MAT之PSO：利用PSO+ω參數實現對一元函數y = sin(10*pi*x) ./ x進行求解優化，找到最優個體適應度

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目錄

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實現代碼

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輸出結果

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實現代碼

x = 1:0.01:2;????????? y =?sin(10*pi*x) ./ x; figure plot(x, y) title('繪制目標函數曲線圖—Jason niu'); hold?onc1 = 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:sizepoppop(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:maxgenw = ws - (ws-we)*(i/maxgen);??for?j?= 1:sizepopV(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,:));endfor?j?= 1:sizepop??if?fitness(j) > fitnessgbest(j)gbest(j,:) = pop(j,:);?????fitnessgbest(j) = fitness(j);endif?fitness(j) > fitnesszbestzbest = pop(j,:);fitnesszbest = fitness(j);endendyy(i) = fitnesszbest;???? end[fitnesszbest zbest] plot(zbest, fitnesszbest,'r*')figure plot(yy) title('PSO：PSO算法(快于GA算法)+ω參數實現找到最優個體適應度—Jason niu','fontsize',12); xlabel('進化代數','fontsize',12);ylabel('適應度','fontsize',12);

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PSO：利用PSO+ω參數實現對一元函數y = sin(10*pi*x) ./ x進行求解優化，找到最優個體適應度

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