PSO:利用PSO+ω参数实现对一元函数y = sin(10*pi*x) ./ x进行求解优化,找到最优个体适应度
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x = 1:0.01:2; y = sin(10*pi*x) ./ x; figure plot(x, y) title(\'绘制目标函数曲线图—Jason niu\'); hold on c1 = 1.49445; c2 = 1.49445; maxgen = 50; sizepop = 10; Vmax = 0.5; Vmin = -0.5; popmax = 2; popmin = 1; ws = 0.9; we = 0.4; for i = 1:sizepop pop(i,:) = (rands(1) + 1) / 2 + 1; V(i,:) = 0.5 * rands(1); fitness(i) = fun(pop(i,:)); end [bestfitness bestindex] = max(fitness); zbest = pop(bestindex,:); gbest = pop; fitnessgbest = fitness; fitnesszbest = bestfitness; for i = 1:maxgen w = ws - (ws-we)*(i/maxgen); for j = 1:sizepop V(j,:) = w*V(j,:) + c1*rand*(gbest(j,:) - pop(j,:)) + c2*rand*(zbest - pop(j,:)); V(j,find(V(j,:)>Vmax)) = Vmax; V(j,find(V(j,:)<Vmin)) = Vmin; pop(j,:) = pop(j,:) + V(j,:); pop(j,find(pop(j,:)>popmax)) = popmax; pop(j,find(pop(j,:)<popmin)) = popmin; fitness(j) = fun(pop(j,:)); end for j = 1:sizepop if fitness(j) > fitnessgbest(j) gbest(j,:) = pop(j,:); fitnessgbest(j) = fitness(j); end if fitness(j) > fitnesszbest zbest = pop(j,:); fitnesszbest = fitness(j); end end yy(i) = fitnesszbest; end [fitnesszbest zbest] plot(zbest, fitnesszbest,\'r*\') figure plot(yy) title(\'PSO:PSO算法(快于GA算法)+ω参数实现找到最优个体适应度—Jason niu\',\'fontsize\',12); xlabel(\'进化代数\',\'fontsize\',12);ylabel(\'适应度\',\'fontsize\',12);
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