CN111382896B - WTA target optimization method of self-adaptive chaotic parallel clone selection algorithm - Google Patents
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Abstract
The invention relates to a WTA target optimization method of a self-adaptive chaos parallel clone selection algorithm, which solves the problem of weapon target distribution of an air defense formation, combines the advantages of chaos theory and parallel population classification, and realizes population initialization and population update; wherein, a population initialization operator and a mutation operator are designed by utilizing chaos regeneration and chaos disturbance; designing a parallel mechanism for each sub population by adopting a parallel population classification method, and keeping population diversity according to affinity; the adaptive clone proliferation operator, the antibody inhibition operator and the antibody circulation supplementing operator are designed to improve the clone selection algorithm, and the operators can improve the global optimization capacity and the local searching capacity.
Description
Technical Field
The invention belongs to the field of firepower distribution, and particularly relates to a WTA target optimization method of a self-adaptive chaotic parallel clonal selection algorithm.
Background
With the development of modern military transformation, the expression form of sea warfare is gradually changed from individual warfare to formation warfare, but formation anti-air combat is always faced with serious air target threat. Therefore, the research of the fire distribution (weapon target assignment, WTA)) is very important, and the aim is to study the optimized decision relationship between weapon and target, so as to maximize the expected effect of the overall combat effectiveness. The WTA problem is essentially a nonlinear combinatorial optimization problem, which is a typical non-deterministic polynomial completion problem.
Aiming at WTA models with different application backgrounds, different algorithms are provided by students at home and abroad to improve the calculation efficiency and the accuracy:
the traditional mathematical linear or nonlinear method is mainly adopted to solve the model in the early stage, but because of the large number of weapons and targets, the traditional methods are easy to cause the problem of high computational complexity, and can not meet the requirements of accuracy and instantaneity of application; in recent years, with the development of computer technology, some heuristic intelligent algorithms such as differential evolution, tabu search, neural networks, genetic algorithms, particle swarm algorithms and the like attract more and more scholars' attention, and under different conditions, the extraction methods show good solving capability. However, these algorithms have more initial parameters and large calculation amount.
The artificial immune algorithm is used as an intelligent simulation method of the natural immune system function, and is one of the latest research results of intelligent optimization; the cloning selection algorithm (Colonal selection algorithm, CSA) is introduced into WTA by a plurality of scholars, so that complex calculation and time consumption can be avoided, but the crossover probability and mutation probability of the cloning selection algorithm are quantitative values, the affinity and concentration of antibodies are not considered, and the adaptability and the robustness of the algorithm are reduced; in order to increase the convergence rate of the algorithm, combining the advantages of CSA and genetic algorithms, improved CSA algorithms have been proposed, but the premature phenomenon cannot be overcome.
In order to overcome the above drawbacks, it is therefore desirable to develop new CSA algorithms to achieve a high quality, efficient solution of WTA problems.
Disclosure of Invention
In order to solve the defects in the existing firepower distribution scheme, the invention provides a WTA target optimization method of a self-adaptive chaotic parallel clone selection algorithm.
The technical problems to be solved by the invention are realized by the following technical scheme:
a WTA target optimization method of a self-adaptive chaotic parallel clone selection algorithm comprises the following steps:
step 1: constructing an optimization model of formation air defense war WTA, wherein the optimization model f is defined as:
wherein p is ij ∈[0,1]Representing the effectiveness of the weapon, W representing the number of weapons, i=1, 2 … W, T representing the target number of enemy threats, j=1, 2 … T, λ j ∈[0,1]Representing the damage probability of an enemy threat target, x ij Indicating whether or not the ith weapon is assigned to the jth enemy threat target, if x is assigned ij =1, otherwise x ij ＝0；
Step 2: the antibody, the antigen and the affinity are defined through the comparison of an immune response mechanism and a WTA model, wherein the antigen represents an objective function and a constraint condition, and the antibody represents all potential solutions of firepower distribution, namely all solutions of an optimized model number f; affinity provides a quantitative estimate of antibodies, antigens, representing the maximum mathematical expectation of operational efficacy, and is defined as:
step 3: initializing related parameters including antibody scale N, memory population scale M (M < N), and maximum iteration number K;
step 4: setting the current iteration number to k, and calculating the antibody X through a formula (3) i (k) Adaptation to antigensDegree f i (k),i∈N；
Step 5: judging termination conditions: if the current iteration number k=k, the optimal result X is output opt ；
Otherwise let k=k+1 and pass the fitness f i (k) Calculating the memory population P m (k)；
Specifically, according to the fitness f i (k) Ranking the values to form antibody population P * (k) And from said antibody population P * (k) Selecting M optimal individuals to form a memory population P m (k)；
Step 6: for antibody population P * (k) Parallel classification is performed to generate a plurality of antibody sub-populations including elite sub-population P E (k) Conventional sub-population P G (k) And inferior sub-population P I (k)；
Step 7: for elite seed population P E (k) Conventional sub-population P G (k) Performing cloning propagation calculation to obtain cloned elite seed populationAnd the conventional sub-population->
Step 8: for cloned elite seed populationAnd the conventional sub-population->Performing chaos mutation calculation to obtain mutated elite seed population +.>And the conventional sub-population->
Step 9: for mutated elite seed populationAnd the conventional sub-population->Performing clone selection calculation to obtain an antibody evolution population P M (k) Specific:
respectively for elite sub-populations by formula (3)And the conventional sub-population->The individual in (2) carries out fitness calculation;
if antibody X i The fitness f (X) i ) Smaller than the corresponding new antibodies generated after the treatment of step 7 and step 8Is->Namely:
then use variant antibodiesInstead of the original antibody X i The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, continuing to retain the original antibody population P * (k) Antibody X in (B) i ；
Step 10: calculation of antibody evolved population P M (k) Diversity D of (2) ij And by diversity D ij Performing antibody inhibition judgment;
step 11: updating antibody population P (k) =p m (k)+P M (k)+P N (k) I.e. the updated population comprisesMemory population P m (k) Evolved population P M (k) And new inferior subgroup P N (k)；
Step 12: recalculating fitness f between antigen and antibody in the updated population using equation (3) i (k) And according to the fitness f i (k) The values of (2) are arranged in a descending order, M high-adaptability antibodies are selected to update the memory population, and a new memory population P is formed m (k)；
Step 13: and returning to the step 5.
Further, in the step 3, the initialization antibody P (0) is calculated by using a Logistic mapping chaotic function;
P(0)＝[X B 1 (0),X B 2 (0)…X B N (0)]whereini is E N, B is the dimension of the variable;
the method comprises the following steps: define y as chaotic variable, y 0 E (0, 1), and y 0 Not equal to 0.5, h is the maximum number of iterations of the chaos, μ is a control parameter of the chaos behavior, and the Logistic mapping is:
where H is the current number of chaotic iterations, h=n,represents the kth generation chaotic variable,>represents the k+1st generation chaotic variable; the process comprises the following steps:
further, the specific content of the parallel classification of the pairs of antibody populations in the step 6 is as follows:
antibody population P in proportion E G I * (k) Divided into elite seed population P E (k) Conventional sub-population P G (k) And inferior sub-population P I (k) Three classes, wherein e+g+i=1, p E (k)+P G (k)+P I (k)＝P * (k)＝P(k)。
Further, the elite sub-population P in the step 7 E (k) Conventional sub-population P G (k) The calculation method for cloning propagation comprises the following steps:
H k ＝H k.a /H k.max (7)
γ k ＝round[N×(γ 0 +ω(1-H k ))] (8)
wherein N represents the size of the antibody population, a ib The b variable, a, representing the i-th antibody jb The b variable, H, representing the j-th antibody ij Represents the affinity between antibody i and antibody j, H a Represents the average affinity of the antibody population, H k Represents the population diversity of the kth generation antibody, H k.max Represents the maximum affinity of the kth generation of antibody population; gamma ray 0 Represents the cloning radix, ω represents the cloning factor, γ k Represents the clone scale, round represents the rounding symbol.
Further, the cloned elite seed population is subjected to the step 8And the conventional sub-population->The specific contents of the chaos variation calculation are as follows:
r＝βe (-αT) (10)
in the method, in the process of the invention,representing the new variable generated by the antibody i, b-th variable, r representing the disturbance factor, y i Representing chaotic variables, beta and alpha representing tuning variables.
Further, the specific content of the step 10 is:
calculation of antibody X i With antibody X j Euclidean distance of (2), and using said Euclidean distance to represent diversity D of population ij I.e.
Sigma is a threshold for determining whether or not antibody inhibition is performed, typically sigma e [0:1];
if D ij > sigma, inferior sub-population P is performed I (k) Updates of, in particular, inferior sub-population P I (k) Chaotic regeneration is carried out by the formula (4) to form a new inferior sub population P with equal scale N (k) The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, the inferior sub-population P is not performed I (k) Is updated according to the update of the update program.
The invention has the beneficial effects that:
the invention builds a formation air combat WTA optimization model, comprehensively utilizes a chaos theory, a parallel population classification model and a clone selection algorithm, designs various operators, and also provides a target optimization method of a self-adaptive chaos parallel clone selection algorithm, thereby improving global optimization capacity and local search capacity.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
Fig. 1 is a schematic diagram of a target optimization method of an adaptive chaotic parallel clonal selection algorithm.
Fig. 2 is a schematic diagram of the adaptation process of the adaptive chaotic parallel clonal selection algorithm.
Detailed Description
The following detailed description, structural features and functions of the present invention are provided with reference to the accompanying drawings and examples in order to further illustrate the technical means and effects of the present invention to achieve the predetermined objects.
Example 1:
referring to fig. 1, the present embodiment provides a WTA target optimization method of a self-adaptive chaotic parallel clonal selection algorithm, including the following steps:
step 1: constructing an optimization model f of formation anti-air combat WTA, wherein the WTA aims at maximizing the combat effectiveness of weapon equipment to the target number, and the optimization model f is defined as:
wherein, the formula (2) represents a constraint condition; p is p ij ∈[0,1]Representing the effectiveness of the weapon, W representing the number of weapons, i=1, 2 … W, T representing the target number of enemy threats, j=1, 2 … T, λ j ∈[0,1]Representing the probability of damage to enemy threat targets, [ x ] ij ] W×T As decision matrix, x ij Indicating whether or not the ith weapon is assigned to the jth enemy threat target, if x is assigned ij =1, otherwise x ij ＝0；
Step 2: the antibody, the antigen and the affinity are defined through the comparison of an immune response mechanism and a WTA model, wherein the antigen represents an objective function and a constraint condition, and the antibody represents all potential solutions of firepower distribution, namely all solutions of an optimized model number f; affinity provides a quantitative estimate of antibodies, antigens, representing the maximum mathematical expectation of combat efficacyI.e. affinity is defined as:
as can be seen from the above formula (3), the affinity is the optimized model in step 1, and the same is the fitness, that is, the affinity, the fitness, and the mathematical expression of the optimized model are the same;
step 3: initializing related parameters including antibody scale N, memory population scale M (M < N), and maximum iteration number K; wherein:
in the step 3, initializing an antibody P (0) by using a Logistic mapping chaotic function calculation;
P(0)＝[X B 1 (0),X B 2 (0)…X B N (0)]whereini is E N, B is the dimension of the variable;
the method comprises the following steps: define y as chaotic variable, y 0 E (0, 1), and y 0 Not equal to 0.5, h is the maximum number of iterations of the chaos, μ is a control parameter of the chaos behavior, and the Logistic mapping is:
where H is the current number of chaotic iterations, h=n,represents the kth generation chaotic variable,>represents the k+1st generation chaotic variable; the process comprises the following steps:
step 4: setting the current iteration number to k, and calculating the antibody X through a formula (3) i (k) Fitness with antigen f i (k),i∈N；
Step 5: judging termination conditions: if the current iteration number k=k, the optimal result X is output opt ；
Otherwise let k=k+1 and pass the fitness f i (k) Calculating the memory population P m (k)；
Specifically, according to the fitness f i (k) Ranking the values to form antibody population P * (k) And from said antibody population P * (k) Selecting M optimal individuals to form a memory population P m (k)；
Step 6: for antibody population P * (k) Parallel classification is performed to generate a plurality of antibody sub-populations including elite sub-population P E (k) Conventional sub-population P G (k) And inferior sub-population P I (k)；
The specific content of the parallel classification of the pairs of antibody populations in the step 6 is as follows:
antibody population P in proportion E G I * (k) Divided into elite seed population P E (k) Conventional sub-population P G (k) And inferior sub-population P I (k) Three classes, wherein e+g+i=1, p E (k)+P G (k)+P I (k)＝P * (k)＝P(k)。
Step 7: for elite seed population P E (k) Conventional sub-population P G (k) Performing cloning propagation calculation to obtain cloned elite seed populationAnd the conventional sub-population->Wherein:
in the step 7, the elite seed group P E (k) Conventional sub-population P G (k) The calculation method for cloning propagation comprises the following steps:
H k ＝H k.a /H k.max (7)
γ k ＝round[N×(γ 0 +ω(1-H k ))] (8)
wherein N represents the size of the antibody population, a ib The b variable, a, representing the i-th antibody jb The b variable, H, representing the j-th antibody ij Represents the affinity between antibody i and antibody j, H a Represents the average affinity of the antibody population, H k Represents the population diversity of the kth generation antibody, H k.max Represents the maximum affinity of the kth generation of antibody population; gamma ray 0 Represents the cloning radix, ω represents the cloning factor, γ k Represents the clone scale, round represents the rounding symbol.
Step 8: for cloned elite seed populationAnd the conventional sub-population->Performing chaos mutation calculation to obtain mutated elite seed population +.>And the conventional sub-population->
The cloned elite seed population in step 8And the conventional sub-population->The specific contents of the chaos variation calculation are as follows:
r＝βe (-αT) (10)
in the method, in the process of the invention,representing the new variable generated by the antibody i, b-th variable, r representing the disturbance factor, y i Representing chaotic variables, beta and alpha representing tuning variables.
Step 9: for mutated elite seed populationAnd the conventional sub-population->Performing clone selection calculation to obtain an antibody evolution population P M (k) Specific:
respectively for elite sub-populations by formula (3)And the conventional sub-population->The individual in (2) carries out fitness calculation;
if antibody X i The fitness f (X) i ) Smaller than the corresponding new antibodies generated after the treatment of step 7 and step 8Is->I.e.
Then use variant antibodiesInstead of the original antibody X i The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, continuing to retain the original antibody population P * (k) Antibody X in (B) i ；
Step 10: calculation of antibody evolved population P M (k) Diversity D of (2) ij And by diversity D ij Performing antibody inhibition judgment;
the specific content of the step 10 is as follows:
calculation of antibody X i With antibody X j Euclidean distance of (2), and using said Euclidean distance to represent diversity D of population ij I.e.
Sigma is a threshold for determining whether or not antibody inhibition is performed, typically sigma e [0:1];
if D ij > sigma, inferior sub-population P is performed I (k) Updates of, in particular, inferior sub-population P I (k) Chaotic regeneration is carried out by the formula (4) to form a new inferior sub population P with equal scale N (k) The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, the inferior sub-population P is not performed I (k) Is updated according to the update of the update program.
Step 11: updating antibody population P (k) =p m (k)+P M (k)+P N (k) I.e. the updated population comprises a memory population P m (k) Evolved population P M (k) And new inferior subgroup P N (k)；
In this example, antibody population P * (k) Is obtained by ranking the antibodies in P (k) by fitness, i.e.P * (k) The size of (a) is equal to the size of P (k).
Step 12: recalculating the adaptation between antigen and antibody in the updated population using equation (3)Degree f i (k) And according to the fitness f i (k) The values of (2) are arranged in a descending order, M high-adaptability antibodies are selected to update the memory population, and a new memory population P is formed m (k)；
Step 13: and returning to the step 5.
Example 2:
in order to verify the feasibility and effectiveness of the optimization model and method proposed in example 1, a typical simulation model was designed as follows: the formation air defense system consists of 6 weapon systems, and is used for positively conducting 10 invasion to an enemy target, namely W=6, T=10 and the weapon damage probability p ij (i=1, … 6,j =1, 2 … 10) and target threat system r j (j=1, 2 … 10) is shown in tables 1 and 2.
TABLE 1 probability of weapon unit destruction
TABLE 2 target threat coefficients
| Target object | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Threat coefficient | 0.09 | 0.12 | 0.14 | 0.06 | 0.05 | 0.10 | 0.08 | 0.09 | 0.15 | 0.12 |
The parameters of the adaptive chaotic parallel clonal selection algorithm are set as follows: maximum iteration number k=500, population size n=100, memory population size m=0.15N, cloning radix γ 0 Group classification ratio E: G: i=0.2:0.4:0.2, cloning factor ω=0.25, cloning scale γ k =0.15N; related chaotic variable setting: the chaos behavior control parameter μ=4, the chaos maximum iteration number h=60, and the adjustment variables β=0.5 and α=0.6.
As can be seen from fig. 2, WTA target optimization based on the adaptive chaotic parallel clone selection algorithm finally converges to 0.9821 in the 40 th generation, and meanwhile, global optimal solution and local optimal solution are considered, so that the WTA target optimization has high stability. The optimal output result is shown in Table 3, WTA decision matrix X opt See formula (13).
Table 3 distribution results
| Weapon unit | Enemy target |
| 1 | 1,3,4,10 |
| 2 | 1,1,2,3,5 |
| 3 | 4,6,6,7 |
| 4 | 7,8,10 |
| 5 | 7,8,10 |
| 6 | 7,8,10 |
From equation (13) it can be seen that the weapon distribution scenario, such as the first column of the matrix, represents that the weapon unit needs to attack targets 1, 3, 4, 10; also, since the number of weapon units is smaller than the enemy target, the presence of one weapon unit attacks multiple local targets. Finally, the operational efficiency of the air defense weapon system in this scenario is 0.9821.
The invention provides a target optimization method of a self-adaptive chaotic parallel clonal selection algorithm, which solves the problem of target allocation (WTA) of an air defense formation weapon. The algorithm combines the advantages of chaos theory and parallel population classification, and realizes population initialization and population update. Wherein, a population initialization operator and a mutation operator are designed by utilizing chaos regeneration and chaos disturbance; designing a parallel mechanism for each sub population by adopting a parallel population classification method, and keeping population diversity according to affinity; the CSA is improved by designing a self-adaptive clone proliferation operator, an antibody inhibition operator and an antibody circulation supplementing operator, and the operators can improve the global optimization capacity and the local searching capacity. Finally, the simulation verification self-adaptive chaotic parallel clone selection algorithm has good optimization performance in the aspects of search precision and convergence flexibility, and a high-efficiency method is provided for solving WTA in formation anti-air combat.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (5)
1. The WTA target optimization method of the self-adaptive chaotic parallel clonal selection algorithm is characterized by comprising the following steps of:
step 1: constructing an optimization model of formation air defense war WTA, wherein the optimization model f is defined as:
wherein p is ij ∈[0,1]Representing the effectiveness of the weapon, W representing the number of weapons, i=1, 2 … W, T representing the target number of enemy threats, j=1, 2 … T, λ j ∈[0,1]Representing the damage probability of an enemy threat target, x ij Indicating whether or not the ith weapon is assigned to the jth enemy threat target, if x is assigned ij =1, otherwise x ij ＝0；
Step 2: the antibody, the antigen and the affinity are defined through the comparison of an immune response mechanism and a WTA model, wherein the antigen represents an objective function and a constraint condition, and the antibody represents all potential solutions of firepower distribution, namely all solutions of an optimized model number f; affinity provides a quantitative estimate of antibodies, antigens, representing the maximum mathematical expectation of operational efficacy, and is defined as:
step 3: initializing related parameters including antibody scale N, memory population scale M (M < N), and maximum iteration number K;
step 4: setting the current iteration number to k, and calculating the antibody X through a formula (3) i (k) Fitness with antigen f i (k),i∈N；
Step 5: judging termination conditions: if the current iteration number k=k, the optimal result X is output opt ；
Otherwise let k=k+1 and pass the fitness f i (k) Calculating the memory population P m (k)；
Specifically, according to the fitness f i (k) Ranking the values to form antibody population P * (k) And from said antibody population P * (k) Selecting M optimal individuals to form a memory population P m (k)；
Step 6: for antibody population P * (k) Parallel classification is performed to generate a plurality of antibody sub-populations including elite sub-population P E (k) Conventional sub-population P G (k) And inferior sub-population P I (k)；
Step 7: for elite seed population P E (k) Conventional sub-population P G (k) Performing cloning propagation calculation to obtain cloned elite seed populationAnd the conventional sub-population->
Step 8: for cloned elite seed populationAnd the conventional sub-population->Performing chaos mutation calculation to obtain mutated elite seed population +.>And the conventional sub-population->
Step 9: for mutated elite seed populationAnd the conventional sub-population->Performing clone selection calculation to obtain an antibody evolution population P M (k) Specific:
respectively for elite sub-populations by formula (3)And the conventional sub-population->The individual in (2) carries out fitness calculation;
if antibody X i The fitness f (X) i ) Smaller than the corresponding new antibodies generated after the treatment of step 7 and step 8Is->Namely:
then use variant antibodiesInstead of the original antibody X i The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, continuing to retain the original antibody population P * (k) Antibody X in (B) i ；
Step 10: calculation of antibody evolved population P M (k) Diversity D of (2) ij And by diversity D ij Performing antibody inhibition judgment;
step 11: updating antibody population P (k) =p m (k)+P M (k)+P N (k) I.e. the updated population comprises a memory population P m (k) Evolved population P M (k) And new inferior subgroup P N (k)；
Step 12: recalculating fitness f between antigen and antibody in the updated population using equation (3) i (k) And according to the fitness f i (k) The values of (2) are arranged in a descending order, M high-adaptability antibodies are selected to update the memory population, and a new memory population P is formed m (k)；
Step 13: returning to the step 5;
in the step 3, initializing an antibody P (0) by using a Logistic mapping chaotic function calculation;
P(0)＝[X B 1 (0),X B 2 (0)...X B N (0)]whereinB is the dimension of the variable;
the method comprises the following steps: define y as chaotic variable, y 0 E (0, 1), and y 0 Not equal to 0.5, h is the maximum number of iterations of the chaos, μ is a control parameter of the chaos behavior, and the Logistic mapping is:
where H is the current number of chaotic iterations, h=n,represents the kth generation chaotic variable,>represents the k+1st generation chaotic variable; the process comprises the following steps:
2. the WTA target optimization method according to claim 1, wherein the specific content of the parallel classification of the pairs of antibody populations in step 6 is:
antibody population P in proportion E G I * (k) Divided into elite seed population P E (k) Conventional sub-population P G (k) And inferior sub-population P I (k) Three classes, wherein e+g+i=1, p E (k)+P G (k)+P I (k)＝P * (k)＝P(k)。
3. The WTA target optimization method according to claim 2, wherein said step 7 is performed on elite sub-population P E (k) Conventional sub-population P G (k) The calculation method for cloning propagation comprises the following steps:
H k ＝H k.a /H k.max (7)
γ k ＝round[N×(γ 0 +ω(1-H k ))] (8)
wherein N represents the size of the antibody population, a ib The b variable, a, representing the i-th antibody jb The b variable, H, representing the j-th antibody ij Represents the affinity between antibody i and antibody j, H a Represents the average affinity of the antibody population, H k Represents the population diversity of the kth generation antibody, H k.max Represents the maximum affinity of the kth generation of antibody population; gamma ray 0 Represents the cloning radix, ω represents the cloning factor, γ k Represents the clone scale, round represents the rounding symbol.
4. The WTA target optimization method according to claim 3, wherein said step 8 is performed on a cloned elite sub-populationAnd the conventional sub-population->The specific contents of the chaos variation calculation are as follows:
r＝βe (-αT) (10)
in the method, in the process of the invention,representing the new variable generated by the antibody i, b-th variable, r representing the disturbance factor, y i Representing chaotic variables, beta and alpha representing tuning variables.
5. The WTA target optimization method according to claim 4, wherein the specific contents of step 10 are:
calculation of antibody X i With antibody X j Euclidean distance of (2), and using said Euclidean distance to represent diversity D of population ij I.e.
Sigma is a threshold for determining whether or not antibody inhibition is performed, typically sigma e [0:1];
if D ij > sigma, inferior sub-population P is performed I (k) Updates of, in particular, inferior sub-population P I (k) Chaotic regeneration is carried out by the formula (4) to form a new inferior sub population P with equal scale N (k) The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, the inferior sub-population P is not performed I (k) Is updated according to the update of the update program.
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