CN114510876A - Multi-platform weapon target allocation method based on symbiotic search biophysical optimization - Google Patents
Multi-platform weapon target allocation method based on symbiotic search biophysical optimization Download PDFInfo
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Abstract
The method for allocating the multi-platform weapon targets based on symbiotic search biogeography optimization is provided: constructing a multi-platform WTA model based on a fuzzy expected effect; setting the population scale, and randomly generating an initial solution; encoding the population by adopting a matrix encoding strategy based on integers; calculating objective function values corresponding to each solution, sequencing the objective function values from large to small, and reserving the solutions corresponding to the first q larger objective function values; optimizing the initial population to reduce the randomness of the initial population; improving a migration operator, and performing migration optimization operation; proposing a co-inhabiting mutation operator, and performing mutation optimization operation; recalculating the objective function value corresponding to each solution, sequencing the objective function values from large to small, and replacing the solutions corresponding to the former q large objective function values; judging whether the maximum iteration times is reached, and if so, outputting a result; otherwise, returning to the fifth step. The method is suitable for the requirements of combat assistant decision-making on solving precision and timeliness in an uncertain environment, and can provide method support for the development of a command control system.
Description
Technical Field
The invention relates to the technical field of uncertain optimization and command control, in particular to a multi-platform weapon target distribution method based on symbiotic search biophysical optimization in an uncertain environment.
Background
Under the condition of informatization combat, the combat environment is increasingly complex, and Weapon Target Assignment (WTA) is taken as a command decision core problem and has become a research hotspot at home and abroad. Because enemy interference attack means are increasingly diversified, uncertain factors of a battlefield are continuously increased, how to reasonably utilize a multi-platform weapon unit to attack an enemy attack target under an uncertain condition so as to achieve an optimal combat effect is a problem to be solved urgently by the WTA at present.
In terms of model construction of the multi-platform WTA problem, the classical WTA model aims at maximizing the expected damage effect of weapons, and in addition, cost-based WTA, asset-based WTA, and multi-objective multi-stage WTA variants have been developed. At present, the research results of a multi-platform WTA model under an uncertain environment are less, however, as the battlefield environment is increasingly complex, shooting advantages and target values containing main and objective factors have obvious uncertain characteristics.
In the aspect of solving the multi-platform WTA model, as the heuristic algorithm shows good adaptability, students at home and abroad propose to solve the multi-platform WTA problem by using heuristic algorithms such as a genetic algorithm, a swarm algorithm, a particle swarm algorithm and the like. However, when large-scale weapon-target distribution is performed, the existing algorithms have the problems of high algorithm complexity, low model solving speed and low solving precision to a certain extent. Therefore, the multi-platform WTA solution with high requirement on timeliness still needs to be studied deeply.
The Biogeography-Based Optimization (BBO) algorithm was first proposed by Simon, Biogeography-Based Optimization (IEEE transactions on evaluation, 2008, 12 (6): 702-713.) in 2008 by American scholars. Simon proves that compared with other optimization algorithms, the optimization algorithm has good mining capability and global search capability on candidate solutions, and therefore, the optimization algorithm is concerned by a plurality of scholars at home and abroad. However, the basic BBO algorithm also has the following significant problems: firstly, directly copying a better solution in a migration process, and ensuring that the diversity of a population is not high; secondly, the candidate solutions are attracted by individual solutions with high fitness values, and a plurality of solutions are close or super individual phenomena appear in the later iteration stage; and thirdly, the algorithm is premature and converged. So the scholars experts also continuously improve the BBO algorithm to different degrees.
The symbiont Search (SOS) algorithm is Min Yuan Cheng "Symbiotic Organisms Search: a new heuristic algorithm proposed in 2014 by new statistical optimization algorithm (Computers & Structures, 2014, 139 (1): 98-112.). The algorithm simulates the symbiotic interaction strategy adopted by living and breeding of organisms in the ecosystem, and has strong robustness and optimizing capability. Both the SOS algorithm and the BBO algorithm are generated by the inspiration of an evolution theory, and theoretically have homology, and both algorithms do not need specific parameters; and the BBO algorithm has the advantages of information sharing among different habitats, and the BBO algorithm can be better promoted to play the characteristics by the characteristic of living body sharing advantage co-evolution in the SOS algorithm.
Disclosure of Invention
The invention provides a symbiotic search biophysical optimization-based multi-platform weapon target allocation method, which specifically comprises the following steps:
step 1, constructing multi-platform WTA model based on fuzzy expected effect
N weapon platforms are arranged, wherein i is 1, 2, and n is the ordinal number of the weapon platform; m incoming targets, j being 1, 2.. said, m, j being the ordinal number of the incoming target; the number of weapons per weapons platform is ci，xijIs as followsThe number of weapons assigned to the jth incoming target by the i weapons platforms; the distribution scheme of different weapon platforms to different targets is marked as X;
depicting the shooting profitability and the target value as fuzzy variables and expressing the fuzzy variables by triangular fuzzy variables;
the method comprises the steps of representing the benefit degree of the ith weapon platform on fuzzy shooting of the jth attack target;
representing the fuzzy target value of the jth incoming target; constructing a multi-platform WTA model based on a fuzzy expected effect;
wherein Z is an objective function value; e is the expected value of the fuzzy variable; theta is a weight parameter given by an expert;
the constraints in the model have the following meanings:
(1) each incoming target is at least allocated with one weapon;
(2) the firepower distributed by each weapon platform to the attacking target cannot exceed the weapon quantity of the weapon platform;
step 2 population size popsize was set and initial solution x was randomly generatedij；
popsize、xijAre all positive integers;
step 3, encoding the population by adopting an integer-based matrix encoding strategy;
encoding the population by adopting an integer-based matrix encoding strategy, as shown in formula (2);
wherein X is a weapon target assignment scheme; x is the number ofijThe number of weapons assigned to the jth target for the ith weapons platform is takenThe value range is (0, c)i)；
Step 4 calculates each solution x according to equation (1)ijThe corresponding objective function values Z are sorted from large to small, and the solutions x corresponding to the first q larger objective function values Z are reservedij；
q is determined according to specific conditions;
step5, optimizing the initial population according to mutual benefit operation in a symbiont search algorithm, and reducing the randomness of the initial population;
wherein, XaAnd XbIs a randomly selected habitat; a and b are the ordinal number of the habitat, a 1, 2., popsize, b 1, 2., popsize; popsize is the population number; xbestFor the current optimal solution xij(ii) a MV is a mutual interest vector between two habitats; BF (BF) generator1、BF2E {1, 2} is a profit factor;
and
a new habitat is generated through mutual profit operation and co-evolution; rand is a random number;
step 6, improving a migration operator, and performing migration optimization operation, wherein the migration optimization operation comprises the following steps:
step 6.1 migration operator based on dynamic selection;
step 6.2, migration operators based on mutual benefit evolution;
step 6.3, dynamically and adaptively optimizing a migration operator by cosine;
step 7, proposing a co-inhabiting mutation operator, and performing mutation optimization operation;
introducing a co-habitation idea in the SOS algorithm, and randomly selecting objects from the first half better solution for interaction aiming at the second half worse solution, thereby enhancing the self adaptability;
in the formula, XhRandomly selecting one habitat from the habitats of the popsize/2 after the ordering of the objective function values; xqRandomly selecting one habitat from the habitats of the popsize/2 in the sequence of the objective function values; h and q are the ordinal number of the habitat, h ═ popsize/2., popsize, q ═ 0., popsize/2; xbestThe optimal habitat in the current iteration process is obtained;
is a habitat newly generated after the current mutation operation; the length function represents the length of the vector; round function means rounding the value length (popsize/2) × rand;
step 8, recalculating the objective function value Z corresponding to each solution according to the formula (1), sorting the objective function values from large to small, and replacing the solutions corresponding to the former q larger objective function values;
step 9, judging whether the maximum iteration number G is reachedmaxIf yes, outputting an optimal solution; otherwise, go back to Step 5.
In one embodiment of the present invention, Step 6 is specifically as follows:
step 6.1 migration operator based on dynamic selection
Setting a habitat dynamic selection strategy: in different stages, different selection pressures are specified when carrying out the migration-in and migration-out operations; the selection pressure is properly reduced in the early stage, so that the habitat with the smaller objective function value Z can participate in subsequent optimization to keep the diversity of the population; properly increasing selection pressure in the later stage to enable the population to be quickly converged, so that the optimal solution is quickly approached; the following selection probabilities are presented;
wherein, PaProbability of being selected for the a-th habitat for migration; mu.saThe migration rate of the a-th habitat; mu.sbThe migration rate of any b-th habitat; mu.sa、μbU in the upper right corner represents the u power; u is a selection pressure factor; pd (photo data)maxSelecting a variation initial value of the pressure factor; pd (photo data)minSelecting a final value of the variation of the pressure factor; g is the current iteration number; gmaxIs the maximum iteration number;
step 6.2 migration operator based on mutual interest evolution
For an l-dimensional habitat, l ═ 1, 2.., d, d is the dimension of one habitat; carrying out mutual beneficial evolution on the selected immigration places and the selected emigration places, absorbing mutual beneficial factors and carrying out co-evolution through mutual learning and feedback;
wherein, Xa_newAnd Xb_newNew habitat is generated after the mutual benefit evolution migration operator; a _ new and b _ new are the ordinal numbers of the habitat, a _ new ═ 1,2，...，popsize，b_new＝1，2，...，popsize；
reflection of gain factor to BF1The relationship between the current habitat and the optimal habitat;
obtaining BF reflecting a profit factor2The relationship between the current habitat and the optimal habitat; the ceil function represents rounding towards the positive infinity; the rand function represents random real numbers between random generations (0, 1);
step 6.3 cosine dynamic self-adaptive optimization migration operator
Fusing a reciprocal evolution migration operator and a dynamic selection migration operator, wherein the reciprocal evolution migration operator is mainly used in the early stage, the dynamic selection migration operator is mainly used in the later stage, and a cosine dynamic self-adaptive optimization migration operator is provided, as shown in a formula (11);
wherein beta is a cosine dynamic adaptive optimization migration operator.
The invention takes a multi-platform WTA as a background, and constructs a multi-platform WTA model based on fuzzy expected effect on the basis of considering the uncertainty of shooting profitability and target value. In order to effectively solve the model, draw the common characteristics and complementary advantages of the BBO algorithm and the SOS algorithm, improve the migration and mutation operations in the BBO algorithm, and provide a Symbiotic search-Based (SBBO) algorithm.
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FIG. 1 shows a flow chart of a symbiotic search biophysical optimization-based multi-platform weapon target assignment method of the present invention.
Detailed Description
In order to make the objects, technical lines and advantages of the present invention more clear, the present invention will be further described in detail with reference to the accompanying drawings and embodiments.
The invention provides a symbiotic search biophysical optimization-based multi-platform weapon target allocation method, which specifically comprises the following steps:
step 1, constructing multi-platform WTA model based on fuzzy expected effect
Aiming at the uncertain combat environment, the shooting profitability and the target value are comprehensively considered, and a multi-platform WTA model based on the fuzzy expected effect is constructed. N weapon platforms (i 1, 2.. and n, i are ordinal numbers of the weapon platforms), m attacking targets (j 1, 2.. and m, j are ordinal numbers of the attacking targets), and the number of weapons of each weapon platform is ci，xijThe number of weapons assigned to the jth target for the ith weapons platform; the distribution scheme of different weapon platforms to different targets is denoted X.
In the multi-platform WTA distribution, the target value not only comprises the physical damage effect, but also comprises the risk measurement of damage cost, so the multi-platform WTA distribution has uncertainty characteristics; the shooting benefit is influenced by multiple factors such as target type, target flight speed, F flight time, expert evaluation and the like, contains subjective factors and objective factors, and has obvious uncertainty characteristics in a complex confrontation environment. The invention thus characterizes shot favorability and target value as fuzzy variables and is represented by triangular fuzzy variables.
The method comprises the steps of representing the benefit degree of the ith weapon platform on fuzzy shooting of the jth attack target;
representing the fuzzy target value of the jth incoming target. Because the operation effect and the operation cost are not completely contradictory, in order to avoid blind consumption and destruction, the invention constructs a multi-platform WTA model based on the fuzzy expected effect.
Wherein Z is an objective function value; e is the expected value of the fuzzy variable; theta is a weight parameter given by the expert.
The constraints in the model have the following meanings:
(2) each incoming target is at least allocated with one weapon;
(2) the firepower distributed by each weapon platform to the attacking target cannot exceed the weapon quantity of the weapon platform.
Step 2 population size popsize was set and initial solution x was randomly generatedij。popsize、xijAre all positive integers.
And Step 3, encoding the population by adopting an integer-based matrix encoding strategy.
Because the BBO algorithm adopts integer coding, in order to facilitate solving operation, the invention adopts a matrix coding strategy based on integers to code the population, as shown in formula (2).
Wherein X is a weapon target assignment scheme; x is the number ofijThe weapon quantity allocated to the jth target for the ith weapon platform is in the value range of (0, c)i)。
Step 4 calculates each solution x according to equation (1)ijCorresponding value of the objective function Z (in equation 1, x)ijIs an independent variable, Z is a dependent variable; in the planning problem, xijThat is, Z is the objective function; different solutions correspond to different objective function values; in the formula (1), x is found to maximize Zij) Sorting Z from large to small, and reserving the solutions x corresponding to the first q larger objective function values ZijAnd q is determined according to specific conditions.
Step5, optimizing the initial population according to mutual benefit operation in a symbiont search algorithm, and reducing the randomness of the initial population.
Wherein, XaAnd XbFor randomly chosen habitats (one habitat represents one solution x)ij) (ii) a a and b are the ordinal numbers of the habitat (a 1, 2., popsize, b 1, 2., popsize); popsize is the population number; xbestFor the current optimal solution xij(ii) a MV is a mutual interest vector between two habitats; BF1、BF2E {1, 2} is a profit factor;
and
a new habitat is generated through mutual profit operation and co-evolution; rand is a random number.
Step 6, improving a migration operator, and performing migration optimization operation, which is specifically as follows.
Step 6.1 migration operator based on dynamic selection
In the selection of the original BBO algorithm on the migrated habitat, a roulette mode is mainly adopted, the selection probabilities are random, and the corresponding change cannot be carried out according to different iteration stages, so that the problems of low population diversity and premature local convergence of the algorithm are easily caused. Therefore, the invention sets a habitat dynamic selection strategy: different selection pressures are specified for carrying out the migration-in and migration-out operations in different stages. The selection pressure is properly reduced in the early stage, so that the habitat with the smaller objective function value Z can participate in subsequent optimization to keep the diversity of the population; and the selection pressure is properly increased at the later stage, so that the population can be quickly converged, and the optimal solution is quickly approached. The present invention proposes the following selection probabilities.
Wherein, PaProbability of being selected for the a-th habitat for migration; mu.saThe migration rate of the a-th habitat; mu.sbFor any b-th habitat (summation of denominators involves summing up μaThe sum of (1); mu.sa、μbU in the upper right corner represents the u power; u is a selection pressure factor; pd (photo data)maxSelecting a variation initial value of the pressure factor; pd (photo data)minSelecting a final value of the change in the pressure factor; g is the current iteration number; gmaxIs the maximum number of iterations.
Step 6.2 migration operator based on mutual interest evolution
For the l-th habitat (l ═ 1, 2.., d), d is the dimension of one habitat (also of all habitats). And carrying out mutual beneficial evolution on the selected immigration places and the selected emigration places, absorbing mutual beneficial factors and carrying out co-evolution through mutual learning and feedback.
Wherein, Xa_newAnd Xb_newNew habitat is generated after the mutual benefit evolution migration operator; a _ new and b _ new are ordinals of the habitat (a _ new ═ 1, 2., popsize, b _ new ═ 1, 2., popsize);
reflection of gain factor to BF1The relationship between the current habitat and the optimal habitat;
reflection of gain factor to BF2The relationship between the current habitat and the optimal habitat; the ceil function represents rounding towards the positive infinity; the rand function represents random real numbers between random generations (0, 1). The lower corner mark on the right side X of the equation of formula (8) and formula (9) is used to determine which habitat to select, i.e. the lower corner mark represents the ordinal number of the habitat. The co-evolution process avoids the defect of direct migration and duplication of a single habitat, enables two habitats to be co-evolved, is beneficial to enhancing the diversity of populations and accelerates the process of searching for the optimal solution.
Step 6.3 cosine dynamic self-adaptive optimization migration operator
In order to further improve the searching capability of the algorithm in different stages, the invention fuses the reciprocal evolution migration operator and the dynamic selection migration operator, the reciprocal evolution migration operator is mainly used in the early stage, the dynamic selection migration operator is mainly used in the later stage, and the cosine dynamic self-adaptive optimization migration operator is provided, as shown in formula (11).
Wherein beta is a cosine dynamic self-adaptive optimization migration operator.
Step 7, proposing a co-inhabiting mutation operator for mutation optimization operation.
The mutation operation in the original BBO algorithm mainly mutates the second half of the poor solution according to the mutation probability, but the mutation is random and has no directionality, and may mutate to a good direction and also mutate to a bad direction. While this process is less likely to provide an excellent solution. In order to reduce the randomness of variation, the method introduces a co-habitation idea in the SOS algorithm, namely, aiming at poor solutions of the latter half, objects are randomly selected from better solutions of the former half for interaction, so that the self adaptability degree is enhanced.
In the formula, XhRandomly selecting one habitat from the habitats of the popsize/2 after the ordering of the objective function values; xqRandomly selecting one habitat from the habitats of the popsize/2 in the sequence of the objective function values; h and q are the ordinal number of the habitat (h ═ popsize/2., popsize, q ═ 0., popsize/2); xbestThe optimal habitat in the current iteration process is obtained;
is a habitat newly generated after the current mutation operation; the length function represents the length of the vector (e.g., representing the total number of habitats divided by the length of 2, so that length (popsize/2) ═ popsize/2); the round function indicates that the numerical value length (posize/2) rand is rounded, and the value range of length (posize/2) rand is (0, 50)).
Step 8, recalculating the objective function value Z corresponding to each solution according to the formula (1), sorting the objective function values from large to small, and replacing the solutions corresponding to the former q larger objective function values.
Step 9, judging whether the maximum iteration number G is reachedmaxIf yes, the optimal solution is output (the solutions can be all expressed by x)ijThe optimal solution is x that maximizes the value of the objective functionij) (ii) a Otherwise, go back to Step 5.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
To verify the feasibility and effectiveness of the present invention, the following examples are given: suppose that 4 weapons platforms in a single maneuver cooperate to hit 10 incoming targets, each weapons platform having 25 weapons. The triangular fuzzy variables of shot benefit versus target value are shown in table 1. The parameters are set as follows: popsize 100, θ 0.5, pdmax＝0.9；pdmin＝0.1；Gmax＝200；d＝30；q＝2。
TABLE 1 shooting benefit
TABLE 2 target values
Table 3 shows the final weapon target assignment scheme, and table 4 shows the optimal values, average values and average operation times of the algorithm of the present invention obtained after the algorithm is independently operated for 30 times under different population sizes and different iteration times. As can be seen from table 4, the optimum and mean values increase optimally with increasing population size and number of iterations. Therefore, in practical application, under the condition of meeting timeliness, the algorithm population size and the iteration number can be properly increased.
TABLE 3 optimal weapon target assignment scheme
TABLE 4 Algorithm Performance for different population sizes, iteration times
It should be understood that the above-described specific embodiments are merely illustrative of the present invention and are not intended to limit the present invention.
Claims (2)
1. The method for allocating the targets of the multi-platform weapons based on symbiotic search and biophysical optimization is characterized by specifically comprising the following steps:
step 1, constructing multi-platform WTA model based on fuzzy expected effect
N weapon platforms are arranged, wherein i is 1, 2, and n is the ordinal number of the weapon platform; m incoming targets, j being 1, 2.. said, m, j being the ordinal number of the incoming target; the number of weapons per weapons platform is ci，xijThe number of weapons assigned to the jth target for the ith weapons platform; the distribution scheme of different weapon platforms to different targets is marked as X;
the shooting profitability and the target value are characterized as fuzzy variables and are represented by triangular fuzzy variables;
the method comprises the steps of representing the benefit degree of the ith weapon platform on fuzzy shooting of the jth attack target;
representing the fuzzy target value of the jth incoming target; constructing a multi-platform WTA model based on a fuzzy expected effect;
wherein Z is an objective function value; e is the expected value of the fuzzy variable; theta is a weight parameter given by an expert;
the constraints in the model have the following meanings:
(3) each incoming target is at least allocated with one weapon;
(2) the firepower distributed by each weapon platform to the attacking target cannot exceed the weapon quantity of the weapon platform;
step 2 population size popsize was set and initial solution x was randomly generatedij；
popsize、xijAre all positive integers;
step 3, encoding the population by adopting an integer-based matrix encoding strategy;
encoding the population by adopting an integer-based matrix encoding strategy, as shown in formula (2);
wherein X is a weapon target assignment scheme; x is a radical of a fluorine atomijThe weapon quantity allocated to the jth target for the ith weapon platform is in the value range of (0, c)i)；
Step 4 calculates each solution x according to equation (1)ijThe corresponding objective function values Z are sorted from large to small, and the solutions x corresponding to the first q larger objective function values Z are reservedij；
q is determined according to specific conditions;
step5, optimizing the initial population according to mutual benefit operation in a symbiont search algorithm, and reducing the randomness of the initial population;
wherein, XaAnd XbIs a randomly selected habitat; a and b are the ordinal number of the habitat, a 1, 2., popsize, b 1, 2., popsize; popsize is the population number; xbestFor the current optimal solution xij(ii) a MV is a mutual interest vector between two habitats; BF (BF) generator1、BF2E {1, 2} is a profit factor;
and
a new habitat is generated through mutual profit operation and co-evolution; rand is a random number;
step 6, improving a migration operator, and performing migration optimization operation, wherein the migration optimization operation comprises the following steps:
step 6.1 migration operator based on dynamic selection;
step 6.2, migration operators based on mutual benefit evolution;
step 6.3, dynamically and adaptively optimizing a migration operator by cosine;
step 7, proposing a co-inhabiting mutation operator, and performing mutation optimization operation;
introducing a co-habitation idea in the SOS algorithm, and randomly selecting objects from the first half better solution for interaction aiming at the second half worse solution, thereby enhancing the self adaptability;
in the formula, XhRandomly selecting one habitat from the habitats of the popsize/2 after the ordering of the objective function values; xqRandomly selecting one habitat from the habitats of the popsize/2 in the sequence of the objective function values; h and q are the ordinal number of the habitat, h ═ popsize/2., popsize, q ═ 0., popsize/2; xbestThe optimal habitat in the current iteration process is set;
is a habitat newly generated after the current mutation operation; the length function represents the length of the vector; round function means rounding the value length (popsize/2) × rand;
step 8, recalculating the objective function value Z corresponding to each solution according to the formula (1), sorting the objective function values from large to small, and replacing the solutions corresponding to the former q larger objective function values;
step 9, judging whether the maximum iteration number G is reachedmaxIf yes, outputting an optimal solution; otherwise, go back to Step 5.
2. The symbiotic search biogeography-based optimized multi-platform weapon target assignment method of claim 1, wherein Step 6 is specifically as follows:
step 6.1 migration operator based on dynamic selection
Setting a habitat dynamic selection strategy: in different stages, different selection pressures are specified when carrying out the migration-in and migration-out operations; the selection pressure is properly reduced in the early stage, so that the habitat with the smaller objective function value Z can participate in subsequent optimization to keep the diversity of the population; properly increasing selection pressure in the later stage to enable the population to be quickly converged, so that the optimal solution is quickly approached; the following selection probabilities are presented;
wherein, PaProbability of being selected for the a-th habitat for migration; mu.saThe migration rate of the a-th habitat; mu.sbThe migration rate of any b-th habitat; mu.sa、μbU in the upper right corner represents the u power; u is a selection pressure factor; pd (photo data)maxSelecting a variation initial value of the pressure factor; pd (photo data)minSelecting a final value of the change in the pressure factor; g is the current iteration number; gmaxIs the maximum iteration number;
step 6.2 migration operator based on mutual interest evolution
For an l-dimensional habitat, l ═ 1, 2.., d, d is the dimension of one habitat; carrying out mutual beneficial evolution on the selected immigration places and the selected emigration places, absorbing mutual beneficial factors and carrying out co-evolution through mutual learning and feedback;
wherein Xa_newAnd Xb_newNew habitat is generated after the mutual benefit evolution migration operator; a _ new and b _ new are the ordinal numbers of the habitat, a _ new 1, 2., popsize, b _ new 1, 2., popsize;
reflection of gain factor to BF1The relationship between the current habitat and the optimal habitat;
reflection of gain factor to BF2The relationship between the current habitat and the optimal habitat; the ceil function represents rounding towards the positive infinity; the rand function represents random real numbers between random generations (0, 1);
step 6.3 cosine dynamic self-adaptive optimization migration operator
Fusing a reciprocal evolution migration operator and a dynamic selection migration operator, wherein the reciprocal evolution migration operator is mainly used in the early stage, the dynamic selection migration operator is mainly used in the later stage, and a cosine dynamic self-adaptive optimization migration operator is provided, as shown in a formula (11);
wherein beta is a cosine dynamic adaptive optimization migration operator.
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