An NHHO algorithm and system for online task allocation in heterogeneous carrier clusters

Abstract

This invention belongs to the field of task allocation technology and discloses an NHHO algorithm and system for online task allocation in heterogeneous carrier clusters. First, the HHO algorithm is improved by employing the Contract Network Protocol, a technique used in online task allocation methods, to improve the Harris Hawk algorithm, preventing the algorithm from prematurely reaching a local optimum and thereby increasing convergence. The spiral search method of the Whale Algorithm is also introduced to enhance the algorithm's performance in the early stages of the search. The improved algorithm is then run under different initial conditions defined in Chapter 3, and the results are analyzed in depth. The results of this analysis clearly demonstrate the feasibility and effectiveness of the improved algorithm.

Description

Technical Field

[0001] The present invention belongs to the technical field of task allocation, and in particular relates to an NHHO algorithm and system for online task allocation of heterogeneous carrier clusters. Background Art

[0002] Heterogeneous carrier cluster task allocation involves assigning appropriate operational tasks to each carrier based on the number and type of heterogeneous carriers within the cluster, their designated objectives, and the different payloads they carry. The key to heterogeneous carrier cluster task allocation is to formulate a scientific action plan for each unmanned carrier based on the actual conditions of the mission scope environment, taking into account factors such as the unmanned carrier's performance, fuel consumption, mission priority, and target threats. Furthermore, to ensure optimal performance of the heterogeneous carrier cluster within the mission scope environment, task allocation requires the rational allocation of payloads and deployment timing.

[0003] In the context of heterogeneous carrier cluster task allocation, existing technologies mainly face the following problems and defects:

[0004] 1) The HHO algorithm is prone to falling into local optimal solutions:

[0005] Problem: The Harris Hawks Optimization (HHO) algorithm is prone to falling into local optimal solutions during task allocation, preventing it from finding a global optimal solution. This problem is particularly prominent in complex task allocation environments, impacting the overall performance of heterogeneous carrier clusters.

[0006] Impact: This leads to inefficient task allocation, insufficient resource utilization, and inability to fully leverage the advantages of heterogeneous carrier clusters.

[0007] 2) Poor search performance in the early stages of the search:

[0008] Problem: The HHO algorithm exhibits poor search performance in the early stages of the search, making it difficult to quickly find high-quality solutions. This results in low efficiency and effectiveness in task allocation at the beginning of the task, affecting the overall progress of the task.

[0009] Impact: This resulted in poor initial task allocation results, requiring multiple adjustments and optimizations, which prolonged task planning time.

[0010] 3) The fitness function convergence effect under the algorithm program is not obvious:

[0011] Problem: Compared to genetic algorithms and particle swarm optimization, the HHO algorithm's fitness function doesn't converge significantly during runtime. This means the algorithm struggles to significantly improve the quality of its solution during iteration, impacting the superiority of the final task allocation solution.

[0012] Impact: The fitness function converges slowly, which results in a long optimization process, increased computational costs, and unsatisfactory optimization results.

[0013] 4) Failure to fully consider the dynamic changes in the task scope environment:

[0014] Problem: Existing technologies often fail to fully consider the dynamic changes in the task scope and environment during task allocation, and lack the ability to adapt to real-time changes. This poses challenges to task execution in complex environments for heterogeneous carrier clusters.

[0015] Impact: The task allocation plan cannot adapt to environmental changes in a timely manner, affecting the flexibility and effectiveness of task execution.

[0016] 5) Insufficient consideration of mission priorities and target threats:

[0017] Problem: Existing technologies often fail to take into account task priorities and target threats when allocating tasks, resulting in incomplete optimization of task allocation plans.

[0018] Impact: High-priority tasks and high-threat targets will not receive sufficient attention, affecting the overall safety and effectiveness of mission execution.

[0019] 6) Difficulty in rationally formulating load configuration and usage timing:

[0020] Problem: During task allocation, it is difficult to rationally determine payload configuration and deployment timing. Existing technologies have limited support and optimization capabilities in this area.

[0021] Impact: Irrational use of payloads leads to resource waste or mission failure, affecting the overall performance of the heterogeneous carrier cluster.

[0022] In the field of task allocation for heterogeneous carrier clusters, existing technologies face major challenges, including the HHO algorithm's tendency to fall into local optima, poor initial search performance, weak fitness function convergence, difficulty coping with dynamic environmental changes, insufficient consideration of task priorities and target threats, and difficulty determining payload configuration and deployment timing. These issues and shortcomings limit the overall performance of heterogeneous carrier clusters, necessitating urgent improvements and enhancements in algorithm optimization and system design. Summary of the Invention

[0023] In view of the problems existing in the prior art, the present invention provides an NHHO algorithm for online task allocation in heterogeneous carrier clusters.

[0024] The present invention is implemented as follows: an NHHO algorithm for online task allocation in heterogeneous carrier clusters includes:

[0025] Step 1: Input the heterogeneous carrier cluster and the relevant data of the three types of targets, and determine the initialization parameters, including population size, maximum number of iterations, switching probability, jump intensity, etc.

[0026] Step 2: Initialize the population according to the adversarial environment;

[0027] Step 3: Apply the Harris Hawk algorithm to introduce a spiral update mechanism to calculate the standard function value of each heterogeneous carrier cluster position, determine the individual fitness value, and obtain the current optimal function value and corresponding position;

[0028] Step 4: Update the specific location of the three types of targets and the enemy type 1 target;

[0029] Step 5: Dynamically allocate tasks based on heterogeneous carrier clusters and Class 1 target discovery.

[0030] Step 6: Determine whether the effective distance reaches the standard. If not, return to step 3.

[0031] Step 7: Attack Category 3 targets and Category 1 targets;

[0032] Step 8: If the maximum number of iterations is not reached or the target number of completions is insufficient, return to step 3; otherwise, proceed to the next step.

[0033] Step 9: Output the fitness and required time of the task assignment.

[0034] Another object of the present invention is to provide a heterogeneous carrier cluster online task allocation system comprising:

[0035] The initialization module is used to input the relevant data of the heterogeneous carrier cluster and three types of targets, determine the initialization parameters to be set, including population size, maximum number of iterations, switching probability, jump intensity, etc.; and initialize the population according to the adversarial environment;

[0036] The calculation module is used to apply the Harris Hawk algorithm to introduce a spiral update mechanism to calculate the standard function value of each heterogeneous carrier cluster position, determine the individual fitness value, obtain the current optimal function value and corresponding position; and update the specific position of the three types of targets and the enemy type 1 target;

[0037] Allocation module, used for dynamic task allocation based on heterogeneous carrier clusters and Class 1 target discovery;

[0038] The judgment module is used to judge whether the action distance reaches the standard.

[0039] The output module is used to output the fitness and required time of task allocation.

[0040] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the heterogeneous carrier cluster online task allocation NHHO algorithm.

[0041] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to execute the steps of the heterogeneous carrier cluster online task allocation NHHO algorithm.

[0042] Another object of the present invention is to provide an information data processing terminal, which is used to implement the heterogeneous carrier cluster online task allocation system.

[0043] In combination with the above technical solutions and the technical problems solved, please analyze the advantages and positive effects of the technical solutions to be protected by the present invention from the following aspects:

[0044] First, this paper improves the HHO algorithm by employing the contract network protocol from online task allocation methods to improve the Harris Hawk algorithm. This prevents the algorithm from prematurely reaching a local optimum, thereby improving convergence. Furthermore, the spiral search method of the whale algorithm is introduced to enhance the algorithm's performance in the early stages of the search. The improved algorithm is then run against the different initial conditions defined in Chapter 3, and the results are analyzed in depth. This analysis clearly demonstrates the feasibility and effectiveness of the improved algorithm.

[0045] Second, the technical solution of the present invention solves a technical problem that people have long been eager to solve but have never been able to successfully solve:

[0046] Experimental results indicate that the NHHO algorithm and system for online task allocation in heterogeneous carrier clusters proposed in this paper are suitable for this application. The NHHO algorithm significantly improves task allocation efficiency and effectively guides the population toward a global optimal solution, thereby enhancing the algorithm's global search capabilities. Furthermore, the improvements employed enhance the algorithm's optimization performance in the initial stages and reduce the likelihood of falling into local optimal solutions, demonstrating that the improved algorithm offers substantial improvements over traditional algorithms. Therefore, this invention addresses the problem of online task allocation in heterogeneous carrier clusters to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of the NHHO algorithm for online task allocation in heterogeneous carrier clusters provided by an embodiment of the present invention.

[0048] Figure 2This is a structural block diagram of the online task allocation system for heterogeneous carrier clusters provided by an embodiment of the present invention.

[0049] Figure 3 This is a structural diagram of the Harris Hawk algorithm provided by an embodiment of the present invention.

[0050] Figure 4 This is a comparison chart of different algorithms provided by an embodiment of the present invention, with the number of carriers and targets remaining unchanged, the population size being 20, and 200 iterations.

[0051] Figure 5 This is a comparison chart of different algorithms provided by an embodiment of the present invention with the number of carriers and targets unchanged, a population size of 20, and 500 iterations.

[0052] Figure 6 This is a comparison chart of different algorithms provided by an embodiment of the present invention with the number of carriers and targets remaining unchanged, the population size being 20, and 1000 iterations.

[0053] Figure 7 This is a comparison chart of different algorithms provided by an embodiment of the present invention, with the number of carriers and targets remaining unchanged, the population size being 20, and 200 iterations.

[0054] Figure 8 This is a comparison chart of different algorithms provided by an embodiment of the present invention with the number of carriers and targets unchanged, a population size of 20, and 500 iterations.

[0055] Figure 9 This is a comparison chart of different algorithms provided by an embodiment of the present invention with the number of carriers and targets remaining unchanged, the population size being 20, and 1000 iterations. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] like Figure 1 As shown, an NHHO algorithm for online task allocation of heterogeneous carrier clusters provided by an embodiment of the present invention includes the following steps:

[0058] S101: Input the heterogeneous carrier cluster and the relevant data of the three types of targets, and determine the initialization parameters, including population size, maximum number of iterations, switching probability, jump intensity, etc.

[0059] S102, initializing the population according to the adversarial environment;

[0060] S103, applying the Harris Hawk algorithm to introduce a spiral update mechanism to calculate the standard function value of each heterogeneous carrier cluster position, determine the fitness value of the individual, and obtain the current optimal function value and corresponding position;

[0061] S104, updating the specific positions of the three types of targets and the enemy type 1 target;

[0062] S105, dynamically allocating tasks based on heterogeneous carrier clusters and Class 1 target discovery conditions;

[0063] S106, determine whether the action distance reaches the standard, if not, return to S103;

[0064] S107, attacking Category 3 targets and Category 1 targets;

[0065] S108, if the maximum number of iterations is not reached or the target number of completions is not enough, return to S103, otherwise, continue to the next step;

[0066] S109: Output the adaptability and required time of task allocation.

[0067] This paper proposes an NHHO algorithm for online task allocation in heterogeneous carrier clusters. By combining the Harris Hawk algorithm with a spiral update mechanism, it optimizes task allocation in heterogeneous carrier clusters and improves system efficiency and adaptability. The specific working principle is as follows:

[0068] Step 1: Input parameter initialization

[0069] Input data of heterogeneous carrier clusters, including relevant data of three types of targets (friendly forces, enemy forces, resource points, etc.).

[0070] Determine initialization parameters such as population size, maximum number of iterations, switching probability, jump strength, etc.

[0071] The relevant parameters are input by the user or system preset to initialize the basic configurations of the algorithm and provide a basis for subsequent calculations.

[0072] Step 2: Population initialization

[0073] Initialize the population according to the adversarial environment.

[0074] Generate an initial population containing a variety of heterogeneous carrier positions and states.

[0075] In the initial population, each individual represents a task allocation scheme.

[0076] Step 3: Calculate the fitness value

[0077] Harris Hawk algorithm is applied to introduce spiral update mechanism to calculate the standard function value of each heterogeneous carrier cluster position and determine the fitness value of the individual.

[0078] The hunting behavior of Harris's hawk is simulated through a spiral updating mechanism, and the position of each individual and the corresponding target function value are calculated.

[0079] Determine the fitness value of each individual and obtain the current optimal fitness value and corresponding position.

[0080] Step 4: Update the target location

[0081] Updated the specific locations of Category 3 targets and Category 1 enemy targets.

[0082] Based on the current environment and enemy and friendly situation, the target position is dynamically adjusted to reflect the actual battlefield situation.

[0083] Step 5: Dynamic Task Allocation

[0084] Dynamic task allocation is performed based on the discovery of heterogeneous carrier clusters and Class 1 targets.

[0085] Tasks are dynamically allocated based on the location and status of the carrier and target to ensure optimal resource utilization and efficient task completion.

[0086] Step 6: Determine the range

[0087] Determine whether the action distance meets the standard. If not, return to step 3 and continue iteration.

[0088] Based on the calculated distance between the carrier and the target, it is determined whether the standard is met and whether the task allocation needs to be further optimized.

[0089] Step 7: Targeted Attack

[0090] Attack Category 3 targets and Category 1 targets.

[0091] Once the mission assignment is determined and the range standard is reached, the carrier attacks the target and performs the mission.

[0092] Step 8: Iterative judgment

[0093] Determine whether the maximum number of iterations or the target number of completions has been reached. If not, return to step 3 to continue iterating.

[0094] Determine whether the current number of iterations has reached the preset maximum value, or whether the target completion number has reached the expected value. If not, continue iterative optimization.

[0095] Step 9: Output the results

[0096] Output the fitness value and required time of the task assignment.

[0097] Calculate and output the fitness value and required time of the final task allocation plan as the evaluation index of task completion.

[0098] Specific operation examples:

[0099] Suppose there is a heterogeneous carrier swarm including drones, ground robots and maritime patrol boats, targeting enemy vehicles, enemy soldiers and resource points.

[0100] 1. Input parameter initialization:

[0101] Enter the carrier and target data.

[0102] Set the population size to 50, the maximum number of iterations to 1000, the switching probability to 0.3, and the jump intensity to 1.5.

[0103] 2. Population initialization:

[0104] Generate an initial population containing 50 task allocation schemes.

[0105] 3. Calculate the fitness value:

[0106] The spiral update mechanism is used to simulate the hunting behavior of Harris's hawk and calculate the fitness value.

[0107] 4. Update target location:

[0108] Dynamically adjust target positions to reflect real-time battlefield conditions.

[0109] 5. Dynamic task allocation:

[0110] Dynamically assign tasks based on the discovery of carriers and targets.

[0111] 6. Determine the effective distance:

[0112] Check whether the distance between the carrier and the target meets the standard.

[0113] 7. Target Attack:

[0114] Attack the target of the determined mission.

[0115] 8. Iterative judgment:

[0116] Determine whether the maximum number of iterations or the target number of completions has been reached. If not, continue iterating.

[0117] 9. Output results:

[0118] Output the final task allocation fitness value and required time.

[0119] By combining the Harris Hawk algorithm with a spiral update mechanism, this algorithm achieves efficient optimization of task allocation for heterogeneous carrier clusters, enhancing the system's flexibility and adaptability. By dynamically adjusting task allocation and updating target locations in real time, it effectively improves the efficiency and accuracy of task completion.

[0120] like Figure 2 As shown, an embodiment of the present invention provides a heterogeneous carrier cluster online task allocation system including:

[0121] The initialization module is used to input the relevant data of the heterogeneous carrier cluster and three types of targets, determine the initialization parameters to be set, including population size, maximum number of iterations, switching probability, jump intensity, etc.; and initialize the population according to the adversarial environment;

[0122] The calculation module is used to apply the Harris Hawk algorithm to introduce a spiral update mechanism to calculate the standard function value of each heterogeneous carrier cluster position, determine the individual fitness value, obtain the current optimal function value and corresponding position; and update the specific position of the three types of targets and the enemy type 1 target;

[0123] Allocation module, used for dynamic task allocation based on heterogeneous carrier clusters and Class 1 target discovery;

[0124] The judgment module is used to judge whether the action distance reaches the standard.

[0125] The output module is used to output the fitness and required time of task allocation.

[0126] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the heterogeneous carrier cluster online task allocation NHHO algorithm.

[0127] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to execute the steps of the heterogeneous carrier cluster online task allocation NHHO algorithm.

[0128] Another object of the present invention is to provide an information data processing terminal, which is used to implement the heterogeneous carrier cluster online task allocation system.

[0129] The present invention is specifically implemented:

[0130] 1. The key to tasking heterogeneous vehicle clusters is to develop a scientific action plan for each unmanned vehicle based on the actual mission environment, taking into account factors such as vehicle performance, fuel consumption, mission priority, and target threats. Furthermore, to ensure optimal performance of the heterogeneous vehicle cluster within the mission environment, task assignment requires the rational allocation of payloads and deployment timing.

[0131] 2 Modeling of Task Allocation Problem in Heterogeneous Carrier Clusters

[0132] Assume that our side is performing a mission with a heterogeneous carrier cluster consisting of K unmanned carriers, and deploys N Class 1 targets, M Class 2 targets, and A Class 3 targets within the mission scope. Based on factors such as the target type, threat level, and execution difficulty, the threat level of the target can be determined, and this level is usually quantified by the target threat coefficient.

[0133] §2.2.1 Model parameter design

[0134] Air carrier U={U1,U2,...,Un}(attribute quadruple<UA,PosA,SDA,ZCJLA> , respectively represent the air carrier number, air carrier position, speed, and detection distance);

[0135] Surface carrier U={U1,U2,...,Un}(attribute quadruple<UB,PosB,SDB,GJJLB> , respectively represent the surface carrier number, surface carrier position, speed, and range);

[0136] 3 types of targets U={U1,U2,...,Un}(attribute quadruple<UC,PosC,JZC,WXCDC> , respectively representing the three types of target numbers, three types of target locations, mission value, and threat level);

[0137] 1 type of target U={U1,U2,...,Un}(attribute quadruple<UD,PosD,JZD,WXCDD> , respectively represent the number of category 1 target, the location of category 1 target, the mission value, and the threat level);

[0138] All targets are targets to be attacked, and the locations of Category 3 targets are known, but the locations of Category 1 targets are unknown. Category 2 targets are similar to Category 1 targets, so they are not set separately. Only the mission value and threat level are changed in the parameter settings of Category 1 targets.

[0139] §2.2.2 Task Costs and Benefits

[0140] When assigning missions to airborne and surface carriers, the goal is to improve mission efficiency and minimize losses. Therefore, this paper uses the benefits of unmanned carriers performing missions, the mission risks they assume, and the flight length as criteria for measuring mission effectiveness. Heterogeneous carrier clusters require a trade-off between costs and benefits. The costs borne by unmanned carriers include the costs of potential threats to the mission carriers and the additional flight distance and time consumed during mission execution.

[0141] The target threat cost is calculated as follows: When unmanned vehicle i is performing a mission, the probability of target j damaging it is WXCDj. Therefore, the survival probability of vehicle i can be expressed as Pij = 1-WXCDj (Pij>0 is required for the mission to continue). After performing n consecutive missions (here n≥2), the cumulative threat cost T1 posed by the target to the vehicle can be expressed as:

[0142]

[0143] Voyage cost: Voyage cost reflects the various resources consumed by an unmanned vehicle during the execution of a designated mission, including but not limited to energy, time, supplies, and other related expenses. To more accurately assess this cost, this paper uses the cumulative time required for the unmanned vehicle to complete a designated mission as an indicator of the total voyage cost. The voyage cost, denoted by T2, refers to this total time cost. Voyage cost T2 is expressed as:

[0144]

[0145] In the formula, TMAX represents the maximum time allowed for a single vehicle to complete a mission, while TMIN represents the minimum time required for a single vehicle to complete a mission. The requirement in this paper is to ensure that all unmanned vehicles can reach the mission site and complete the assigned mission, that is, the time required for each unmanned vehicle to complete the mission should be between TMAX and TMIN.

[0146] Reward parameter: refers to the setting of the heterogeneous carrier cluster based on the mission value of the target and the required navigation time after hitting the target, which is the direction for the algorithm iteration. The reward parameter T3 is expressed as:

[0147]

[0148] Where TMAX represents the maximum time allowed for a single carrier to perform any task; the smaller the reward parameter, the better the completion of this model.

[0149] Flight time cost: To prevent heterogeneous carrier clusters from falling into local optimality and consuming too much overall time, a flight time cost T4 is set. T4 is expressed as:

[0150]

[0151] Where TZ is the total time to complete all tasks, and T' is a constant.

[0152] §2.2.3 Task execution constraints

[0153] To further improve the authenticity and complexity of the model, the task execution conditions are constrained:

[0154] All airborne carriers can attack Category 1 and Category 2 targets without suffering any damage, but they cannot attack Category 3 targets:

[0155]

[0156] JZA is the attack performance of the airborne carrier. The paper assumes that all airborne carriers have the same performance.

[0157] Depending on the scope of the mission, all unmanned vehicles are at different altitudes and their positions can overlap:

[0158]

[0159] WZ is the overall position of the mission range, and the position of the unmanned carrier in this article is within the entire mission range.

[0160] The location of Category 3 targets can be detected in the early stages using satellites and other means, but the location of Category 1 and Category 2 targets cannot be detected:

[0161]

[0162] WZC is a known parameter, and WZD is represented by a random number.

[0163] Logical constraints require that before attacking three types of targets, the heterogeneous carrier cluster must first complete the reconnaissance mission and then execute the strike mission:

[0164] T B iC ≥T A iC (2.10)

[0165] TB is the time it takes for a surface carrier to attack a target of type i, and TA is the time it takes for an air carrier to attack a target of type i.

[0166] §2.2.4 Fitness Function

[0167] The optimization objective function for task allocation in heterogeneous carrier clusters is influenced by multiple factors, including target threat cost, range cost, reward parameter, and flight time cost. A weighted approach is used to balance these four costs and benefits. The importance of each influencing factor is reflected by the size of the corresponding weighting factor, where W1 = 0.2, W2 = 0.3, W3 = 0.2, and W4 = 0.3.

[0168] W1+W2+W3+W4=1(2.11)

[0169] The overall cost-benefit in this article is:

[0170]

[0171] Where Z is the overall cost benefit, and n is the integration of all unmanned heterogeneous carrier clusters.

[0172] §2.3 Conclusion

[0173] This paper first explains four types of heterogeneous carrier cluster task execution applications based on the task scope and environmental background assumptions; then, it gives a heterogeneous carrier cluster task allocation model, including model parameters, task costs and benefits, task execution constraints, fitness function, etc.; finally, based on unmanned carrier parameters and task execution methods, it constructs a heterogeneous carrier cluster task allocation model, laying the foundation for the development of heterogeneous carrier cluster task allocation methods.

[0174] 3. Heterogeneous carrier cluster task allocation method based on HHO algorithm

[0175] The task allocation problem is a majority decision problem. Common solutions include centralized and distributed solutions. In view of the complex coupling constraints and uncertainty characteristics of the task allocation problem of heterogeneous carrier clusters in adversarial environments, this paper proposes a task allocation method for heterogeneous carrier clusters based on the HHO algorithm. The effectiveness of this method is verified through simulation analysis.

[0176] like Figure 3 As shown in §3.1 Harris Hawk Algorithm

[0177] §3.1.1 Algorithm Rules

[0178] Before each iteration, it is necessary to check whether the hawk group has crossed the boundary and make necessary adjustments. At the same time, the position and fitness value of the prey are updated.

[0179] During the search phase, hawks use two different search methods, depending on whether they have already spotted their prey.

[0180] As the siege continues, the prey's ability to escape will gradually decrease.

[0181] There is a threshold of prey energy, and once this threshold is exceeded, Harris's hawk will move from the search phase to the swarming phase.

[0182] Before each raid, the prey escaped the encirclement.

[0183] Harris's hawks use four different hunting strategies based on their assessment of the physical condition of the prey trapped in the siege and its escape potential.

[0184] Each round of prey will eventually be captured, so each iteration will produce a new prey, and this position is occupied by the prey with the best fitness in the new generation population.

[0185] §3.1.2 Algorithm Characteristics

[0186] The Harris Hawk algorithm is a computational method developed for the Harris Hawk's encirclement of prey. It is suitable for this paper in the following aspects:

[0187] Our unmanned vehicles are regarded as Harris Hawk population A and Harris Hawk population B respectively, and the three types of targets are regarded as prey.

[0188] Category 3 targets do not have fixed locations, but adjust their directions to more suitable locations based on the situation.

[0189] Each unmanned carrier in the heterogeneous carrier cluster will be assigned tasks according to the situation, and the task assignment will also be changed according to changes in the mission objectives.

[0190] §3.2 Task Allocation Process for Heterogeneous Carrier Clusters

[0191] The specific steps of the algorithm are:

[0192] Step 1: Input the heterogeneous carrier cluster and the relevant data of the three types of targets, and determine the initialization parameters, including population size, maximum number of iterations, switching probability, jump intensity, etc.

[0193] Step 2: Initialize the population according to the adversarial environment;

[0194] Step 3: Apply the Harris Eagle algorithm to calculate the standard function value of each heterogeneous carrier cluster position, determine the fitness value of the individual, and obtain the current optimal function value and corresponding position;

[0195] Step 4: Update the specific location of the three types of targets and the enemy type 1 target;

[0196] Step 5: Determine whether the effective distance reaches the standard, if not, return to step 3;

[0197] Step 6: Attack Category 3 targets and Category 1 targets;

[0198] Step 8: If the maximum number of iterations has not been reached or the task has not been completed, return to step 3; otherwise, proceed to the next step;

[0199] Step 9: Output the task allocation fitness and required time.

[0200] §3.3 Simulation Analysis

[0201] To evaluate the performance of the improved algorithms, simulation experiments were conducted using Harris Hawk Optimization (HHO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Given the randomness of the algorithms, multiple simulations were performed for each algorithm, and the average of the 20 best fitness values ​​was selected as a comparison benchmark.

[0202] §3.3.1 Simulation environment settings

[0203] During one mission, we discovered Category 3 and Category 1 targets. To address this threat, we dispatched six airborne and six surface carriers. The targets included five Category 3 targets, ten Category 1 targets, and five Category 2 targets. The locations of Category 3 targets were roughly known, being stationary or slightly moving. The locations of other targets were unknown and required confirmation by the airborne carriers. The surface carriers were responsible for destroying Category 3 targets based on the target information provided by the airborne carriers, but could not directly strike Category 1 targets. If the surface carriers triggered a Category 1 target, they would eliminate it but suffer damage themselves. Due to payload limitations, the airborne carriers could not effectively strike Category 3 targets, but they could effectively attack Category 1 and Category 2 targets. See Table 3.1 for detailed target parameters.

[0204] Table 3.1 Target parameters

[0205] Target Number Location (km) Mission Value Threat level 1 (46,108) 0.6 0.6 2 (59,38) 0.8 0.8 3 (101,66) 0.7 0.7 4 (2,18) 0.8 0.8 5 (151,34) 0.7 0.7 6 (rand(200),rand(200)) 0.3 0.3 7 (rand(200),rand(200)) 0.3 0.3 8 (rand(200),rand(200)) 0.3 0.3 9 (rand(200),rand(200)) 0.3 0.3 10 (rand(200),rand(200)) 0.3 0.3 11 (rand(200),rand(200)) 0.3 0.3 12 (rand(200),rand(200)) 0.3 0.3 13 (rand(200),rand(200)) 0.3 0.3 14 (rand(200),rand(200)) 0.3 0.3 15 (rand(200),rand(200)) 0.3 0.3 16 (rand(200),rand(200)) 0.4 0.4 17 (rand(200),rand(200)) 0.4 0.4 18 (rand(200),rand(200)) 0.4 0.4 19 (rand(200),rand(200)) 0.4 0.4 20 (rand(200),rand(200)) 0.4 0.4

[0206] In the above table, 1-5 represent Category 3 targets, 6-15 represent Category 1 targets, and 16-20 represent Category 2 targets.

[0207] The attribute parameters of aerial carriers and surface carriers are shown in Tables 3.2 and 3.3 below.

[0208] Table 3.2 Airborne carrier parameters

[0209]

[0210] Table 3.3 Surface carrier parameters

[0211]

[0212]

[0213] In Tables 3.2 and 3.3 above, in order to prevent the position of Category 1 target from overlapping with the position of the surface carrier and directly damaging the surface carrier, the positions of the aerial carrier and the surface carrier are made to overlap, and the aerial carrier is released first to operate on the Category 1 target.

[0214] §3.3.2 Simulation Analysis

[0215] The above model was simulated using genetic algorithm (GA), particle swarm algorithm (PSO) and Harris Hawk (HHO) respectively. The results were compared with the population size of 20 and the iterations of 200, 500 and 1000 respectively. Figure 4 、 Figure 5 and Figure 6 As shown:

[0216] The simulation results show that the HHO algorithm has better fitness than both the GA and PSO algorithms in each case after running the program 200, 500, and 1000 times. However, the chart clearly shows that the performance difference between the HHO and GA algorithms decreases with increasing iterations. This indicates that the HHO algorithm has encountered a local optimum, known as "premature maturity."

[0217] This paper first explains the principle and process of the HHO algorithm; secondly, it performs simulation analysis on the HHO algorithm with different initial conditions; finally, it analyzes and explains the program running results, thereby demonstrating the feasibility of the HHO algorithm in solving the task allocation problem of heterogeneous carrier clusters.

[0218] 4. Online task allocation method for heterogeneous carrier clusters based on NHHO algorithm

[0219] The HHO algorithm offers two key advantages over other classical algorithms for solving online task allocation problems in heterogeneous carrier clusters: 1. Its performance advantage and 2. its adaptability to task allocation problems. However, the HHO algorithm is prone to falling into local optima and suffers from poor search performance in the early stages of the search. Furthermore, the algorithm's fitness function converges less effectively than that of genetic algorithms and particle swarm optimization algorithms. Therefore, this paper proposes to improve the HHO algorithm and investigate task allocation methods based on the NHHO algorithm.

[0220] §4.1 Basis for Improvement

[0221] §4.1.1 Update mechanism based on the contract network protocol algorithm

[0222] Considering the real-time changes in task scope, this paper uses the Contract Net Protocol (CNP) theory to perform online task allocation for heterogeneous carrier clusters. This allocation method redistributes tasks based on real-time status feedback. There are three main scenarios:

[0223] (1) When a new task (discovering a Class 1 target or a Class 2 target) appears during the execution of the task, online task allocation is used to redistribute tasks among heterogeneous carrier clusters.

[0224] (2) When an unmanned carrier is unable to continue its mission due to limited capabilities or destruction, other unmanned carriers of the same type will take over and reallocate the mission.

[0225] (3) If the assigned task moves or changes (the task has been completed), the heterogeneous carrier cluster will reallocate the task.

[0226] This paper adopts the improved contract network protocol algorithm to construct a centralized heterogeneous carrier cluster task allocation model. Taking into account the actual task scope, the heterogeneous carrier cluster is used as the tenderer, bidder and executor of the task.

[0227] After introducing the contract network protocol algorithm method, we calculate whether the task requirements are met. The judgment function formula is as follows:

[0228]

[0229] In the formula, cand(i, j) represents whether the airborne vehicle can reach the target, POSA-POSC represents the range required to reach a Class 1 target, and POSA-POSD represents the range required to reach a Class 3 target. Therefore, if the range required to reach a Class 1 target is less than the range required to reach a Class 3 target, the result returns 1; otherwise, it returns 0. Therefore, as long as an airborne vehicle meets the range requirement, it can bid for the target mission.

[0230] Then, through the contract network algorithm, the bidder with the shortest time to reach the target is selected to perform the task.

[0231] §4.1.2 Spiral Update Mechanism Combining the Whale Algorithm

[0232] To improve the HHO algorithm's population search and enhance its performance in the early stages, the spiral search method of the Whale Optimization Algorithm (WOA) was incorporated into the HHO algorithm. The Whale Optimization Algorithm simulates the hunting behavior of humpback whales, which surround and attack their prey using a web of bubbles along a spiral line. The algorithm consists of three phases: encircling the prey, attacking with a bubble net, and searching for the prey. The bubble net attack phase uses a whale-like hunting method, where a bubble net is expelled along a spiral line and gradually approaches the prey. To simulate this process, the WOA algorithm constructs a spiral equation of motion. This spiral update mechanism contributes to the WOA algorithm's rapid convergence.

[0233] This paper introduces the spiral update mechanism of the WOA algorithm into the HHO algorithm, improving its convergence performance. Furthermore, with the introduction of the WOA algorithm's spiral mechanism, the HHO algorithm can use the position update in the spiral mechanism to determine the distance between the unmanned vehicle and the target when new Class 1 and Class 2 targets are discovered. Using the position update formula, the HHO algorithm performs fitness analysis and determines the optimal fitness value.

[0234] The position update formula of the HHO algorithm after introducing the spiral mechanism of the WOA algorithm is as follows:

[0235]

[0236] Where b is the spiral constant and L is a random number between [0,1].

[0237] §4.2 Task allocation process based on improved Harris Hawk algorithm

[0238] The specific steps of the algorithm are:

[0239] Step 1: Input the heterogeneous carrier cluster and the relevant data of the three types of targets, and determine the initialization parameters, including population size, maximum number of iterations, switching probability, jump intensity, etc.

[0240] Step 2: Initialize the population according to the adversarial environment;

[0241] Step 3: Apply the Harris Hawk algorithm to introduce a spiral update mechanism to calculate the standard function value of each heterogeneous carrier cluster position, determine the individual fitness value, and obtain the current optimal function value and corresponding position;

[0242] Step 4: Update the specific location of the three types of targets and the enemy type 1 target;

[0243] Step 5: Dynamically allocate tasks based on heterogeneous carrier clusters and Class 1 target discovery;

[0244] Step 6: Determine whether the effective distance reaches the standard, if not, return to step 3;

[0245] Step 7: Attack Category 3 targets and Category 1 targets;

[0246] Step 8: If the maximum number of iterations is not reached or the target number of completions is insufficient, return to step 3; otherwise, proceed to the next step.

[0247] Step 9: Output the fitness and required time of the task allocation.

[0248] Simulation analysis: Setting the target and unmanned vehicle parameters the same as in Section 3.3.1, and setting the number of iterations (evolutions) to the initial population size the same as in Chapter 3, the convergence effect of the improved optimization algorithm is compared with that before the improvement as follows:

[0249] The basic parameters are shown in Table 3.1, Table 3.2 and Table 3.3. The population size is 20. The New Harris Hawks Optimization (NHHO) algorithm is used for 200, 500 and 1000 iterations respectively. The results are compared with HHO, GA and PSO. Figure 7 , Figure 8 and Figure 9 As shown:

[0250] pass Figure 7 、 Figure 8 and Figure 9 It can be seen that compared with HHO, PSO and GA, the fitness function convergence speed of NHHO algorithm is significantly faster than that of HHO, PSO and GA algorithms, and the optimal fitness also decreases rapidly with the increase of the number of iterations.

[0251] To further illustrate that the NHHO algorithm is better than HHO, PSO, and GA in task allocation in an adversarial environment, the following will change the algorithm population number and number of iterations, the running time, and the optimal function value. The basic parameters are the optimal fitness and running time of the different algorithms in Table 3.1, Table 3.2, and Table 3.3. Each algorithm is run 20 times, and the average value is shown in Table 4.1, Table 4.2, Table 4.3, Table 4.4, and Table 4.5:

[0252] Table 4.1 Comparison of experimental data

[0253]

[0254] Table 4.2 Comparison of experimental data

[0255]

[0256] Table 4.3 Comparison of experimental data

[0257]

[0258] Table 4.4 Comparison of experimental data

[0259]

[0260]

[0261] Table 4.5 Comparison of experimental data

[0262]

[0263] After running the program 20 times and calculating the average runtime and optimal function value, simulation results of the improved algorithm showed that the optimization performance of the objective function value surpassed that of the original algorithm in all the aforementioned test scenarios. These results clearly demonstrate that the NHHO algorithm significantly improves the efficiency of task allocation, effectively guiding the population toward the global optimal solution, thereby enhancing the algorithm's global search capability. Furthermore, the improvements improved the algorithm's optimization performance in the initial phase and reduced the tendency to fall into local optimal solutions, demonstrating that the improved algorithm represents a substantial improvement over the traditional algorithm. However, simulation experiments also revealed a shortcoming of the NHHO algorithm: the improved task allocation time increased. This is due to the use of an online task allocation mechanism in the NHHO algorithm, which increases the amount of data computation and algorithm complexity, and thus the runtime.

[0264] This paper first improves the HHO algorithm and uses the contract network protocol in the online task allocation method to improve the Harris Hawk algorithm to prevent the algorithm from reaching local optimality too early, thereby improving the degree of convergence. It also introduces the spiral search method of the whale algorithm to enhance the algorithm's search performance in the early stages of the search. The improved algorithm is used to run the program for different initial conditions defined in Chapter 3, and the results are deeply analyzed. The results of this analysis clearly demonstrate the feasibility and effectiveness of the improved algorithm.

[0265] This paper proposes an NHHO algorithm and system for task allocation in heterogeneous carrier clusters. When a heterogeneous carrier cluster performs a sea strike mission, this algorithm and system enable rapid and accurate pre-assignment and online task allocation for each carrier in the cluster. The task allocation objective function significantly outperforms classic task allocation algorithms—genetic algorithms, particle swarm optimization algorithms, and the classic Harris Hawk algorithm. Therefore, this paper effectively solves the online task allocation problem for heterogeneous carrier clusters.

[0266] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0267] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A NHHO algorithm for online task allocation in heterogeneous carrier clusters, characterized by: The following steps are involved: a. Input the relevant data of the heterogeneous carrier cluster and the target, and determine the initialization parameters, including population size, maximum number of iterations, switching probability, and jump strength; the targets include class 1, class 2, and class 3; b. Initialize the population according to the adversarial environment; c. Apply the Harris Hawk algorithm and introduce a spiral update mechanism to calculate and determine the standard function value of each heterogeneous carrier cluster position, thereby obtaining the individual fitness value and determining the current optimal function value and corresponding position; d. Dynamically allocate tasks based on heterogeneous carrier clusters and Class 1 target discovery; e. Determine whether the conditions for task completion are met. If not, repeat steps c to d; f. Attacking Category 3 targets and Category 1 targets; g. Output the adaptability and time required for task allocation; In step c, by introducing a spiral update mechanism, the Harris Hawk algorithm can simulate the hunting behavior of an eagle and update the positions of the heterogeneous carrier cluster in a spiral manner, thereby optimizing task allocation; In step d, the dynamic task allocation is based on the current position of the heterogeneous carrier cluster and the discovery of Class 1 targets, and the optimal task allocation is achieved by evaluating the fitness value of each heterogeneous carrier; 3 types of targets U={U1,U2,...,Un}(attribute quadruple,<UC,PosC,JZC,WXCDC> , respectively representing the three types of target numbers, three types of target locations, mission value, and threat level); 1 type of target U={U1,U2,...,Un}(attribute quadruple,<UD,PosD,JZD,WXCDD> , respectively represent the number of category 1 target, the location of category 1 target, the mission value, and the threat level); All targets are targets to be attacked, and the locations of Category 3 targets are known, but the locations of Category 1 targets are unknown. Category 2 targets are similar to Category 1 targets, so they are not set separately. Only the mission value and threat level are changed in the parameter settings of Category 1 targets.

2. A heterogeneous carrier cluster online task allocation system implementing the NHHO algorithm for heterogeneous carrier cluster online task allocation as claimed in claim 1, characterized in that: The heterogeneous carrier cluster online task allocation system includes: The initialization module is used to input the relevant data of the heterogeneous carrier cluster and the target, and determine the initialization parameters, including the population size, maximum number of iterations, switching probability and jump strength; initialize the population according to the adversarial environment; the targets include Class 1, Class 2 and Class 3; The calculation module is used to apply the Harris Eagle algorithm to introduce a spiral update mechanism to calculate the standard function value of each heterogeneous carrier cluster position, determine the individual fitness value, obtain the current optimal function value and corresponding position; and update the positions of the three types of targets and the specific position of the one type of target; Allocation module, used for dynamic task allocation based on heterogeneous carrier clusters and Class 1 target discovery; The judgment module is used to judge whether the action distance reaches the standard. The output module is used to output the fitness and required time of task allocation.

3. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the heterogeneous carrier cluster online task allocation NHHO algorithm as claimed in claim 1.

4. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the heterogeneous carrier cluster online task allocation NHHO algorithm as claimed in claim 1.

5. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the heterogeneous carrier cluster online task allocation system as claimed in claim 2.

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