1 简介针对基本麻雀搜索算法在迭代后期种群多样性减小,容易陷入局部极值的问题,提出一种融合柯西变异和反向学习的改进麻雀算法(ISSA).首先,采用一种映射折叠次数无限的Sin混沌初始化种群,为全局寻优奠定基础;其次,在发现者位置更新方式中引入上一代全局最优解,提高全局搜索的充分性,同时加入自适应权重,协调局部挖掘和全局探索的能力,并加快收敛速度;然后,融合柯西变异算子和反向学习策略,在最优解位置进行扰动变异,产生新解,增强算法跃出局部空间的能力;最后,与3种基本算法和2种改进的麻雀算法进行对比,对8个基准测试函数进行仿真实验以及Wilcoxon秩和检验,评估ISSA的寻优性能,并对ISSA进行时间复杂度分析.结果表明ISSA与其余5种算法相比,收敛速度更快,精度更高,全局寻优能力得到较大提升.2 部分代码%_________________________________________________________________________________ % Salp Swarm Algorithm (SSA) source codes version 1.0 % % You can simply define your cost in a seperate file and load its handle to fobj % The initial parameters that you need are: %__________________________________________ % fobj YourCostFunction % dim number of your variables % Max_iteration maximum number of generations % SearchAgents_no number of search agents % lb[lb1,lb2,...,lbn] where lbn is the lower bound of variable n % ub[ub1,ub2,...,ubn] where ubn is the upper bound of variable n % If all the variables have equal lower bound you can just % define lb and ub as two single number numbers % To run SSA: [Best_score,Best_pos,SSA_cg_curve]SSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj) %__________________________________________ clear all clc SearchAgents_no30; % Number of search agents Function_nameF5; % Name of the test function that can be from F1 to F23 ( Max_iteration100; % Maximum numbef of iterations % Load details of the selected benchmark function [lb,ub,dim,fobj]Get_Functions_details(Function_name); [Best_score,Best_pos,SSA_cg_curve]SSA(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([( x_1 , x_2 )]) %Draw objective space subplot(1,2,2); semilogy(SSA_cg_curve,Color,r) title(Objective space) xlabel(Iteration); ylabel(Best score obtained so far); axis tight grid on box on legend(SSA) display([The best solution obtained by SSA is \m , num2str(Best_pos)]); display([The best optimal value of the objective funciton found by SSA is \n , num2str(Best_score)]); img gcf; %获取当前画图的句柄 print(img, -dpng, -r600, ./img.png) %即可得到对应格式和期望dpi的图像3 仿真结果4 参考文献[1]毛清华, and 张强. 融合柯西变异和反向学习的改进麻雀算法. 计算机科学与探索 15.6(2021):10.博主简介擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真有科研问题可私信交流。部分理论引用网络文献若有侵权联系博主删除。
【麻雀算法】基于融合柯西变异和反向学习改进麻雀算法求解单目标优化问题matlab代码
1 简介针对基本麻雀搜索算法在迭代后期种群多样性减小,容易陷入局部极值的问题,提出一种融合柯西变异和反向学习的改进麻雀算法(ISSA).首先,采用一种映射折叠次数无限的Sin混沌初始化种群,为全局寻优奠定基础;其次,在发现者位置更新方式中引入上一代全局最优解,提高全局搜索的充分性,同时加入自适应权重,协调局部挖掘和全局探索的能力,并加快收敛速度;然后,融合柯西变异算子和反向学习策略,在最优解位置进行扰动变异,产生新解,增强算法跃出局部空间的能力;最后,与3种基本算法和2种改进的麻雀算法进行对比,对8个基准测试函数进行仿真实验以及Wilcoxon秩和检验,评估ISSA的寻优性能,并对ISSA进行时间复杂度分析.结果表明ISSA与其余5种算法相比,收敛速度更快,精度更高,全局寻优能力得到较大提升.2 部分代码%_________________________________________________________________________________ % Salp Swarm Algorithm (SSA) source codes version 1.0 % % You can simply define your cost in a seperate file and load its handle to fobj % The initial parameters that you need are: %__________________________________________ % fobj YourCostFunction % dim number of your variables % Max_iteration maximum number of generations % SearchAgents_no number of search agents % lb[lb1,lb2,...,lbn] where lbn is the lower bound of variable n % ub[ub1,ub2,...,ubn] where ubn is the upper bound of variable n % If all the variables have equal lower bound you can just % define lb and ub as two single number numbers % To run SSA: [Best_score,Best_pos,SSA_cg_curve]SSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj) %__________________________________________ clear all clc SearchAgents_no30; % Number of search agents Function_nameF5; % Name of the test function that can be from F1 to F23 ( Max_iteration100; % Maximum numbef of iterations % Load details of the selected benchmark function [lb,ub,dim,fobj]Get_Functions_details(Function_name); [Best_score,Best_pos,SSA_cg_curve]SSA(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([( x_1 , x_2 )]) %Draw objective space subplot(1,2,2); semilogy(SSA_cg_curve,Color,r) title(Objective space) xlabel(Iteration); ylabel(Best score obtained so far); axis tight grid on box on legend(SSA) display([The best solution obtained by SSA is \m , num2str(Best_pos)]); display([The best optimal value of the objective funciton found by SSA is \n , num2str(Best_score)]); img gcf; %获取当前画图的句柄 print(img, -dpng, -r600, ./img.png) %即可得到对应格式和期望dpi的图像3 仿真结果4 参考文献[1]毛清华, and 张强. 融合柯西变异和反向学习的改进麻雀算法. 计算机科学与探索 15.6(2021):10.博主简介擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真有科研问题可私信交流。部分理论引用网络文献若有侵权联系博主删除。