灰狼優(yōu)化算法(Grey Wolf Optimization,GWO)是新型啟元優(yōu)化算法,相比于其他群體智能優(yōu)化算法,該算法同樣存在收斂速度較慢、不穩(wěn)定、易陷入局部最優(yōu)等問題。針對上述問題,根據(jù)GWO算法的結(jié)構(gòu)特點,提出了一種自適應調(diào)整策略的混沌灰狼優(yōu)化算法(Chaotic Local Search GWO),利用自適應調(diào)整策略來提高GWO算法的收斂速度,通過混沌局部搜索策略增加種群的多樣性,使搜索過程避免陷入局部最優(yōu)。最后利用6個測試函數(shù)對算法進行仿真驗證,并結(jié)合其他4種算法進行了橫向比較。實驗結(jié)果證明,所提出的改進算法在收斂速度、精度以及穩(wěn)定性方面具有明顯的優(yōu)勢。

clear all clc SearchAgents_no=30; % Number of search agents Max_iteration=500; % Maximum numbef of iterations Function_name='F12'; % Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper) %for func_num=18:23; ?% initial_flag=0; ? %Function_name=strcat('F',num2str(func_num)); % time=cputime; % Load details of the selected benchmark function [lb,ub,dim,fobj]=Get_Functions_details(Function_name); [Best_score,Best_pos,GWO_cg_curve]=GWO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); [Best_score,Best_pos,IGWO_cg_curve]=IGWO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); PSO_cg_curve=PSO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); % run PSO to compare to results [Best_score,Best_pos,CGWO_cg_curve]=CGWO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); [Best_score,Best_pos,FGWO_cg_curve]=FGWO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); [Best_score,Best_pos,SCA_cg_curve]=SCA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); figure('Position',[500 500 660 290]) %Draw search space subplot(1,2,1); func_plot(Function_name); title('Parameter space') xlabel('x_1'); ylabel('x_2'); zlabel([Function_name,'( x_1 , x_2 )']) %Draw objective space subplot(1,2,2); semilogy(GWO_cg_curve,'Color','r') hold on semilogy(PSO_cg_curve,'Color','b') title('Objective space') xlabel('Iteration'); ylabel('Best score obtained so far'); hold on semilogy(IGWO_cg_curve,'Color','g') title('Objective space') xlabel('Iteration'); ylabel('Best score obtained so far'); hold on semilogy(CGWO_cg_curve,'Color','c') title('Objective space') xlabel('Iteration'); ylabel('Best score obtained so far'); hold on semilogy(FGWO_cg_curve,'Color','m') title('Objective space') xlabel('Iteration'); ylabel('Best score obtained so far'); hold on semilogy(SCA_cg_curve,'Color','k') title('Objective space') xlabel('Iteration'); ylabel('Best score obtained so far'); CGWO_ave=mean2(CGWO_cg_curve); CGWO_std=std2(CGWO_cg_curve); GWO_ave=mean2(GWO_cg_curve); GWO_std=std2(GWO_cg_curve); FGWO_ave=mean2(FGWO_cg_curve); FGWO_std=std2(FGWO_cg_curve); IGWO_ave=mean2(IGWO_cg_curve); IGWO_std=std2(IGWO_cg_curve); PSO_ave=mean2(PSO_cg_curve); PSO_std=std2(PSO_cg_curve); SCA_ave=mean2(SCA_cg_curve); SCA_std=std2(SCA_cg_curve); axis tight grid on box on legend('GWO','PSO','IGWO','CGWO','FGWO','SCA') display(['The best solution obtained by CGWO is : ', num2str(Best_pos)]); display(['The best optimal value of the objective funciton found by CGWO is : ', num2str(Best_score)]);

博主擅長優(yōu)化求解、神經(jīng)網(wǎng)絡預測、信號處理、元胞自動機、圖像處理等多種領域的Matlab仿真，QQ1575304183