CN111382896A - WTA target optimization method of adaptive chaotic parallel clonal selection algorithm - Google Patents

WTA target optimization method of adaptive chaotic parallel clonal selection algorithm Download PDF

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CN111382896A
CN111382896A CN201811645323.0A CN201811645323A CN111382896A CN 111382896 A CN111382896 A CN 111382896A CN 201811645323 A CN201811645323 A CN 201811645323A CN 111382896 A CN111382896 A CN 111382896A
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梁洪涛
朱鑫
田华
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Shaanxi Normal University
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Abstract

The invention relates to a WTA target optimization method of a self-adaptive chaotic parallel clone selection algorithm, which solves the problem of weapon target distribution of air defense formation; wherein, a population initialization operator and a mutation operator are designed by utilizing chaotic regeneration and chaotic disturbance; performing parallel mechanism design on each sub-population by adopting a parallel population classification method, and keeping population diversity according to affinity; the self-adaptive clone multiplication operator, the antibody inhibition operator and the antibody circulation supplement operator are designed to improve the clone selection algorithm, and the operators can improve the global optimization capability and the local search capability.

Description

WTA target optimization method of adaptive chaotic parallel clonal selection algorithm
Technical Field
The invention belongs to the field of fire 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 manifestation of sea battle gradually changes from single-soldier operation to formation operation, but formation air defense operation always faces serious air target threat. Therefore, the research on the fire power distribution (WTA) is very important, and the aim is to research the optimal decision relationship between weapons and targets and maximize the expected effect of the overall combat effectiveness. The WTA problem is essentially a nonlinear combinatorial optimization problem, which is typically a non-deterministic polynomial complete problem.
For WTA models with different application backgrounds, scholars at home and abroad propose different algorithms to improve the calculation efficiency and precision:
in the early stage, the traditional mathematical linear or nonlinear methods are mainly adopted to solve the model, but because the number of weapons and targets is large, the traditional methods easily cause the problem of high computational complexity and cannot meet the requirements of accuracy and real-time performance of application; in recent years, with the development of computer technology, some enlightening intelligent algorithms such as differential evolution, tabu search, neural networks, genetic algorithms, particle swarm optimization and the like attract more and more students' attention, and the enlightening intelligent algorithms show good solving capability under different conditions. However, these algorithms have more initial parameters and large calculation amount.
The artificial immune algorithm is used as an intelligent imitation method of the natural immune system function, and is one of the latest research results of intelligent optimization; the method has the advantages that a good scholars introduces a Clone Selection Algorithm (CSA) into the WTA, so that the complexity and time consumption of calculation can be avoided, but the cross probability and the mutation probability of the clone selection algorithm are quantitative values, the affinity and the concentration of an antibody are not considered, and the adaptability and the robustness of the algorithm are reduced; in order to improve the convergence rate of the algorithm and integrate the advantages of the CSA and the genetic algorithm, an improved CSA algorithm is provided, but the premature phenomenon cannot be overcome.
In order to overcome the above disadvantages, it is desirable to develop a new CSA algorithm to implement high-quality and high-efficiency solution of the WTA problem.
Disclosure of Invention
In order to overcome the defects in the conventional fire distribution scheme, the invention provides a WTA target optimization method of a self-adaptive chaotic parallel clone selection algorithm.
The technical problem to be solved by the invention is realized by the following technical scheme:
a WTA target optimization method of a self-adaptive chaotic parallel clonal selection algorithm comprises the following steps:
step 1: constructing an optimization model of the formation air defense combat WTA, wherein an optimization model f is defined as:
Figure BDA0001931952430000021
in the formula, pij∈[0,1]Indicating the effectiveness of the weapon, W indicating the number of weapons, i 1,2 … W, T indicating the number of enemy threat objects, j 1,2 … T, λj∈[0,1]Representing the damage probability, x, of an enemy threat objectijIndicating whether the ith weapon was assigned to the jth enemy threat target, if x was assignedij1, otherwise xij＝0；
Step 2: defining antibodies, antigens and affinities by comparing an immune response mechanism with a WTA model, wherein the antigens represent an objective function and constraint conditions, and the antibodies represent all potential solutions of fire power distribution, namely all solutions of an optimization model number f; affinity provides a quantitative estimate of antibody, antigen, representing the greatest mathematical expectation of combat effectiveness, and is defined as:
Figure BDA0001931952430000022
and step 3: initializing relevant parameters including an antibody scale N, a memory population scale M (M < N), and a maximum iteration number K;
and 4, step 4: setting the current iteration number as k, and calculating the antibody X by formula (3)i(k) Fitness to antigen fi(k),i∈N；
And 5: judging a termination condition: if the current iteration number K is equal to K, outputting an optimal result Xopt；
Otherwise, let k be k +1 and pass the fitness fi(k) Calculating memory population Pm(k)；
In particular, according to the fitness fi(k) Is ranked to form an antibody population P*(k) And from said antibody population P*(k) M optimal individuals are selected to form a memory populationPm(k)；
Step 6: for antibody population P*(k) Performing parallel classification to generate multiple antibody sub-populations including elite sub-population PE(k) Conventional sub-population PG(k) And a rogue sub-population PI(k)；
And 7: respectively for elite sub-population PE(k) Conventional sub-population PG(k) Performing clone reproduction calculation to obtain cloned elite population
Figure BDA0001931952430000031
And conventional sub-populations
Figure BDA0001931952430000032
And 8: respectively aligning the cloned elite populations
Figure BDA0001931952430000033
And conventional sub-populations
Figure BDA0001931952430000034
Performing chaotic variation calculation to obtain the varied elite population
Figure BDA0001931952430000035
And conventional sub-populations
Figure BDA0001931952430000036
And step 9: for variant elite population
Figure BDA0001931952430000037
And conventional sub-populations
Figure BDA0001931952430000038
Performing clone selection calculation to obtain an antibody evolution population P of the antibody evolution populationM(k) Specifically, the method comprises the following steps:
respectively aligning the elite sub-populations by the formula (3)
Figure BDA0001931952430000039
And conventional sub-populations
Figure BDA00019319524300000310
Calculating the fitness of the individual;
if the antibody XiFitness f (X)i) Less than the corresponding new antibody generated after the treatment of step 7 and step 8
Figure BDA00019319524300000311
Is adapted to
Figure BDA00019319524300000312
Namely:
Figure BDA00019319524300000313
then using the variant antibody
Figure BDA00019319524300000314
Replacing original antibody Xi(ii) a Otherwise, the original antibody population P is kept*(k) Antibody X of (1)i；
Step 10: calculation of antibody evolution population PM(k) Diversity of DijAnd by diversity DijPerforming antibody inhibition judgment;
step 11: renewing antibody population P (k) ═ Pm(k)+PM(k)+PN(k) I.e. the updated population comprises the memory population Pm(k) Evolved population PM(k) And a new inferior subgroup PN(k)；
Step 12: recalculating the fitness f between the antigen and the antibody in the population after the update using equation (3)i(k) And according to the fitness fi(k) The values of (A) are arranged in a descending order, M antibodies with high fitness are selected to update the memory population, and a new memory population P is formedm(k)；
Step 13: and returning to the step 5.
Further, in the step 3, an initialization antibody P (0) is calculated by using a Logistic mapping chaotic function;
P(0)＝[XB 1(0),XB 2(0)...XB N(0)]wherein
Figure BDA0001931952430000041
B is a variable dimension;
the method comprises the following specific steps: defining y as a chaotic variable, y0∈ (0,1), and y0Not equal to 0.5, H is the maximum iteration number of chaos, mu is the control parameter of the chaos behavior, and the Logistic mapping is as follows:
Figure BDA0001931952430000042
wherein H is the current chaos iteration number, H is N,
Figure BDA0001931952430000043
represents the k-th generation of chaotic variables,
Figure BDA0001931952430000044
representing a k +1 th generation chaotic variable; the following steps are generated through the process:
Figure BDA0001931952430000045
further, the specific content of the parallel classification of the antibody populations in step 6 is as follows:
antibody population P is divided in the ratio E, G, I*(k) Is divided into elite population PE(k) Conventional sub-population PG(k) And a rogue sub-population PI(k) Three groups, wherein E + G + I is 1, PE(k)+PG(k)+PI(k)＝P*(k)＝P(k)。
Further, in the step 7, the elite population P is treatedE(k) Conventional sub-population PG(k) The calculation method for carrying out clone propagation comprises the following steps:
Figure BDA0001931952430000046
Figure BDA0001931952430000051
Hk＝Hk.a/Hk.max(7)
γk＝round[N×(γ0+ω(1-Hk))](8)
wherein N represents the size of the antibody population, aibDenotes the b variable, a, of the ith antibodyjbDenotes the b variable, H, of the j antibodyijIndicates the affinity between antibody i and antibody j, HaRepresents the average affinity, H, of the antibody populationkDenotes the diversity of the antibody population of the k generation, Hk.maxRepresents the maximum affinity of the antibody population of the k generation; gamma ray0Denotes cloning base number, ω denotes cloning factor, γkRepresents the clone scale and round represents the rounding symbol.
Further, the cloned elite population is subjected to the step 8
Figure BDA0001931952430000052
And conventional sub-populations
Figure BDA0001931952430000053
The specific contents of the chaotic variation calculation are as follows:
Figure BDA0001931952430000054
r＝βe(-αT)(10)
in the formula (I), the compound is shown in the specification,
Figure BDA0001931952430000055
new variables generated by the b-th variable of the antibody i are shown, r is a disturbance coefficient, yi is a chaotic variable, and β and α are shown as adjusting variables.
Further, the specific content of step 10 is:
calculation of antibody XiWith antibody XjAnd in said euclidean distanceDiversity of the population DijI.e. by
Figure BDA0001931952430000056
σ is a threshold for determining whether or not antibody inhibition is performed, and is generally σ ∈ [ 0-1 ];
if D isijIf the sum is more than sigma, updating the inferior sub-population PI (k), and specifically, chaotic regeneration of the inferior sub-population PI (k) through a formula (4) to form a new inferior sub-population P with equal scaleN(k) (ii) a Otherwise, the inferior sub-population P is not performedI(k) And (4) updating.
The invention has the beneficial effects that:
the invention constructs a formation air defense combat WTA optimization model, comprehensively utilizes a chaos theory, a parallel population classification model and a clonal selection algorithm, designs various operators, and also provides a target optimization method of a self-adaptive chaos parallel clonal selection algorithm to improve the global optimization capability and the local search capability.
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 diagram illustrating a process of fitness change of an adaptive chaotic parallel clonal selection algorithm.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the intended purpose, the following detailed description of the embodiments, structural features and effects of the present invention will be made with reference to the accompanying drawings and examples.
Example 1:
referring to fig. 1, the 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 the formation air defense combat WTA, wherein the WTA aims at maximizing the combat effectiveness of weaponry on the target quantity, and the optimization model f is defined as:
Figure BDA0001931952430000061
Figure BDA0001931952430000062
in the formula, formula (2) represents a constraint condition; p is a radical ofij∈[0,1]Indicating the effectiveness of the weapon, W indicating the number of weapons, i 1,2 … W, T indicating the number of enemy threat objects, j 1,2 … T, λj∈[0,1]Represents the damage probability of the enemy threat object, [ x ]ij]W×TAs a decision matrix, xijIndicating whether the ith weapon was assigned to the jth enemy threat target, if x was assignedij1, otherwise xij＝0；
Step 2: defining antibodies, antigens and affinities by comparing an immune response mechanism with a WTA model, wherein the antigens represent an objective function and constraint conditions, and the antibodies represent all potential solutions of fire power distribution, namely all solutions of an optimization model number f; the affinity provides a quantitative estimate of the antibody, antigen, and represents the maximum mathematical expectation of combat effectiveness
Figure BDA0001931952430000071
That is, the affinity is defined as:
Figure BDA0001931952430000072
according to the formula (3), the affinity, namely the optimization model in the step 1, is also equivalent to the fitness hereinafter, namely the affinity, the fitness and the mathematical expression of the optimization model are the same;
and step 3: initializing relevant parameters including an antibody scale N, a memory population scale M (M < N), and a maximum iteration number K; wherein:
in the step 3, calculating an initialized antibody P (0) by adopting a Logistic mapping chaotic function;
P(0)＝[XB 1(0),XB 2(0)...XB N(0)]whereinB is a variable dimension;
the method comprises the following specific steps: defining y as a chaotic variable, y0∈ (0,1), and y0Not equal to 0.5, H is the maximum iteration number of chaos, mu is the control parameter of the chaos behavior, and the Logistic mapping is as follows:
Figure BDA0001931952430000074
wherein H is the current chaos iteration number, H is N,
Figure BDA0001931952430000075
represents the k-th generation of chaotic variables,
Figure BDA0001931952430000076
representing a k +1 th generation chaotic variable; the following steps are generated through the process:
Figure BDA0001931952430000077
and 4, step 4: setting the current iteration number as k, and calculating the antibody X by formula (3)i(k) Fitness to antigen fi(k),i∈N；
And 5: judging a termination condition: if the current iteration number K is equal to K, outputting an optimal result Xopt；
Otherwise, let k be k +1 and pass the fitness fi(k) Calculating memory population Pm(k)；
In particular, according to the fitness fi(k) Is ranked to form an antibody population P*(k) And from said antibody population P*(k) M optimal individuals are selected to form a memory population Pm(k)；
Step 6: for antibody population P*(k) Performing parallel classification to generate multiple antibody sub-populations including elite sub-population PE(k) Conventional, conventionalSub-population PG(k) And a rogue sub-population PI(k)；
The concrete content of parallel classification of the antibody population in the step 6 is as follows:
antibody population P is divided in the ratio E, G, I*(k) Is divided into elite population PE(k) Conventional sub-population PG(k) And a rogue sub-population PI(k) Three groups, wherein E + G + I is 1, PE(k)+PG(k)+PI(k)＝P*(k)＝P(k)。
And 7: respectively for elite sub-population PE(k) Conventional sub-population PG(k) Performing clone reproduction calculation to obtain cloned elite population
Figure BDA0001931952430000081
And conventional sub-populations
Figure BDA0001931952430000082
Wherein:
the Elite seed population P in the step 7E(k) Conventional sub-population PG(k) The calculation method for carrying out clone propagation comprises the following steps:
Figure BDA0001931952430000083
Figure BDA0001931952430000084
Hk＝Hk.a/Hk.max(7)
γk＝round[N×(γ0+ω(1-Hk))](8)
wherein N represents the size of the antibody population, aibDenotes the b variable, a, of the ith antibodyjbDenotes the b variable, H, of the j antibodyijIndicates the affinity between antibody i and antibody j, HaRepresents the average affinity, H, of the antibody populationkDenotes the diversity of the antibody population of the k generation, Hk.maxRepresents the maximum affinity of the antibody population of the k generation; gamma ray0Representing clonesThe base number, ω, represents the cloning factor, γkRepresents the clone scale and round represents the rounding symbol.
And 8: respectively aligning the cloned elite populations
Figure BDA0001931952430000091
And conventional sub-populations
Figure BDA0001931952430000092
Performing chaotic variation calculation to obtain the varied elite population
Figure BDA0001931952430000093
And conventional sub-populations
Figure BDA0001931952430000094
The cloned elite population is subjected to the step 8
Figure BDA0001931952430000095
And conventional sub-populations
Figure BDA0001931952430000096
The specific contents of the chaotic variation calculation are as follows:
Figure BDA0001931952430000097
r＝βe(-αT)(10)
in the formula (I), the compound is shown in the specification,
Figure BDA0001931952430000098
new variable representing the generation of the b-th variable of antibody i, r represents perturbation coefficient, yiRepresenting chaotic variables and β and α representing adjustment variables.
And step 9: for variant elite population
Figure BDA0001931952430000099
And conventional sub-populations
Figure BDA00019319524300000910
Performing clone selection calculation to obtain an antibody evolution population P of the antibody evolution populationM(k) Specifically, the method comprises the following steps:
respectively aligning the elite sub-populations by the formula (3)
Figure BDA00019319524300000911
And conventional sub-populations
Figure BDA00019319524300000912
Calculating the fitness of the individual;
if the antibody XiFitness f (X)i) Less than the corresponding new antibody generated after the treatment of step 7 and step 8
Figure BDA00019319524300000913
Is adapted to
Figure BDA00019319524300000914
Namely, it is
Figure BDA00019319524300000915
Then using the variant antibody
Figure BDA00019319524300000916
Replacing original antibody Xi(ii) a Otherwise, the original antibody population P is kept*(k) Antibody X of (1)i；
Step 10: calculation of antibody evolution population PM(k) Diversity of DijAnd by diversity DijPerforming antibody inhibition judgment;
the specific content of the step 10 is as follows:
calculation of antibody XiWith antibody XjAnd representing the diversity D of the population by said Euclidean distanceijI.e. by
Figure BDA0001931952430000101
σ is a threshold for determining whether or not antibody inhibition is performed, and is generally σ ∈ [ 0-1 ];
if D isijIf > σ, then proceed with the inferior sub-population PI(k) Update of, in particular, a rogue sub-population PI(k) Forming a new inferior sub-population P with equal scale through chaotic regeneration in a formula (4)N(k) (ii) a Otherwise, the inferior sub-population P is not performedI(k) And (4) updating.
Step 11: renewing antibody population P (k) ═ Pm(k)+PM(k)+PN(k) I.e. the updated population comprises the memory population Pm(k) Evolved population PM(k) And a new inferior subgroup PN(k)；
In this example, antibody population P*(k) The antibodies within P (k) are ranked by fitness, i.e. the size of scale P x (k) is equal to the size of P (k).
Step 12: recalculating the fitness f between the antigen and the antibody in the population after the update using equation (3)i(k) And according to the fitness fi(k) The values of (A) are arranged in a descending order, M antibodies with high fitness are selected to update the memory population, and a new memory population P is formedm(k)；
Step 13: and returning to the step 5.
Example 2:
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, which are positively opposite to 10 intrusions of enemy targets, namely W is 6, T is 10, the weapon damage probability pij(i 1,2 … 6, j 1,2 … 10) and a targeted threat system rj(j ═ 1,2 … 10) is shown in tables 1 and 2.
TABLE 1 weapon Unit destruction probability
Figure BDA0001931952430000102
Figure BDA0001931952430000111
TABLE 2 target threat coefficients
Target 1 2 3 4 5 6 7 8 9 10
Coefficient of threat 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: the maximum iteration number K is 500, the population size N is 100, the memory population size M is 0.15N, and the cloning cardinality gamma is00.3, the population classification ratio E: G: I: 0.2:0.4:0.2, the cloning factor ω: 0.25, the cloning scale γkAnd setting a chaotic behavior control parameter mu to be 4, setting the maximum iteration number H of the chaos to be 60, and setting an adjusting variable β to be 0.5 and an adjusting variable α to be 0.6.
As can be seen from fig. 2, the WTA target optimization based on the adaptive chaotic parallel clonal selection algorithm finally converges to 0.9821 in the 40 th generation, and meanwhile, the global optimization and the local optimization are considered, so that the stability is high. The optimal output results are shown in Table 3, WTA decision matrix XoptSee equation (13).
TABLE 3 assignment 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
Figure BDA0001931952430000112
From equation (13), the weapon distribution scheme can be seen, such as the first column of the matrix indicates that the weapon unit needs to attack targets # 1, 3, 4, 10; meanwhile, as the number of weapon units is smaller than that of enemy targets, the occurrence of one weapon unit can attack a plurality of local targets. Finally, the combat 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 clone selection algorithm, which solves the problem of anti-air formation Weapon Target Allocation (WTA). The algorithm combines the advantages of the chaos theory and the parallel population classification, and realizes population initialization and population updating. Wherein, a population initialization operator and a mutation operator are designed by utilizing chaotic regeneration and chaotic disturbance; performing parallel mechanism design on each sub-population by adopting a parallel population classification method, and keeping population diversity according to affinity; and (3) designing an adaptive clone multiplication operator, an antibody inhibition operator and an antibody circulation supplement operator to improve the CSA, wherein the operators can improve the global optimization capability and the local search capability. Finally, the self-adaptive chaotic parallel clone selection algorithm is verified through simulation to have better optimization performance in the aspects of search precision and convergence flexibility, and an efficient method is provided for solving the WTA in formation air defense combat.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (6)

1. A WTA target optimization method of a self-adaptive chaotic parallel clonal selection algorithm is characterized by comprising the following steps of:
step 1: constructing an optimization model of the formation air defense combat WTA, wherein an optimization model f is defined as:
Figure FDA0001931952420000011
in the formula, pij∈[0,1]Indicating the effectiveness of the weapon, W indicating the number of weapons, i 1,2 … W, T indicating the number of enemy threat objects, j 1,2 … T, λj∈[0,1]Representing the damage probability, x, of an enemy threat objectijIndicating whether the ith weapon was assigned to the jth enemy threat target, if x was assignedij1, otherwise xij＝0；
Step 2: defining antibodies, antigens and affinities by comparing an immune response mechanism with a WTA model, wherein the antigens represent an objective function and constraint conditions, and the antibodies represent all potential solutions of fire power distribution, namely all solutions of an optimization model number f; affinity provides a quantitative estimate of antibody, antigen, representing the greatest mathematical expectation of combat effectiveness, and is defined as:
Figure FDA0001931952420000012
and step 3: initializing relevant parameters including an antibody scale N, a memory population scale M (M < N), and a maximum iteration number K;
and 4, step 4: setting the current iteration number as k, and calculating the antibody X by formula (3)i(k) Fitness to antigen fi(k),i∈N；
And 5: judging a termination condition: if the current iteration number K is equal to K, outputting an optimal result Xopt；
Otherwise, let k be k +1 and pass the fitness fi(k) Calculating memory population Pm(k)；
In particular, according to the fitness fi(k) Sequencing the values of (A) to form antibody speciesGroup P*(k) And from said antibody population P*(k) M optimal individuals are selected to form a memory population Pm(k)；
Step 6: for antibody population P*(k) Performing parallel classification to generate multiple antibody sub-populations including elite sub-population PE(k) Conventional sub-population PG(k) And a rogue sub-population PI(k)；
And 7: respectively for elite sub-population PE(k) Conventional sub-population PG(k) Performing clone reproduction calculation to obtain cloned elite population
Figure FDA0001931952420000021
And conventional sub-populations
Figure FDA0001931952420000022
And 8: respectively aligning the cloned elite populations
Figure FDA0001931952420000023
And conventional sub-populations
Figure FDA0001931952420000024
Performing chaotic variation calculation to obtain the varied elite population
Figure FDA0001931952420000025
And conventional sub-populations
Figure FDA0001931952420000026
And step 9: for variant elite population
Figure FDA0001931952420000027
And conventional sub-populations
Figure FDA0001931952420000028
Performing clone selection calculation to obtain antibody of antibody evolution populationEvolved population PM(k) Specifically, the method comprises the following steps:
respectively aligning the elite sub-populations by the formula (3)
Figure FDA0001931952420000029
And conventional sub-populations
Figure FDA00019319524200000210
Calculating the fitness of the individual;
if the antibody XiFitness f (X)i) Less than the corresponding new antibody generated after the treatment of step 7 and step 8
Figure FDA00019319524200000211
Is adapted to
Figure FDA00019319524200000212
Namely:
Figure FDA00019319524200000213
then using the variant antibody
Figure FDA00019319524200000214
Replacing original antibody Xi(ii) a Otherwise, the original antibody population P is kept*(k) Antibody X of (1)i；
Step 10: calculation of antibody evolution population PM(k) Diversity of DijAnd by diversity DijPerforming antibody inhibition judgment;
step 11: renewing antibody population P (k) ═ Pm(k)+PM(k)+PN(k) I.e. the updated population comprises the memory population Pm(k) Evolved population PM(k) And a new inferior subgroup PN(k)；
Step 12: recalculating the fitness f between the antigen and the antibody in the population after the update using equation (3)i(k) And according to the fitness fi(k) The values of (A) are sorted in descending order, M adaptations are selectedThe high-degree antibody updates the memory population to form a new memory population Pm(k)；
Step 13: and returning to the step 5.
2. The WTA target optimization method according to claim 1, wherein in step 3, an initialization antibody P (0) is calculated using Logistic mapping chaotic function;
P(0)＝[XB 1(0),XB 2(0)...XB N(0)]wherein
Figure FDA0001931952420000031
B is a variable dimension;
the method comprises the following specific steps: defining y as a chaotic variable, y0∈ (0,1), and y0Not equal to 0.5, H is the maximum iteration number of chaos, mu is the control parameter of the chaos behavior, and the Logistic mapping is as follows:
Figure FDA0001931952420000032
wherein H is the current chaos iteration number, H is N,
Figure FDA0001931952420000033
represents the k-th generation of chaotic variables,
Figure FDA0001931952420000034
representing a k +1 th generation chaotic variable; the following steps are generated through the process:
Figure FDA0001931952420000035
3. the WTA target optimization method according to claim 2, wherein the antibody population in step 6 is classified in parallel according to the following specific contents:
dividing the antibody population P x (k) into an elite population P according to the ratio E: G: IE(k) Conventional sub-population PG(k) And a rogue sub-population PI(k) Three groups, wherein E + G + I is 1, PE(k)+PG(k)+PI(k)＝P*(k)＝P(k)。
4. The WTA target optimization method of claim 3, wherein the elite sub-population P is subjected to the step 7E(k) Conventional sub-population PG(k) The calculation method for carrying out clone propagation comprises the following steps:
Figure FDA0001931952420000036
Figure FDA0001931952420000037
Hk＝Hk.a/Hk.max(7)
γk＝round[N×(γ0+ω(1-Hk))](8)
wherein N represents the size of the antibody population, aibDenotes the b variable, a, of the ith antibodyjbDenotes the b variable, H, of the j antibodyijIndicates the affinity between antibody i and antibody j, HaRepresents the average affinity, H, of the antibody populationkDenotes the diversity of the antibody population of the k generation, Hk.maxRepresents the maximum affinity of the antibody population of the k generation; gamma ray0Denotes cloning base number, ω denotes cloning factor, γkRepresents the clone scale and round represents the rounding symbol.
5. The WTA target optimization method of claim 4, wherein the cloned elite sub-population in step 8 is subjected to
Figure FDA0001931952420000041
And conventional sub-populations
Figure FDA0001931952420000042
Device for calculating chaos variationThe body content is as follows:
Figure FDA0001931952420000043
r＝βe(-αT)(10)
in the formula (I), the compound is shown in the specification,
Figure FDA0001931952420000044
new variables generated by the b-th variable of the antibody i are shown, r is a disturbance coefficient, yi is a chaotic variable, and β and α are shown as adjusting variables.
6. The WTA target optimization method according to claim 5, wherein the specific content of step 10 is:
calculation of antibody XiWith antibody XjAnd representing the diversity D of the population by said Euclidean distanceijI.e. by
Figure FDA0001931952420000045
σ is a threshold for determining whether or not antibody inhibition is performed, and is generally σ ∈ [ 0-1 ];
if D isijIf the sum is more than sigma, updating the inferior sub-population PI (k), and specifically, chaotic regeneration of the inferior sub-population PI (k) through a formula (4) to form a new inferior sub-population P with equal scaleN(k) (ii) a Otherwise, the inferior sub-population P is not performedI(k) And (4) updating.
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