions for multiple tasks to obtain a target bidding matrix.

3. The method for dynamic task allocation of drone clusters based on ant lion optimization according to claim 2 is characterized in that: For each individual in the plurality of individuals corresponding to each drone, the fitness value is used to evaluate the task allocation scheme corresponding to the intermediate bidding matrix, wherein the intermediate bidding matrix is ​​generated according to the bidding price of the drone corresponding to the current position of the individual during the random walk for the plurality of tasks and the bidding prices of other drones in the information for the plurality of tasks; The fitness value is determined based on the number of the drone cluster, the number of tasks, the decision variables of each drone's task assignment to each task, the relative distance between the task and the assigned drone, the speed of the assigned drone, and the time required for the assigned drone to perform the task; Among them, the decision variables for the task allocation of each drone to each task represent: whether the drone is allocated the task in the task allocation scheme corresponding to the intermediate bidding matrix; the allocated drone is the drone to which the task is allocated in the task allocation scheme corresponding to the intermediate bidding matrix.

4. The method for dynamic task allocation of drone clusters based on ant lion optimization according to claim 2 is characterized in that: Before calculating the fitness value of each ant, it also includes: Set a random number, and determine whether the position of the ant needs to be updated again according to the random number, the current number of iterations and the maximum number of iterations; When the position of the ant needs to be updated again, the position of the ant is updated according to the golden sine strategy, wherein the probability of updating the position of the ant again decreases as the current number of iterations increases.

5. The method for dynamic task allocation of drone clusters based on ant lion optimization according to claim 4 is characterized in that: The process of determining whether the position of the ant needs to be updated again is as follows: When the random number meets , it is determined that the position of the ant needs to be updated again; in, rand represents the random number, rand∈ (0,1); t represents the current iteration number, and T represents the maximum iteration number.

6. The method for dynamic task allocation of drone clusters based on ant lion optimization according to claim 4 is characterized in that: The process of updating the position of the ant according to the golden sine strategy is as follows: ； in, Indicates the number before the update i The position of the ants, Indicates the updated i The position of the ants; c represents the upper bound of the ant, d represents the lower bound of the ant, the i Dimension refers to the first dimension among multiple individuals. i Individual.

7. The method for dynamic task allocation of drone clusters based on ant lion optimization according to claim 6 is characterized in that: The updating of the upper bound and the lower bound by using a boundary shrinkage method based on logarithmic fitting includes: Setting a proportionality coefficient, wherein the proportionality coefficient is obtained according to a logarithmic fitting function, and the value of the proportionality coefficient increases with the increase of the number of iterations; Based on the proportional coefficient, the upper bound of the ants, and the lower bound of the ants, an upper bound and a lower bound in each round of iteration are determined.

8. The method for dynamic task allocation of drone clusters based on ant lion optimization according to claim 7 is characterized in that: The calculation formula for updating the upper bound and the lower bound is: ； in, represents the proportionality coefficient, represents the upper bound during the t-th iteration, It represents the lower bound in the tth iteration process.

9. The method for dynamic task allocation of drone clusters based on ant lion optimization according to claim 7 is characterized in that: The calculation formula of the proportionality coefficient is: ； in, represents the proportionality coefficient, m represents the first coefficient in the logarithmic fitting function, b n represents the second coefficient in the logarithmic fitting function; t represents the current number of iterations, and T represents the maximum number of iterations; m ∈{1, 2, 3, 4, 5}, n ∈{1, 2, 3, 4, 5}.

10. The method for dynamic task allocation of drone swarm based on ant lion optimization according to any one of claims 1 to 9, characterized in that: Each of the drones updates the bidding price in the bidding matrix by using the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a target bidding matrix, including: Each of the drones updates the bidding price in the bidding matrix by using the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a first target bidding matrix; Each of the drones respectively updates the bidding price in the first target bidding matrix by using the antlion optimization algorithm based on piecewise logarithmic fitting to obtain a second target bidding matrix; Determine whether the task allocation scheme corresponding to the second target bidding matrix is ​​the same as the task allocation scheme corresponding to the first target bidding matrix; When the task allocation scheme corresponding to the second target bidding matrix is ​​the same as the task allocation scheme corresponding to the first target bidding matrix, determining the second target bidding matrix as the target bidding matrix; When the task allocation scheme corresponding to the second target bidding matrix is ​​different from the task allocation scheme corresponding to the first target bidding matrix, each drone re-updates the bidding prices of multiple drones for multiple tasks in the second target bidding matrix through the ant lion optimization algorithm based on piecewise logarithmic fitting, and obtains a third target bidding matrix; When the task allocation scheme corresponding to the third target bidding matrix is ​​the same as the task allocation scheme corresponding to the obtained second target bidding matrix, allocating the multiple tasks to the multiple drones according to the task allocation scheme corresponding to the target bidding matrix includes: determining the third target bidding matrix as the target bidding matrix, and allocating the multiple tasks to the multiple drones according to the task allocation scheme corresponding to the updated third target bidding matrix.

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