【优化求解】基于无序灰狼算法求解多目标问题matlab代码-北京尧图网络

1 简介Mirjalili 等人提出了一种新的群体智能算法———灰狼优化算法(GWO)并通过多个基准测试函数进行测试从结果上验证了该算法的可行性通过对比GWO 算法已被证明在算法对函数求解精度和稳定性上要明显优于 PSO、DE 和 GSA 算法。生物在自然界严酷环境下即使并不具有人类的高智能但在相同的目标即食物的激励下通过不断地适应与集体合作都表现出了令人惊叹的群体智能。文献6基于狼群严密的组织系统及其精妙的协作捕猎方式提出了一种新的群体智能算法———灰狼优化算法。2 部分代码%% Non Sorted Grey Wolf Algorithm (NSGWO)clcclear allD 30; % Number of decision variablesM 2; % Number of objective functionsKMD;LB ones(1, D).*0; % LB - A vector of decimal values which indicate the minimum value for each decision variable.UB ones(1, D).*1; % UB - Vector of maximum possible values for decision variables.Max_iteration 100; % Set the maximum number of generation (GEN)SearchAgents_no 100; % Set the population size (Search Agent)ishow 10;%% Initialize the population% Population is initialized with random values which are within the% specified range. Each chromosome consists of the decision variables. Also% the value of the objective functions, rank and crowding distance% information is also added to the chromosome vector but only the elements% of the vector which has the decision variables are operated upon to% perform the genetic operations like corssover and mutation.chromosome initialize_variables(SearchAgents_no, M, D, LB, UB);%% Sort the initialized population% Sort the population using non-domination-sort. This returns two columns% for each individual which are the rank and the crowding distance% corresponding to their position in the front they belong. At this stage% the rank and the crowding distance for each chromosome is added to the% chromosome vector for easy of computation.intermediate_chromosome non_domination_sort_mod(chromosome, M, D);%% Perform Selection% Once the intermediate population is sorted only the best solution is% selected based on it rank and crowding distance. Each front is filled in% ascending order until the addition of population size is reached. The% last front is included in the population based on the individuals with% least crowding distance% Select NP fittest solutions using non dominated and crowding distance% sorting and store in populationPopulation replace_chromosome(intermediate_chromosome, M,D,SearchAgents_no);%% Start the evolution process% The following are performed in each generation% * Select the parents which are fit for reproduction% * Perfrom crossover and Mutation operator on the selected parents% * Perform Selection from the parents and the offsprings% * Replace the unfit individuals with the fit individuals to maintain a% constant population size.Pareto NSGWO(D,M,LB,UB,Population,SearchAgents_no,Max_iteration,ishow);save Pareto.txt Pareto -ascii; % save data for future use%% Plot dataif M 2plot_data2(M,D,Pareto)elseif M 3plot_data_TCQ(M,D,Pareto);end3 仿真结果4 参考文献[1]孟安波, and 林艺城. 一种基于多目标的改进灰狼优化算法., CN107067121A. 2017.

【优化求解】基于无序灰狼算法求解多目标问题matlab代码

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