【改进粒子群优化算法】基于惯性权重和学习因子动态调整的粒子群算法【期刊论文复现】（Matlab代码实现）

for i=1:nPop%种群位置、速度、适应度初始化
    pop(i).Position = unifrnd(VarMin, VarMax, VarSize);
    pop(i).Velocity = unifrnd(Vmin, Vmax, VarSize);
    pop(i).Cost = fun(pop(i).Position,index);
    if pop(i).Cost < pBestSol.Cost 
       pBestSol = pop(i);%个体适应度最佳
    end
end
gBestSol = pBestSol;
    for it=1:MaxIt
        for i=1:nPop
        w = 1+(1-0.7).*(it.^2)/(MaxIt^2);
        c1 = (1-0.7).*it/MaxIt+0.7;
        c2 = (1-0.7).*it/MaxIt+0.7;
        %速度更新    
        pop(i).Velocity = w.*pop(i).Velocity+c1.*rand(VarSize).*(pBestSol.Position-pop(i).Position)+c2.*rand(VarSize).*(gBestSol.Position-pop(i).Position);
        %速度边界处理
        pop(i).Velocity = max(pop(i).Velocity, Vmin);
        pop(i).Velocity = min(pop(i).Velocity, Vmax);
        %位置更新
        pop(i).Position = pop(i).Position+pop(i).Velocity;
        %位置边界处理
        pop(i).Position = max(pop(i).Position, VarMin);
        pop(i).Position = min(pop(i).Position, VarMax);
        %适应度值更新
        pop(i).Cost = fun(pop(i).Position,index);
        %更新局部最优
        if pop(i).Cost < pBestSol.Cost
            pBestSol = pop(i);
        %更新全局最优
        if pBestSol.Cost < gBestSol.Cost    
            gBestSol = pBestSol;
        end
        end
            BestCosts(it) = gBestSol.Cost;
            disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCosts(it))]);
    end    
    end
IPSO_polbelbest = pBestSol;