Multi-UUV array position distribution method
By introducing Piecewise chaotic mapping and dynamic weights into the coronavirus optimization algorithm, the variation method is improved, and the UUV positioning distance and coverage indicators are combined to optimize UUV positioning allocation, the poor population diversity and local convergence problems of UUV positioning allocation in the existing technology are solved, and faster convergence and higher optimization accuracy are achieved.
Patent Information
- Application Number
- CN202510291823.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-12
- Publication Date
- 2025-07-18
AI Technical Summary
The existing coronavirus optimization algorithms have problems such as poor population diversity, easy to fall into local convergence, low convergence accuracy and poor global optimization results in the allocation of UUV positions.
Piecewise chaotic mapping is used to initialize populations, introduce dynamic weights and dynamically adjust parameters through cosine function, improve the variation method of the coronavirus optimization algorithm, combine positioning distance and coverage indicators to build the objective function, and optimize the UUV position allocation plan.
The convergence speed and global optimization ability of UUV position allocation are improved, population diversity is enhanced, algorithm precocious puberty and local extreme value are avoided, and the accuracy and accuracy of allocation results are improved.
Smart Images
Figure CN120335480A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of UUV array position allocation, and particularly relates to a multi-UUV array position allocation method. Background Art
[0002] In the operation of multi-unmanned underwater vehicles (UUVs), the array position allocation method is affected by factors such as the diversity of task requirements and the feasibility of the allocation scheme. The quality of the UUV array position allocation scheme directly affects the efficiency and safety of the multi-UUV positioning process. Therefore, it is necessary to combine the actual situation and consider objectives such as time optimality and rationality optimality, and use an intelligent swarm algorithm with global optimization ability to optimize the design of the array position allocation scheme.
[0003] The Coronavirus Optimization Algorithm (COVIDOA) is a bio-inspired metaheuristic algorithm based on the COVID-19 transmission model proposed by the Egyptian scholar Asmaa M. Khalid. The main difference between it and other swarm optimization algorithms is that the Coronavirus Optimization Algorithm simulates the attack behavior of the novel coronavirus: (1) virus replication behavior; (2) virus mutation behavior. This algorithm has the characteristics of strong global search ability, fast convergence speed, strong adaptability, and high convergence accuracy, and has been widely applied in engineering optimization, image segmentation, scheduling problems, etc.
[0004] However, the standard Coronavirus Optimization Algorithm still has some defects: poor population diversity and easy to fall into the problem of local convergence. Therefore, in response to the above problems, some scholars have begun to conduct improvement research on the algorithm; for example, introducing the chaotic mapping method to enhance the diversity of the population. However, the method of introducing the chaotic mapping method to enhance the diversity of the population only improves the performance of the Coronavirus Optimization Algorithm to a certain extent, but the problems of decreased convergence performance in the later stage, low accuracy of the global optimization result, and easy to fall into local optimality still exist.
[0005] Therefore, it is necessary to provide a multi-UUV array position allocation method to solve the above problems. Summary of the Invention
[0006] The present invention provides a multi-UUV array position allocation method. Based on the Coronavirus Optimization Algorithm, it initializes the population using the Piecewise chaotic mapping, introduces a dynamic weight instead of a random weight, and at the same time uses an improved variable mutation method to gradually approach the optimal solution at different stages of the algorithm, so as to solve the problems of poor convergence accuracy, reduced convergence speed in the later stage, and easy to fall into local optimality in the existing technology for array position allocation design.
[0007] The multi-UUV array position allocation method of the present invention adopts the following technical solutions, including: Based on the initial position and the preset target position, construct an allocation model for the multi-UUV array position allocation scheme; Based on the standard coronavirus optimization algorithm, the population is initialized using Piecewise chaotic mapping, a dynamic weight is introduced to replace the random weight of the coronavirus optimization algorithm, and the mutation method of the coronavirus optimization algorithm is improved by dynamically adjusting parameters through the cosine function to obtain the improved target coronavirus optimization algorithm; Based on the approach distance index and coverage index of the target positions with respect to the initial positions in the array position allocation scheme, a target function of the target coronavirus optimization algorithm is constructed; Based on the target coronavirus optimization algorithm and the target function, the multi-UUV array position allocation scheme is optimized. Among them, each individual in the coronavirus optimization algorithm represents a solution of the target function corresponding to a UUV array position allocation scheme in the parameter optimization space; The UUV array position allocation scheme in the parameter optimization space corresponding to the maximum value among all target function values is taken as the optimal UUV array position allocation scheme.
[0008] Preferably, the steps for constructing the allocation model of the multi-UUV array position allocation scheme are as follows: Set the initial position matrix and target position matrix of the UUVs; Based on the UUV corresponding to each initial position in the initial position matrix and the target position point in the target position matrix to which the UUV is allocated, construct the allocation model of the multi-UUV array position allocation scheme. The expression of the allocation model is:
[0009] In the formula, represents the array position allocation scheme; represents the th UUV at the th initial position point is allocated to the th position point in the target position matrix.
[0010] Preferably, the steps for obtaining the approach distance index are as follows: Obtain the distance from each initial position point to each target position point; Based on the distance from each initial position point to each target position point, obtain the approach distance index.
[0011] Preferably, the expression for the distance from each initial position point to each target position point is:
[0012] In the formula, represents the distance between the th initial position point and the th target position point; represents the x coordinate of the th initial position point; represents the x coordinate of the j-th target array point; represents the y coordinate of the j-th target array point; represents the z coordinate of the j-th target array point.
[0013] Preferably, the expression of the in-place distance index is:
[0014] In the formula, represents the in-place distance index value; represents the distance between the i-th initial array point of the first UUV and the j-th target array point; represents the distance between the i-th initial array point of the N-th UUV and the j-th target array point.
[0015] Preferably, the steps to obtain the coverage index are: Perform duplicate removal and counting on the elements of the array allocation scheme; Based on the duplicate removal and counting results, and using the linear interpolation method, calculate the coverage index of the array allocation scheme. The expression of the coverage index is:
[0016] In the formula, represents the coverage index value; represents the th UUV with the initial array point allocated to the th array point in the target position matrix; represents the array allocation scheme; represents the duplicate removal and counting operation; represents the linear interpolation operation.
[0017] Preferably, the expression of the objective function is:
[0018] In the formula, represents the objective function value; represents the fitness value; represents the in-place distance index value; represents the coverage index value.
[0019] Preferably, the expression of the dynamic weight is:
[0020] In the formula, represents the dynamic weight; represents the position of the current individual in the population; is the total number of the population.
[0021] Preferably, the steps of improving the mutation method of the coronavirus optimization algorithm by dynamically adjusting parameters through the cosine function are as follows:
[0022] In the formula, is the number or position of the current individual in the population; is the total number of the population; is the updated virus state, is the current virus state, is the current optimal virus state.
[0023] Preferably, the expression of the virus state is.
[0024]
[0025] In the formula, is the virus state; is the allocation plan of the th individual's Nth UUV; is the population size; N is the total number of UUVs.
[0026] The beneficial effects of the present invention are as follows: Based on the standard coronavirus optimization algorithm, the population is initialized by Piecewise chaotic mapping, a dynamic weight is introduced to replace the random weight of the coronavirus optimization algorithm, and the mutation method of the coronavirus optimization algorithm is improved by dynamically adjusting parameters through the cosine function to obtain the improved target coronavirus optimization algorithm. Then, based on the in-place distance index and the coverage index, the objective function of the target coronavirus optimization algorithm is constructed. Then, based on the target coronavirus optimization algorithm and the objective function, the multi-UUV position allocation plan is optimized to obtain the optimal UUV position allocation plan; that is, the target coronavirus optimization algorithm uses Piecewise chaotic mapping to initialize the population, making the initial state of the population more evenly cover the parameter space, enhancing the population diversity, and effectively avoiding algorithm prematurity and falling into local extrema. The introduction of dynamic weights improves the convergence accuracy and global search ability of the optimization algorithm; the improvement of the mutation method increases the probability of jumping out of local extrema, thereby increasing the probability of reaching the global optimum and enhancing the ability of the algorithm to avoid falling into local extrema. The present invention has a faster convergence speed, higher optimization accuracy, and stronger global optimization ability when using the improved target coronavirus optimization algorithm and objective function, making the UUV position allocation result more accurate. Description of the Drawings
[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.
[0028] Figure 1 It is a flowchart of a multi-UUV array position allocation method of the present invention; Figure 2 It is a flowchart of the improved target coronavirus optimization algorithm provided in the embodiment; Figure 3 It is a change diagram of the fitness value in the embodiment; Figure 4 It is a UUV array position allocation scheme diagram finally designed in this embodiment; Figure 5 It is a schematic diagram of the average convergence curve corresponding to each test function in this embodiment. Detailed implementation manners
[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.
[0030] An embodiment of a multi-UUV array position allocation method of the present invention, as Figure 1 shown, includes: S1. Construct an allocation model; Specifically, based on the initial array positions and the preset target array positions, construct an allocation model for the multi-UUV array position allocation scheme; that is, first set the initial position matrix and the target position matrix of the UUVs; based on the UUV corresponding to each initial array position in the initial position matrix and the target array position point to which the UUV is allocated in the target position matrix, construct an allocation model for the multi-UUV array position allocation scheme.
[0031] Exemplarily, in a specific embodiment, the initial position matrix of the UUVs is:
[0032] In the formula, represents the initial array position (i.e., three-dimensional coordinates) of the Nth UUV; N is the total number of UUVs.
[0033] Exemplarily, in a specific embodiment, the target position matrix of the UUVs is:
[0034] In the formula, represents the target position (i.e., three-dimensional coordinates) of the Nth UUV; N is the total number of UUVs.
[0035] Exemplarily, in a specific embodiment, the expression of the allocation model of the multi-UUV position allocation scheme is:
[0036] In the formula, represents the position allocation scheme; represents the th UUV at the initial position point is allocated to the th position point of the target position matrix.
[0037] S2. Construct an improved target coronavirus optimization algorithm; Specifically, based on the standard coronavirus optimization algorithm, the population is initialized by Piecewise chaotic mapping, a dynamic weight is introduced to replace the random weight of the coronavirus optimization algorithm, and the mutation method of the coronavirus optimization algorithm is improved by dynamically adjusting the parameters through the cosine function to obtain the improved target coronavirus optimization algorithm.
[0038] Exemplarily, in a specific embodiment, the expression of the dynamic weight is:
[0039] In the formula, represents the dynamic weight; represents the position of the current individual in the population; is the total number of the population.
[0040] Exemplarily, in a specific embodiment, the steps of improving the mutation method of the coronavirus optimization algorithm by dynamically adjusting the parameters through the cosine function are:
[0041] In the formula, is the number or position of the current individual in the population; is the total number of the population; is the updated virus state; is the current virus state; is the current optimal virus state.
[0042] Exemplarily, in a specific embodiment, the expression of the virus state is:
[0043] In the formula, is the virus state; is the allocation plan of the th individual's Nth UUV;
[0044] S3. Construct the objective function of the improved objective coronavirus optimization algorithm; Specifically, based on the initial positions of the array position allocation plan for the in-position distance index and coverage index of the target position, construct the objective function of the objective coronavirus optimization algorithm.
[0045] Exemplarily, in a specific embodiment, the steps for obtaining the in-position distance index are as follows: obtain the distance from each initial position point to each target position point; according to the distance from each initial position point to each target position point, obtain the in-position distance index, that is, the expression of the in-position distance index is:
[0046] In the formula, represents the value of the in-position distance index; represents the distance between the th initial position point of the first UUV and the
[0047] jth target position point;
[0048] In the formula, represents the distance between the ith initial position point and the jth target position point; represents the x coordinate of the ith initial position point; represents the y coordinate of the ith initial position point;
[0049] Exemplarily, in a specific embodiment, the steps for obtaining the coverage index are as follows: de-duplicate and count the elements of the array position allocation plan; based on the de-duplication and counting results, and using the linear interpolation method, calculate the coverage index of the array position allocation plan, and the expression of the coverage index is:
[0050] In the formula, Indicates the coverage index value; Indicates the th initial array point of the UUV is assigned to the th array point of the target position matrix; Indicates the array position allocation scheme; Indicates the duplicate removal counting operation; Indicates the linear interpolation operation.
[0051] Exemplarily, in a specific embodiment, the expression of the objective function is:
[0052] In the formula, Indicates the objective function value; Indicates the fitness value; Indicates the in-place distance index value; Indicates the coverage index value.
[0053] S4. Optimize the multi-UUV array position allocation scheme and obtain the optimal UUV array position allocation scheme; Specifically, based on the target coronavirus optimization algorithm and the objective function, optimize the multi-UUV array position allocation scheme allocated by the allocation model. Among them, each individual in the coronavirus optimization algorithm represents a solution of the objective function corresponding to a UUV array position allocation scheme in the parameter optimization space; the UUV array position allocation scheme in the parameter optimization space corresponding to the maximum value among all objective function values is used as the optimal UUV array position allocation scheme.
[0054] Exemplarily, in a specific embodiment, as Figure 2 shown, the steps to optimize the multi-UUV array position allocation scheme based on the target coronavirus optimization algorithm and the objective function are: S41. Parameter setting of the target coronavirus optimization algorithm: Design the minimum variable and the maximum variable , that is, the feasible allocation scheme range of the UUV is ( , ); Define the dimension of the optimization problem, that is, the dimension is the number of UUVs; Set the optimization parameters, the number of individuals , the maximum number of iterations , the shift value , the number of sub-proteins , the mutation probability , the crossover parameter and , the initial position matrix , the target position matrix .
[0055] S42. Initialize the virus state: In a specific embodiment, the Piecewise chaotic mapping is used to initialize the virus state, and the expression of the virus state is:
[0056] In the formula, is the virus state; is the allocation scheme of the th individual's Nth UUV; is the population size; N is the total number of UUVs.
[0057] S43. Determine the objective function: In a specific embodiment, determining the objective function includes: Comprehensively consider the in-place distance index of the UUV array allocation and the scheme coverage index of the array allocation , and according to the [0.5, 0.5] weight combination, the fitness function is:
[0058] Take the maximum value of the fitness function score as the current objective function:
[0059] S44. Update the optimal solution: According to the objective function value of the current state of the nth virus , the maximum objective function value and the corresponding virus solution space position , Update the optimal fitness to , and the optimal solution is .
[0060] S45. Virus replication stage: For each solution in the population, select the individuals with high fitness as the parents through the roulette wheel selection method, and the selected parents are shifted by the "frame shift technology" to generate two sub-protein sequences: (1) If the +1 frame shift technology is used, the value of the parent solution is shifted 1 to the right, and the value of the first position is randomly set within the interval;
[0061]
[0062] Among them, is the minimum value of each variable in each solution; is the maximum value of each variable in each solution; represents the th protein generated; is the parent solution (the virus state of the parent); im is the problem dimension.
[0063] (2) If the -1 frame shift technology is used, the value of the parent solution is shifted 1 in the opposite direction, and the value of the last position is randomly set within the interval.
[0064]
[0065]
[0066] Uniform crossover is performed on these two sub-protein sequences to generate a new virus.
[0067] S46. Optimal solution update: According to the objective function value of each virus's current state as , the maximum objective function value and the corresponding virus solution space position are used to update the optimal fitness to , and the optimal solution is .
[0068] S47. Virus mutation stage: Since the standard coronavirus optimization algorithm may sometimes fall into local extrema during the solution process, this embodiment introduces a mutation operation to increase the probability of jumping out of local extrema.
[0069]
[0070] Among them, is the mutation rate; is the weight; is the position of the current individual in the population; is the total number of the population; is the updated virus state, is the current virus state, is the current optimal virus state.
[0071] Each candidate solution is mutated, and the new individuals after mutation and the original individuals together form a new population, and their fitness values are calculated.
[0072] S48. Population update: The original population and the new population are merged, sorted by fitness, and the individual with the highest fitness is retained, and the optimal fitness value in each iteration is recorded.
[0073] S49. After reaching the maximum number of iterations, the target coronavirus optimization algorithm outputs the optimal allocation solution and the corresponding fitness value.
[0074] S10. End the program. According to the optimal allocation solution, connect the initial position and the target position of each UUV, and draw a schematic diagram of the allocation result in three-dimensional space to visually display the allocation effect.
[0075] The feasibility verification of the multi-UUV position allocation method based on the improved target coronavirus optimization algorithm in this embodiment is as follows with reference to the attached drawings: Step 1. Establish an allocation model for the multi-UUV position allocation scheme: Let the initial position matrix of the UUVs be:
[0076] Let the target position matrix of the UUVs be:
[0077] Define the expression of the allocation model of the allocation scheme as follows
[0078] Step 2. Establish an evaluation model for the allocation scheme First, determine the in-place distance index , specifically including:
[0079]
[0080] Among them, is the number of UUVs; is the distance matrix.
[0081]
[0082] is the value of the in-place distance index.
[0083] Secondly, establish the coverage index of the position allocation scheme is determined, and the coverage index of the position allocation scheme is:
[0084] Step 3. Parameter settings of the target coronavirus optimization algorithm: Design the minimum variable and the maximum variable , that is, the feasible position range of the UUVs; define the dimension of the optimization problem, that is, the number of UUVs; set the optimization parameters, the number of individuals , the maximum number of iterations , shift value , number of sub-proteins , mutation probability , crossover parameter and , starting position matrix , ending position matrix .
[0085]
[0086]
[0087] Step 4, algorithm initialization Initialize using the Piecewise chaotic map, specifically including:
[0088] where is the allocation scheme, M is the population size, and N is the number of UUVs.
[0089] Step 5, virus replication stage based on frame-shift technology: For each solution in the population, select an individual with a higher fitness as the parent through roulette wheel selection. The selected parent is shifted using the "frame-shift technology" to generate two sub-protein sequences: (1) If the +1 frame-shift technology is used, the value of the parent solution is shifted 1 position to the right, and the value of the first position is randomly set within the interval.
[0090]
[0091] where is the minimum value of each variable in each solution; is the maximum value of each variable in each solution; represents the generated th protein; ositions_last is the parent solution; im is the problem dimension.
[0092] (2) If the -1 frame-shift technology is used, the value of the parent solution is shifted 1 position in the opposite direction, and the value of the last position is randomly set within the interval.
[0093]
[0094]
[0095] Uniform crossover is performed on these two sub-protein sequences to generate new viruses.
[0096] Step 6, Optimal solution update: For the newly generated candidate solution, use the allocation scheme evaluation model to calculate its fitness. If the fitness of the new solution is better than the current optimal solution, update it as the optimal solution.
[0097] Step 7, Mutation processing Since the standard coronavirus optimization algorithm sometimes gets stuck in local extrema during the solution process, a mutation operation is introduced here to increase the probability of jumping out of local extrema.
[0098]
[0099] Among them, is the mutation rate; is the weight; is the position of the current individual in the population; is the total number of the population; is the updated virus state, is the current virus state, is the current optimal virus state.
[0100] Step 8, Population update: Merge the original population and the new population, sort them by fitness, and retain the individual with the highest fitness. Record the optimal fitness value in each iteration. Among them, the fitness value change graph is as Figure 3 shown.
[0101] Step 9, After the maximum number of iterations, the algorithm outputs the optimal allocation solution and the corresponding fitness.
[0102] Step 10, End the program. According to the optimal allocation solution, connect the initial positions of each UUV with the preset positions, and draw a schematic diagram of the allocation result in three-dimensional space. The finally designed UUV array allocation scheme diagram is as Figure 4 shown, Figure 4 which can intuitively display the allocation effect. After the multi-UUV array allocation is completed, the results will be output: the optimal allocation scheme ; the optimal fitness . At the same time, to verify the feasibility of the proposed ICOVIDOA optimization algorithm (Objective Coronavirus Optimization Algorithm), the ICOVIDOA algorithm (Objective Coronavirus Optimization Algorithm) designed in this paper is compared with the classical particle swarm optimization algorithm (PSO) and the traditional COVIDOA optimization algorithm (Standard Coronavirus Optimization Algorithm). The 30-dimensional unimodal test function F1, 30-dimensional multimodal test functions F2, F3, and F4 in CEC2017 are selected, and the specific function expressions are shown in Table 1.
[0103] Table 1
[0104] Among them, D is the dimension of the solution, is the ideal optimal value, is the variable of the i-th dimension.
[0105] In order to reduce the influence of contingency on the performance test results, after 10 independent trials are conducted for each test function, the algorithm performance is compared from three aspects: the optimal value, the average value, and the standard deviation. The experimental settings are a population size of 50 and a maximum number of iterations of 300. The average convergence results of each function are as Figure 5 shown, and from Figure 5 it can be seen that for unimodal functions, the ICOVIDOA optimization algorithm has a stronger search ability compared with other algorithms, can obtain a better value within the number of iterations, and the optimization results are stable. For multimodal functions, the ICOVIDOA algorithm significantly improves the convergence speed; from the optimal value, the average value, and the variance, ICOVIDOA shows stronger local search ability and optimization accuracy compared with the other algorithms, and in Figure 5 the ICOVIDOA algorithm can approach the optimal value faster. It can be seen that the ICOVIDOA algorithm not only improves the optimization speed but also the optimization accuracy, and has higher result stability. Among them, Table 2 gives the optimization statistical results of each algorithm for the above test functions.
[0106] Table 2
[0107] From Figure 5 it can be known that after 500 iterations, the design of the multi-UUV position allocation method can be completed. Based on the standard coronavirus optimization algorithm, the method of the present invention initializes the population using Piecewise chaotic mapping, introduces dynamic weights instead of random weights, and at the same time uses an improved variable mutation method to gradually approach the optimal solution at different stages of the algorithm, improves the standard coronavirus optimization algorithm, and improves the convergence speed, the global search ability, and the solution accuracy of the optimal solution.
[0108] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. A multi-UUV position allocation method, characterized in that Including: Construct an allocation model for the multi-UUV array position allocation scheme based on the initial array position and the preset target array position; Based on the standard coronavirus optimization algorithm, initialize the population using Piecewise chaotic mapping, introduce a dynamic weight to replace the random weight of the coronavirus optimization algorithm, and improve the mutation method of the coronavirus optimization algorithm by dynamically adjusting parameters through the cosine function to obtain the improved target coronavirus optimization algorithm; Construct an objective function of the target coronavirus optimization algorithm based on the in-place distance index and coverage index of the target array position with respect to the initial array position of the array position allocation scheme; Optimize the multi-UUV array position allocation scheme allocated by the allocation model based on the target coronavirus optimization algorithm and the objective function, where each individual in the coronavirus optimization algorithm represents a solution of the objective function corresponding to a UUV array position allocation scheme in the parameter optimization space; Take the UUV array position allocation scheme in the parameter optimization space corresponding to the maximum value among all objective function values as the optimal UUV array position allocation scheme.
2. The multi-UUV position allocation method according to claim 1, wherein The steps to construct an allocation model for the multi-UUV array position allocation scheme are as follows: Set the initial position matrix and target position matrix of the UUV; Based on the UUV corresponding to each initial array position in the initial position matrix and the target array position point to which the UUV is allocated in the target position matrix, construct an allocation model for the multi-UUV array position allocation scheme. The expression of the allocation model is: In the formula, represents the array position allocation scheme; represents the th UUV assigned to the th array position of the target position matrix.
3. A method for multi-UUV position allocation according to claim 1, characterized in that, The steps to obtain the in-place distance index are as follows: Obtain the distance from each initial array position point to each target array position point; Based on the distance from each initial array position point to each target array position point, obtain the in-place distance index.
4. A method for multi-UUV position allocation according to claim 3, characterized in that The expression for the distance from each initial array position point to each target array position point is: In the formula, represents the distance between the i-th initial array point and the j-th target array point; represents the x coordinate of the i-th initial array point; represents the y coordinate of the i-th initial array point; represents the z coordinate of the i-th initial array point; represents the x coordinate of the j-th target array point; represents the y coordinate of the j-th target array point; represents the z coordinate of the j-th target array point.
5. A method for allocating positions of multiple UUVs according to claim 3, characterized in that The expression for the in-place distance index is: In the formula, represents the in-position distance index value; represents the distance between the i-th initial array point and the j-th target array point of the first UUV; represents the distance between the i-th initial array point and the j-th target array point of the N-th UUV.
6. The multi-UUV position allocation method according to claim 1, characterized in that, The steps to obtain the coverage index are as follows: De-duplicate and count the elements of the array position allocation scheme; Based on the de-duplication and counting results, and using the linear interpolation method, calculate the coverage index of the array position allocation scheme. The expression of the coverage index is: In the formula, represents the coverage index value; represents the th UUV assigned to the th array position in the target position matrix; represents the array position allocation scheme; represents the duplicate removal counting operation; represents the linear interpolation operation.
7. A method for allocating positions of multiple UUVs according to claim 1, characterized in that The expression of the objective function is: In the formula, represents the objective function value; represents the fitness value; represents the in-place distance index value; represents the coverage index value.
8. A method for multi-UUV position allocation according to claim 1, characterized in that The expression of the dynamic weight is: In the formula, represents the dynamic weight; represents the position of the current individual in the population; is the total number of the population.
9. A method for multi-UUV position allocation according to claim 1, characterized in that The steps to improve the mutation method of the coronavirus optimization algorithm by dynamically adjusting parameters through the cosine function are as follows: In the formula, is the number or position in the population of the current individual; is the total number of the population; is the updated virus state, is the current virus state, is the current optimal virus state.
10. A method for multi-UUV array position allocation according to claim 9, characterized in that, The expression of the virus state is: wherein, is the virus state; is the allocation scheme of the th UUV of the th individual; is the population size; N is the total number of UUVs.