MCR-WPT system tiny planar coil structure parameter optimization method based on spot bird optimization algorithm

Abstract

The invention provides an MCR-WPT system tiny planar coil structure parameter optimization method based on a spot turner optimization algorithm, and the method comprises the steps: deducing an MCR-WPT system energy transmission efficiency form through analyzing a compensation structure network; analyzing physical structure parameters in the tiny planar coil, and calculating self-inductance, mutual inductance and alternating-current resistance; calculating a theoretical self-inductance value of the micro planar coil; calculating a mutual inductance coefficient value between the coupling coils; a Litz wire is adopted to wind a coupling coil; calculating the alternating current resistance of the Litz wire under the high-frequency alternating current; the coils of the transmitting end and the receiving end have the same specification and parameter; establishing a coupling coil structure optimization model; setting upper and lower boundaries of parameter variables; adding constraint conditions; taking the energy transmission efficiency as an optimization target of the target function; and carrying out optimization calculation on the coupling coil structure optimization model by using a kirschner optimization algorithm. The method is shorter in average operation time and higher in operation speed.

Description

Technical Field

[0001] The present application relates to the field of portable electronic technology, and in particular to a method for optimizing the structural parameters of a tiny planar coil of an MCR-WPT system based on a Kingfisher optimization algorithm. Background Art

[0002] The MCR-WPT system is a technology that uses high-frequency electromagnetic fields to achieve wireless energy transmission through the principle of resonant coupling. At present, there are the following problems in the optimization of the structural parameters of the small planar coupling coils in the MCR-WPT system: the design of the coupling coil mainly relies on a large number of simulations and experiments, gradually analyzing the parameters of the coil, or first calculating the Q value of the coil and then trying to design the corresponding coil. The existing problem is that the design efficiency is low and the physical structural parameters of the coupling coil cannot be directly obtained.

[0003] Traditional optimization algorithms, such as genetic algorithms and particle swarm optimization algorithms, often face problems such as low search efficiency, easy to fall into local optimality, and sensitivity to parameter settings when dealing with the optimization problem of coupled coils in MCR-WPT systems. These algorithms are difficult to find the optimal solution quickly and accurately in a complex electromagnetic field environment.

[0004] Based on this, it is necessary to invent a new coupling coil structure optimization method, use a new swarm intelligence algorithm to solve the deficiencies in the existing design, improve the design efficiency and directly obtain the optimized physical structure parameters to solve the parameter optimization problem in the existing coupling coil design. Summary of the invention

[0005] The present application provides a method for optimizing the structural parameters of a tiny planar coil of an MCR-WPT system based on a Kingfisher optimization algorithm, which can be used to solve the technical problem of optimizing the efficiency of coupling coil design in the current MCR-WPT system.

[0006] The present application provides a method for optimizing the structural parameters of a small planar coil of an MCR-WPT system based on a kingfisher optimization algorithm, the method comprising:

[0007] Step S1: derive the energy transmission efficiency form of the MCR-WPT system by analyzing the compensation structure network;

[0008] Step S2: Analyze the physical structure parameters of the tiny planar coil, and calculate the self-inductance, mutual inductance, and AC resistance by coupling the coil structure parameters;

[0009] Step S3: Calculate the theoretical self-inductance of the tiny planar coil;

[0010] Step S4: Calculate the mutual inductance value between the coupled coils;

[0011] Step S5: using Litz wire to wind a coupling coil; calculating the AC resistance of the Litz wire under high frequency AC;

[0012] Step S6: Make the coils at the transmitting end and the receiving end have the same specifications and parameters to ensure the numerical consistency of the resonant capacitor;

[0013] Step S7: Establishing a coupling coil structure optimization model by calculating and analyzing the coupling coil parameters;

[0014] Step S8: Set the upper and lower limits of the parameter variables, using the power frequency, the wire diameter of the planar coil, the pitch and the distance between the two coils as the optimization variables; add the constraint condition k≤k s ;Energy transmission efficiency is used as the optimization goal of the objective function;

[0015] Step S9: Use the Kingfisher optimization algorithm to optimize the coupled coil structure optimization model, introduce Kent chaos mapping into the initialization stage to perform head chaos mutation, and introduce α-stable distribution for the foraging strategy of the PKO algorithm to enhance the algorithm's ability to search for the global optimal solution, avoid the algorithm from falling into the local optimal solution, and accelerate the convergence speed.

[0016] Compared with other mainstream high-performance algorithms, the optimization method based on the kingfisher optimization algorithm described in the present invention has a shorter average running time, a faster operation speed and a higher calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the process of the present invention.

[0018] Figure 2 It is a schematic diagram of the SS topology structure in step S1 of the present invention.

[0019] Figure 3 It is a structural diagram of the coaxial planar circular coil of the present invention.

[0020] Figure 4 It is a flow chart of the PKO optimization algorithm in step S9 of the present invention.

[0021] Figure 5 It is a schematic diagram of the transient model of the coupling coil of the present invention. DETAILED DESCRIPTION

[0022] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0023] The following first introduces the embodiments of the present application in conjunction with the accompanying drawings.

[0024] A method for optimizing the structure of a small planar coil of an MCR-WPT system based on a kingfisher optimization algorithm is implemented by the following steps:

[0025] Step S1: By analyzing the compensation structure network, the energy transmission efficiency form of the MCR-WPT system is derived.

[0026] The energy transfer efficiency is as follows:

[0027]

[0028] In formula (1), η represents the energy transfer efficiency of the system; ω represents the resonant angular frequency of the system; M represents the mutual inductance of the coupling coil; R1 and R2 represent the internal resistance of the primary and secondary coils respectively; R L Indicates the load size of the system.

[0029] Step S2: Analyze the physical structure parameters of the tiny planar coil, and calculate the self-inductance, mutual inductance, and AC resistance by coupling the coil structure parameters.

[0030] The calculated input coupling coil parameter range is: outer diameter 8mm, inner diameter 2mm, wire diameter range 0.1mm-1mm, pitch range 0.01mm-1mm, spacing range 3mm-5mm, power supply frequency range 50KHz-150KHz.

[0031] Step S3: Calculate the theoretical self-inductance of the tiny planar coil.

[0032] The theoretical self-inductance of the coil is determined as follows:

[0033]

[0034] Where L is the theoretical self-inductance of the coil, μ0 is the magnetic permeability of vacuum, r max is the outer radius of the coil, r min is the inner radius of the coil, C1=2.46, C2=0.2.

[0035] Step S4: Calculate the mutual inductance value between the coupled coils.

[0036] The method for determining the mutual inductance value between the coupled coils is as follows:

[0037]

[0038] Where M is the theoretical mutual inductance of the coil, N r is the number of turns of the primary coil, N t is the number of turns of the secondary coil, r r is the average radius of the primary coil, r t is the average radius of the secondary coil, and h is the distance between the coils.

[0039] Step S5: In order to reduce the loss caused by the increase of the coil AC resistance under high-frequency conditions and improve the energy transmission efficiency of the MCR-WPT system, the coupling coil is wound with Litz wire; and the AC resistance of the Litz wire under high-frequency AC is calculated.

[0040] The method to determine the AC resistance value is as follows:

[0041]

[0042] Among them, R ac is the AC resistance value, k is the number of Litz wire strands, d str Single wire diameter.

[0043] Step S6: Make the coils at the transmitting end and the receiving end have the same specifications and parameters to ensure the numerical consistency of the resonant capacitor. For the SS topology compensation network, the coupling coefficient calculation formula is:

[0044]

[0045] The critical coupling coefficient is calculated as:

[0046]

[0047] The quality factor Q is calculated as:

[0048]

[0049] Where f is the frequency.

[0050] Step S7: Establishing a coupling coil structure optimization model by calculating and analyzing the coupling coil parameters.

[0051] The coupled coil structure optimization model is shown in formula (8); where f is the frequency, R is the internal resistance of the coil, M is the theoretical mutual inductance value of the coil, N is the number of coil turns, r is the average radius of the coil, and h is the distance between the coils; d str is the single-strand wire diameter, k is the coupling coefficient, k s is the critical coupling coefficient; these parameters jointly affect the energy transfer efficiency of the system.

[0052]

[0053] Step S8: Set the upper and lower limits of the parameter variables, using the power frequency, the wire diameter of the planar coil, the pitch and the distance between the two coils as the optimization variables; set the upper and lower limits of the parameter variables. To prevent the phenomenon of frequency splitting, add the constraint condition k≤k s ; Taking energy transmission efficiency as the optimization goal of the objective function.

[0054] Step S9: Use the Kingfisher optimization algorithm to optimize the coupling coil structure optimization model, introduce Kent chaos mapping to the initialization stage for head chaos mutation, and introduce α-stable distribution for the foraging strategy of the PKO algorithm to enhance the algorithm's ability to search for the global optimal solution, avoid the algorithm from falling into the local optimal solution, and accelerate the convergence speed. In this way, the physical structure parameters of the coupling coil of the MCR-WPT system can be quickly calculated.

[0055] Step S9 includes:

[0056] Step S91, algorithm initialization stage: The PKO algorithm is initialized using a random initialization method to randomly generate an initial population in the search space; assuming that the dimension space is D and the population size is N; the algorithm generates an initial population, and the solution of each individual is randomly generated, ranging between the upper and lower bounds of the search space; each individual in the population will represent the current position of the kingfisher; the calculation formula is as follows:

[0057]

[0058] In the formula, X i,j represents the position of the i-th individual in the j-th dimension, rand represents a random value between 0 and 1, UB and LB represent the upper and lower bounds of the search range;

[0059] Step S92, determining a foraging strategy: the strategy simulates the behavior of the kingfisher hovering or perching in the air to find prey; the kingfisher is simulated to detect in a wide search area to find a potential optimal solution;

[0060] The position of individuals is dynamically adjusted using random factors to ensure that the entire solution space is fully explored. This stage aims to avoid falling into a local optimal solution. The formula for updating the position is calculated based on the distance between individuals to ensure that the kingfisher can explore unexplored areas:

[0061]

[0062] During the iteration, the solution of the next iteration is X i (t+1) indicates the position of the current iteration, and X i (t) represents; the parameter α is calculated as 2*randn(1,dim), where randn represents a random number in a normal distribution; N represents the population size, and dim represents the dimension of the problem under consideration;

[0063] The value of parameter T is determined dynamically based on the current strategy, including whether to perch or hover, to ensure optimal performance in different operating modes;

[0064] The T parameter in the habitat strategy is calculated as follows:

[0065]

[0066] Crest_angles=2*pi*rand(12);

[0067] The maximum number of iterations is specified by Max_Iter; the constant value BF, i.e., the beat factor, is set to 8, and rand is a random value between 0 and 1.

[0068] The T parameter in the hover strategy is calculated as follows:

[0069]

[0070] The fitness of the i-th and j-th kingfishers are represented by PKO_Fitness(i) and PKO_Fitness(j), respectively; similarly, the constant value BF is set to 8, and rand is a random value between 0 and 1.

[0071] Step S93, the diving strategy simulates the diving hunting behavior of the kingfisher; the goal is to search and develop the found excellent solution; the algorithm moves the individual toward the current optimal solution, thereby performing local search and optimization; each individual will update its position according to its own fitness value and the position of the best individual in the population to ensure that it can get closer to the global optimal solution; the calculation formula is as follows:

[0072] X i (t+1)=X i (t)+HA*o*α*(bX best (t)),i=1,2,…N (15);

[0073] Among them, the fitness value of the i-th kingfisher is recorded as PKO_Fitness(i), Best_Fitness represents the best fitness value obtained from all iterations, α is a control parameter, calculated as 2*randn(1,dim)-1, o and HA represent hunting ability, calculated by the following two formulas:

[0074]

[0075]

[0076] b=X i (t)+o 2 *randn*X best (t)(18);

[0077] Step S94, the symbiotic strategy simulates the symbiotic relationship between the kingfisher and other animals (such as the symbiosis with the otter), and compares and optimizes the fitness by referring to the positions of other individuals; when the individual refers to other solutions, the better solution will be retained according to the comparison results, so as to avoid falling into the local optimum; this is achieved by comparing and exchanging positions with randomly selected individuals, similar to cache search, to help the algorithm jump out of the local optimum; the calculation formula is as follows:

[0078]

[0079] α=2*randn(1,dim)-1 (21);

[0080]

[0081] In the formula, two individuals are randomly selected from the population, and their positions are represented by X. m and X n The predation efficiency of kingfisher is represented by PE, where PE max and PE min The fixed values ​​are 0.5 and 0 respectively;

[0082] Step S95, the development strategy simulates the kingfisher using its symbiotic partner to return to the storage area for a detailed search; in the comparison between the new and old positions, the algorithm decides whether to adopt the new solution by judging the fitness value; in this stage, switching between foraging and symbiotic strategies is performed to ensure a balance between the global and local optimization of the search.

[0083] Step S96, end stage: the algorithm continues to cycle through each stage until the stop condition is met, such as reaching the maximum number of iterations or finding a satisfactory solution; in each iteration, the individual searches for the global optimal solution through foraging exploration and development strategies;

[0084] Step S97, introduce Kent chaos mapping into the initialization stage to perform head chaos mutation, the chaos mapping evenly distributes the initialization population to the entire solution space, enhances the algorithm's ability to search for the global optimal solution, avoids the algorithm from falling into the local optimal solution, speeds up the convergence speed, and achieves fast calculation.

[0085] The expression of the Kent chaotic map initialization population is as follows:

[0086]

[0087] Control parameter a∈(0, 1); the chaotic orbit state value range is (0, 1); the population distribution is initialized by the Kent chaotic mapping strategy, and the generated population is more uniform, which is more conducive to the global traversal of the optimization space by particles and can enhance the global optimization ability of the algorithm.

[0088] Step S98, introduce α-stable distribution for the foraging strategy of the PKO algorithm. In the speed update formula, the random number generated by α-stable distribution is used to replace the traditional pseudo-random number rand, thereby increasing the ability of the algorithm to jump out of the local optimum.

[0089] The optimization model was constructed using the PKO optimization algorithm framework for verification. The number of iterations was set to 1000 in the operation. In order to ensure the accuracy of the results and the inherent randomness of the search algorithm, 50 independent rounds of operations were performed, and finally a representative output was obtained by comprehensive analysis of the operation results. By analyzing the obtained data results, the average solution time of the PKO algorithm is 0.14s, and it can stably converge to the optimal solution in about 150 iterations. Compared with the whale optimization algorithm (WOA), Harris Hawk algorithm (HHO), sine-cosine optimization algorithm (SCA) and other algorithms, PKO has significant advantages in convergence speed and calculation accuracy. Compared with the traditional coupling coil design optimization method, the MCR-WPT system micro-planar coil structure optimization method described in the present invention has the following advantages: Compared with other mainstream high-performance algorithms, the optimization method based on the kingfisher optimization algorithm described in the present invention has a shorter average running time, faster operation speed and higher calculation efficiency.

[0090] When the outer diameter is set to 8mm and the inner diameter is set to 2mm, within the given parameter range, the optimal coupling coil parameter configuration is determined by optimization calculation: the power frequency is 150KHz, the wire diameter is 0.18mm, the distance between the primary coil and the secondary coil is 3mm, the number of turns is 15.7, and the energy transmission efficiency is achieved up to 13.88%. The result data of the coupling coil under different parameter specifications are shown in Table 1.

[0091] Table 1 Performance comparison of different parameters

[0092]

[0093] Through the obtained result data, the energy transmission efficiency of the optimized coupling coil structure parameters is the highest compared with other parameters, which verifies the effectiveness and feasibility of the adopted optimization method.

[0094] The present application optimizes the physical structural parameters of the coil through the Kingfisher optimization algorithm to improve the energy transmission efficiency of the system; calculates the self-inductance and mutual inductance of the coil, as well as the AC resistance considering the skin effect and proximity effect under high-frequency AC; verifies the optimized coil structure through electromagnetic simulation software to ensure the effectiveness of the optimization parameters; derives the calculation formula for the energy transmission efficiency of the system by analyzing the SS topology compensation structure network; the calculation formula includes parameters such as the resonant angular frequency, mutual inductance value, internal resistance of the primary and secondary coils, and system load size; optimizes the electrical characteristics of the coil by calculating the theoretical self-inductance value of the tiny planar coil; the calculation of the self-inductance value takes into account the outer diameter, inner diameter, wire diameter and coil spacing of the coil; optimizes the coupling effect between the coils by calculating the mutual inductance coefficient value between the coupled coils; the calculation of the mutual inductance coefficient value takes into account the primary coil and the secondary coil. The number of turns, average radius and distance between coils are taken into account; the resistance characteristics of the coil are optimized by calculating the AC resistance of the Litz wire under high-frequency AC; the calculation of the AC resistance takes into account the number of strands and single-strand wire diameter of the Litz wire; by considering the frequency splitting phenomenon of the MCR-WPT system, constraints are established to prevent over-coupling; by jointly calculating the energy transmission efficiency calculation formula, an optimization model for the coupled coil structure is established; the optimization model takes into account the influence of the physical structure parameters of the coil on the energy transmission efficiency; by setting the upper and lower limits and constraints of the optimization variables, the energy transmission efficiency is taken as the optimization target; the optimization variables include power supply frequency, wire diameter, pitch and coil spacing of the coil; chaotic mapping and α-stable distribution are used for the initialization and foraging strategy of the optimization algorithm to speed up the convergence of the algorithm and avoid falling into the local optimal solution.

[0095] The above-described embodiments of the present application do not constitute a limitation on the protection scope of the present application.

Claims

1. A method for optimizing the structural parameters of a small planar coil of an MCR-WPT system based on the Kingfisher optimization algorithm, characterized in that: The method comprises: Step S1: derive the energy transmission efficiency form of the MCR-WPT system by analyzing the compensation structure network; Step S2: Analyze the physical structure parameters of the tiny planar coil, and calculate the self-inductance, mutual inductance, and AC resistance by coupling the coil structure parameters; Step S3: Calculate the theoretical self-inductance of the tiny planar coil; Step S4: Calculate the mutual inductance value between the coupled coils; Step S5: using Litz wire to wind a coupling coil; calculating the AC resistance of the Litz wire under high frequency AC; Step S6: Make the coils at the transmitting end and the receiving end have the same specifications and parameters to ensure the numerical consistency of the resonant capacitor; Step S7: Establishing a coupling coil structure optimization model by calculating and analyzing the coupling coil parameters; Step S8: Set the upper and lower limits of the parameter variables, using the power frequency, the wire diameter of the planar coil, the pitch and the distance between the two coils as the optimization variables; add the constraint condition k≤k s ;Energy transmission efficiency is used as the optimization goal of the objective function; Step S9: Use the Kingfisher optimization algorithm to optimize the coupled coil structure optimization model, introduce Kent chaos mapping into the initialization stage to perform head chaos mutation, and introduce α-stable distribution for the foraging strategy of the PKO algorithm to enhance the algorithm's ability to search for the global optimal solution, avoid the algorithm from falling into the local optimal solution, and accelerate the convergence speed.

2. The method according to claim 1, characterized in that In step S1, the energy transmission efficiency is as follows: In formula (1), η represents the energy transfer efficiency of the system; ω represents the resonant angular frequency of the system; M represents the mutual inductance of the coupling coil; R1 and R2 represent the internal resistance of the primary and secondary coils respectively; R L Indicates the load size of the system.

3. The method according to claim 1, characterized in that In step S2, the input coupling coil parameter range is calculated as follows: outer diameter 8 mm, inner diameter 2 mm, wire diameter range 0.1 mm-1 mm, pitch range 0.01 mm-1 mm, spacing range 3 mm-5 mm, power supply frequency range 50 KHz-150 KHz.

4. The method according to claim 1, characterized in that: In step S3, the method for determining the theoretical self-inductance of the coil is as follows: Where L is the theoretical self-inductance of the coil, μ0 is the magnetic permeability of vacuum, r max is the outer radius of the coil, r min is the inner radius of the coil, C1=2.46, C2=0.

2.

5. The method according to claim 1, characterized in that In step S4, the method for determining the mutual inductance value between the coupling coils is as follows: Where M is the theoretical mutual inductance of the coil, N r is the number of turns of the primary coil, N t is the number of turns of the secondary coil, r r is the average radius of the primary coil, r t is the average radius of the secondary coil, and h is the distance between the coils.

6. The method according to claim 1, characterized in that In step S5, the method for determining the AC resistance value is as follows: Among them, R ac is the AC resistance value, k is the number of Litz wire strands, d str Single wire diameter.

7. The method according to claim 1, characterized in that Step S6: Make the coils at the transmitting end and the receiving end have the same specifications and parameters to ensure the numerical consistency of the resonant capacitor, including: For the SS topology compensation network, the coupling coefficient calculation formula is: The critical coupling coefficient is calculated as: The quality factor Q is calculated as: Where f is the frequency.

8. The method according to claim 1, characterized in that Step S7: Establishing a coupling coil structure optimization model by calculating and analyzing the coupling coil parameters, including: The coupled coil structure optimization model is shown in formula (8); where f is the frequency, R is the internal resistance of the coil, M is the theoretical mutual inductance value of the coil, N is the number of coil turns, r is the average radius of the coil, and h is the distance between the coils; d str is the single-strand wire diameter, k is the coupling coefficient, k s is the critical coupling coefficient; 9. The method according to claim 1, characterized in that: Step S9 includes: Step S91, algorithm initialization stage: The PKO algorithm is initialized using a random initialization method to randomly generate an initial population in the search space; assuming that the dimension space is D and the population size is N; the algorithm generates an initial population, and the solution of each individual is randomly generated, ranging between the upper and lower bounds of the search space; each individual in the population will represent the current position of the kingfisher; the calculation formula is as follows: Where, X i,j represents the position of the i-th individual in the j-th dimension, rand represents a random value between 0 and 1, UB and LB represent the upper and lower bounds of the search range; Step S92, determining a foraging strategy: the strategy simulates the behavior of the kingfisher hovering or perching in the air to find prey; the kingfisher is simulated to detect in a wide search area to find a potential optimal solution; The formula for updating the position is calculated based on the distance between individuals using random factors to dynamically adjust the position of individuals, ensuring that the kingfisher can explore unexplored areas: During the iteration, the solution of the next iteration is X i (t+1) indicates the position of the current iteration, and X i (t) represents; the parameter α is calculated as 2*randn(1,dim), where randn represents a random number in a normal distribution; N represents the population size, and dim represents the dimension of the problem under consideration; The value of parameter T is determined dynamically based on the current strategy, including whether to perch or hover, to ensure optimal performance in different operating modes; The T parameter in the habitat strategy is calculated as follows: Crest_angles=2*pi*rand (12); The maximum number of iterations is specified by Max_Iter; the constant value BF, i.e., the beat factor, is set to 8, and rand is a random value between 0 and 1. The T parameter in the hover strategy is calculated as follows: The fitness of the i-th and j-th kingfishers are represented by PKO_Fitness(i) and PKO_Fitness(j), respectively; similarly, the constant value BF is set to 8, and rand is a random value between 0 and 1. Step S93, the diving strategy simulates the diving hunting behavior of the kingfisher; the goal is to search and develop the found excellent solution; the algorithm moves the individual toward the current optimal solution, thereby performing local search and optimization; each individual will update its position according to its own fitness value and the position of the best individual in the population to ensure that it can get closer to the global optimal solution; the calculation formula is as follows: X i (t+1)=X i (t)+HA*o*α*(b-X best (t)),i=1,2,…N (15); Among them, the fitness value of the i-th kingfisher is recorded as PKO_Fitness(i), Best_Fitness represents the best fitness value obtained from all iterations, α is a control parameter, calculated as 2*randn(1,dim)-1, o and HA represent hunting ability, calculated by the following two formulas: b=X i (t)+o 2 *randn*X best (t) (18); Step S94, the symbiotic strategy simulates the symbiotic relationship between the kingfisher and other animals, and compares and optimizes the fitness by referring to the positions of other individuals; when an individual refers to other solutions, it will retain the better solution based on the comparison results to avoid falling into the local optimum; this is achieved by comparing and exchanging positions with randomly selected individuals, similar to cache search, to help the algorithm jump out of the local optimum; the calculation formula is as follows: α=2*randn(1,dim)-1 (21); In the formula, two individuals are randomly selected from the population, and their positions are represented by X. m and X n The predation efficiency of kingfisher is represented by PE, where PE max and PE min The fixed values ​​are 0.5 and 0 respectively; Step S95, the development strategy simulates the kingfisher using its symbiotic partner to return to the storage area; in the comparison between the new and old positions, the algorithm determines whether to adopt the new solution by judging the fitness value; Step S96, end stage: the algorithm continues to cycle through each stage until the stop condition is met; in each iteration, the individual searches for the global optimal solution through foraging exploration and development strategies; Step S97, introduce Kent chaos mapping into the initialization stage to perform head chaos mutation. Chaos mapping evenly distributes the initialization population to the entire solution space. The expression of Kent chaos mapping initialization population is as follows: Control parameter a∈(0,1); the chaotic orbit state value range is (0,1); the population distribution is initialized by the Kent chaotic mapping strategy, and the generated population is more uniform; Step S98, introduce α-stable distribution for the foraging strategy of the PKO algorithm, and use the random number generated by α-stable distribution to replace the traditional pseudo-random number rand in the speed update formula.

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