1 简介传统蝗虫优化算法在处理优化问题时依然存在收敛速度慢,易陷入局部最优的不足.为此,提出了融合混沌映射和柯西变异机制的非线性蝗虫优化算法CCGOA.通过融合混沌Tent映射与反向学习机制,对种群初始化,在确保初始种群质量较优前提下,使种群尽可能均匀分布于搜索空间;利用余弦函数设计非线性自适应系数更新机制,更好均衡个体全局搜索与局部开发能力;引入柯西变异对当前最优个体进行变异扰动,避免算法陷入局部最优.通过基准函数寻优测试,证实提出的算法可以有效提升寻优精度和收敛速度.设计了特征选择算法CCGOA-FS并应用于特征选择问题求解.通过若干数据集测试,证实该算法可以有效进行最优特征子集选取,提升数据分类准确率.2 部分代码%_________________________________________________________________________% % Grasshopper Optimization Algorithm (GOA) source codes demo V1.0 % % % %_________________________________________________________________________% % The Grasshopper Optimization Algorithm function [TargetFitness,TargetPosition,Convergence_curve,Trajectories,fitness_history, position_history]GOA(N, Max_iter, lb,ub, dim, fobj) tic disp(GOA is now estimating the global optimum for your problem....) flag0; if size(ub,1)1 ubones(dim,1)*ub; lbones(dim,1)*lb; end if (rem(dim,2)~0) % this algorithm should be run with a even number of variables. This line is to handle odd number of variables dim dim1; ub [ub; 100]; lb [lb; -100]; flag1; end %Initialize the population of grasshoppers GrassHopperPositionsinitialization(N,dim,ub,lb); GrassHopperFitness zeros(1,N); fitness_historyzeros(N,Max_iter); position_historyzeros(N,Max_iter,dim); Convergence_curvezeros(1,Max_iter); Trajectorieszeros(N,Max_iter); cMax1; cMin0.00004; %Calculate the fitness of initial grasshoppers for i1:size(GrassHopperPositions,1) if flag 1 GrassHopperFitness(1,i)fobj(GrassHopperPositions(i,1:end-1)); else GrassHopperFitness(1,i)fobj(GrassHopperPositions(i,:)); end fitness_history(i,1)GrassHopperFitness(1,i); position_history(i,1,:)GrassHopperPositions(i,:); Trajectories(:,1)GrassHopperPositions(:,1); end [sorted_fitness,sorted_indexes]sort(GrassHopperFitness); % Find the best grasshopper (target) in the first population for newindex1:N Sorted_grasshopper(newindex,:)GrassHopperPositions(sorted_indexes(newindex),:); end TargetPositionSorted_grasshopper(1,:); TargetFitnesssorted_fitness(1); % Main loop l2; % Start from the second iteration since the first iteration was dedicated to calculating the fitness of antlions while lMax_iter1 ccMax-l*((cMax-cMin)/Max_iter); % Eq. (2.8) in the paper %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i1:size(GrassHopperPositions,1) temp GrassHopperPositions; % for k1:2:dim S_izeros(dim,1); for j1:N if i~j Distdistance(temp(:,j), temp(:,i)); % Calculate the distance between two grasshoppers r_ij_vec(temp(:,j)-temp(:,i))/(Disteps); % xj-xi/dij in Eq. (2.7) xj_xi2rem(Dist,2); % |xjd - xid| in Eq. (2.7) s_ij((ub - lb)*c/2)*S_func(xj_xi).*r_ij_vec; % The first part inside the big bracket in Eq. (2.7) S_iS_is_ij; end end S_i_total S_i; % end X_new c * S_i_total (TargetPosition); % Eq. (2.7) in the paper GrassHopperPositions_temp(i,:)X_new; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % GrassHopperPositions GrassHopperPositionsGrassHopperPositions_temp; for i1:size(GrassHopperPositions,1) % Relocate grasshoppers that go outside the search space TpGrassHopperPositions(i,:)ub;TmGrassHopperPositions(i,:)lb;GrassHopperPositions(i,:)(GrassHopperPositions(i,:).*(~(TpTm)))ub.*Tplb.*Tm; % Calculating the objective values for all grasshoppers if flag 1 GrassHopperFitness(1,i)fobj(GrassHopperPositions(i,1:end-1)); else GrassHopperFitness(1,i)fobj(GrassHopperPositions(i,:)); end fitness_history(i,l)GrassHopperFitness(1,i); position_history(i,l,:)GrassHopperPositions(i,:); Trajectories(:,l)GrassHopperPositions(:,1); % Update the target if GrassHopperFitness(1,i)TargetFitness TargetPositionGrassHopperPositions(i,:); TargetFitnessGrassHopperFitness(1,i); end end Convergence_curve(l)TargetFitness; disp([In iteration #, num2str(l), , targets objective , num2str(TargetFitness)]) l l 1; end if (flag1) TargetPosition TargetPosition(1:dim-1); end timetoc3 仿真结果4 参考文献[1]兰娅勋. 混沌和柯西变异的蝗虫优化算法及特征选择[J]. 微电子学与计算机, 2021, 38(11):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仿真相关matlab代码问题可私信交流。部分理论引用网络文献若有侵权联系博主删除。