💥1 概述
入侵杂草算法（IWO算法）是模拟杂草繁衍过程的一种随机搜索方法,具有鲁棒性、自适应性强和编程简单等优点,但也有搜索效率低,容易陷入局部最优的不足.在种群初始化阶段,研究者采用多子群法、反向学习法和混沌序列等方法使种群在全局空间分布更均匀;在空间扩散阶段,研究者将防早熟的杂草算法、Alopex算法、Lévy飞行法和蝙蝠算法等应用于IWO算法,使得部分种子在空间扩散阶段获得更强的全局搜索能力;在竞争排斥阶段,采用差分进化算法,可改善种群的多样性,并且更容易选择出优秀个体,提高收敛速度.
📚2 运行结果
🎉3 参考文献
[1]周立军.入侵杂草算法及其改进方法综述[J].白城师范学院学报,2021,35(05):35-42.
👨💻4 Matlab代码
主函数部分代码：
clc; clear; close all; format shortG %% Insert Data data=InsertData(); nvar = data.nvar; % Number of Decision Variables SizeX = [1 nvar]; % Decision Variables Matrix Size lb = -1*ones(1,nvar); % Lower Bound of Decision Variables ub = 1*ones(1,nvar); % Upper Bound of Decision Variables %% IWO Parameters Maxiter =200; % Maximum Number of iterations npop0 = 40; % Initerial Population Size npop = npop0*4; % Maximum Population Size Smin = 0; % Minimum Number of Seeds Smax = 5; % Maximum Number of Seeds Exponent = 2; % Variance Reduction Exponent sigma_initerial = 1; % Initerial Value of Standard Deviation sigma_final = 0.001; % Final Value of Standard Deviation %% Initerialization tic % Empty Plant Structure emp.x = []; emp.fit = []; emp.info = []; pop = repmat(emp, npop0, 1); % Initerial Population Array for i = 1:numel(pop) % Initerialize x pop(i).x = unifrnd(lb, ub); % Evaluation pop(i)= fitness(pop(i),data); end % Initerialize Best fit History BEST = zeros(Maxiter, 1); %% IWO Main Loop for iter = 1:Maxiter % Update Standard Deviation sigma = ((Maxiter - iter)/(Maxiter - 1))^Exponent * (sigma_initerial - sigma_final) + sigma_final; % Get Best and Worst fit Values fits = [pop.fit]; Bestfit = min(fits); Worstfit = max(fits); % Initerialize Offsprings Population newpop = []; % Reproduction for i = 1:numel(pop) ratio = (pop(i).fit - Worstfit)/(Bestfit - Worstfit); S = floor(Smin + (Smax - Smin)*ratio); for j = 1:S % Initerialize Offspring newsol = emp; % Generate Random Location newsol.x = pop(i).x + sigma * randn(SizeX); % Apply Lower/Upper Bounds newsol.x = CB(newsol.x, lb,ub); % Evaluate Offsring newsol = fitness(newsol,data); % Add Offpsring to the Population newpop = [newpop newsol]; %#ok end end % Merge Populations [pop] = [pop newpop]; % Sort Population [~, ind]=sort([pop.fit]); pop = pop(ind); % Competiterive Exclusion (Delete Extra Members) if numel(pop)>npop pop = pop(1:npop); end % Store Best Solution Ever Found gpop = pop(1); % gpop: global Solution % Store Best fit History BEST(iter) = gpop.fit; % Display iteration Information disp(['iter ' num2str(iter) ' Best = ' num2str(BEST(iter))]); end
