基于粒子群优化算法的分布式电源优化调度实现配电网稳定运行（Matlab代码实现）

% k:初始为0.6(k belongs to [0.1,1.0]),速率和x的关系(V = kX)
% wV:初始为1(wV best belongs to [0.8,1.2]),速率更新公式中速度前面的弹性系数
% wP:初始为1,种群更新公式中速度前面的弹性系数
% v:初始为5,SVM Cross Validation参数
% popcmax:初始为100,SVM 参数c的变化的最大值.
% popcmin:初始为0.1,SVM 参数c的变化的最小值.
% popgmax:初始为1000,SVM 参数g的变化的最大值.
% popgmin:初始为0.01,SVM 参数c的变化的最小值.
Vcmax = pso_option.k*pso_option.popcmax;
Vcmin = -Vcmax ;
Vgmax = pso_option.k*pso_option.popgmax;
Vgmin = -Vgmax ;
eps = 10^(-5);
% train_1 = train(1:100,1);
% train_label1 = train_label(1:100);
% train_2 = train(31:40,1:9);
% train_label2 = train_label(31:40);
%% 产生初始粒子和速度
for i=1:pso_option.sizepop
    % 随机产生种群和速度
    pop(i,1) = (pso_option.popcmax-pso_option.popcmin)*rand+pso_option.popcmin;  
    pop(i,2) = (pso_option.popgmax-pso_option.popgmin)*rand+pso_option.popgmin;
    V(i,1)=Vcmax*rands(1,1);  
    V(i,2)=Vgmax*rands(1,1);
    % 计算初始适应度
    fitness(i)=myfunc_fit1(pop(i,:));
%     [traini1,a1,b1]=svmpredict(train_label1,train_1,model);
%     [traini2,a2,b2]=svmpredict(train_label2,train_2,model);
%     fitness(i)= 0.25*mse(traini1-train_label1) + 0.75*mse(traini2-train_label2);
end
% 找极值和极值点
[global_fitness bestindex]=min(fitness); % 全局极值
local_fitness=fitness;   % 个体极值初始化
global_x=pop(bestindex,:);   % 全局极值点
local_x=pop;    % 个体极值点初始化
% 每一代种群的平均适应度
avgfitness_gen = zeros(1,pso_option.maxgen); 
%% 迭代寻优
for i=1:pso_option.maxgen
    for j=1:pso_option.sizepop
        %速度更新        
        V(j,:) = pso_option.wV*V(j,:) + pso_option.c1*rand*(local_x(j,:) - pop(j,:)) + pso_option.c2*rand*(global_x - pop(j,:));
        if V(j,1) > Vcmax
            V(j,1) = Vcmax;
        end
        if V(j,1) < Vcmin
            V(j,1) = Vcmin;
        end
        if V(j,2) > Vgmax
            V(j,2) = Vgmax;
        end
        if V(j,2) < Vgmin
            V(j,2) = Vgmin;
        end