matlab-(含教程)基于缎蓝园丁鸟优化算法(SBO)的目标优化matlab仿真

clc; clear; close all; warning off; addpath(genpath(pwd)); rng('default'); % Problem Statement Npar = 3; VarLow=[-5.12 -5.12 -5.12]; VarHigh = [5.12 5.12 5.12]; %Parameters nb=100; %number of bowerbirds maxIter=100; %maximum number of iterations alpha = 0.1; %greatest step size pMut = 0.05; %mutation probability Z = 0.02; % spread factor % initialize a random value as best value BestSol= rand(1,Npar).* (VarHigh - VarLow) + VarLow; %elite BestCost= fitnessFunc(BestSol); GB = BestCost; t = cputime; %intialize of solutions and memory pop = zeros(nb,Npar); Fit = zeros(nb, 1); F = zeros(nb, 1); for ii = 1:nb % initialize bowers with random values pop(ii,:) = rand(1,Npar).* (VarHigh - VarLow) + VarLow; % find the fitness for of the bower Fit(ii) = fitnessFunc(pop(ii,:)); % store the best value if Fit(ii)<BestCost BestSol = pop(ii,:); BestCost= Fit(ii); end end for jj=1:maxIter rng(jj); %Calculate F from Fitness for ii=1:nb if Fit(ii) > 0 F(ii) = 1/(1+Fit(ii)); else F(ii) = 1 + abs(Fit(ii)); end end Fsum=sum(F); % sum of F % calculate probability of each bower prob = F/sum(F); oldpop = pop; oldfit = Fit; %deterministic changes for ii=1:nb for k = 1:Npar %select a target bower target = Roulette(prob); % calculate step size lambda = alpha/(1+prob(ii)); % update bower pop(ii,k) = pop(ii,k) + lambda*(((pop(target,k) + BestSol(k))/2) - pop(ii,k)); %mutation if rand<pMut pop(ii,k) = pop(ii,k) + Z*randn*(VarHigh(k) - VarLow(k)); end end % limit solution within searchspace pop(ii,:)=limiter(pop(ii,:),VarHigh, VarLow); % caclulate the new fitness Fit(ii) = fitnessFunc(pop(ii,:)); end %Elitism pop = [pop oldpop]; Fit = [Fit oldfit]; [Fit, index]=sort(Fit); % sort pop according to fitness Fit = Fit(1:nb); pop = pop(index, :); pop = pop(1:nb, :); % store the best value BestSol = pop(1, :); BestCost = Fit(1); % store the best value in each iteration GB= [GB BestCost]; end t1=cputime; fprintf('The time taken is %3.2f seconds \n',t1-t); fprintf('The best value is :'); BestSol BestCost figure plot(0:maxIter,GB, 'linewidth',1.2); xlabel('Iterations'); ylabel('Objective Function (Cost)'); grid('on')