请帮我修改一下代码,修改要求如下:实验测试参数设置(种群大小40, 搜索维度30,迭代代数3000代,重复测试次数5次;以上);测试维度为30维;代码如下:% 粒子优化算法 clc clear % 设置初始参数 nPop = 50; % 种群数量 nVar = 2; % 变量数量 maxIter = 3000; % 最大迭代次数 c1 = 1.5; % 学习因子1 c2 = 1.5; % 学习因子2 w = 0.7; % 惯性权重 lb = [-5 -5]; % 变量下限 ub = [5 5]; % 变量上限 % 初始化种群 pop.Position = rand(nPop, nVar) .* (ub - lb) + lb; pop.Velocity = zeros(nPop, nVar); pop.Cost = zeros(nPop, 1); % 计算适应度值 for i = 1:nPop pop.Cost(i) = CostFunction(pop.Position(i,:)); end % 初始化个体最优位置和适应度值 pop.Best.Position = pop.Position; pop.Best.Cost = pop.Cost; % 初始化全局最优位置和适应度值 [globalBestCost, globalBestIndex] = min(pop.Cost); globalBest.Position = pop.Position(globalBestIndex, :); % 迭代寻找最优解 for iter = 1:maxIter for i = 1:nPop % 更新粒子速度 pop.Velocity(i,:) = w * pop.Velocity(i,:)... + c1 * rand(1,nVar) .* (pop.Best.Position(i,:) - pop.Position(i,:))... + c2 * rand(1,nVar) .* (globalBest.Position - pop.Position(i,:)); % 更新粒子位置 pop.Position(i,:) = pop.Position(i,:) + pop.Velocity(i,:); % 处理越界情况 pop.Position(i,:) = max(pop.Position(i,:), lb); pop.Position(i,:) = min(pop.Position(i,:), ub); % 计算适应度值 pop.Cost(i) = CostFunction(pop.Position(i,:)); % 更新个体最优位置和适应度值 if pop.Cost(i) < pop.Best.Cost(i) pop.Best.Position(i,:) = pop.Position(i,:); pop.Best.Cost(i) = pop.Cost(i); end % 更新全局最优位置和适应度值 if pop.Cost(i) < globalBestCost globalBest.Position = pop.Position(i,:); globalBestCost = pop.Cost(i); end end % 输出迭代过程中的最优解 disp(['Iteration ' num2str(iter) ': Best Cost = ' num2str(globalBestCost)]); end % 输出最终结果 disp('Optimization finished.'); disp(['Best Solution: x1 = ' num2str(globalBest.Position(1)) ', x2 = ' num2str(globalBest.Position(2))]); disp(['Best Cost: ' num2str(globalBestCost)]); % 适应度函数 function cost = CostFunction(x) cost = x(1)^2 + x(2)^2; end
时间: 2024-02-15 13:01:33 浏览: 53
好的,根据你的要求,我已经修改了代码。请看下面的修改内容:
```
clc
clear
% 设置实验测试参数
nPop = 40; % 种群大小
nVar = 30; % 搜索维度
maxIter = 3000; % 迭代代数
numRuns = 5; % 重复测试次数
% 设置算法参数
c1 = 1.5; % 学习因子1
c2 = 1.5; % 学习因子2
w = 0.7; % 惯性权重
lb = -5 * ones(1, nVar); % 变量下限
ub = 5 * ones(1, nVar); % 变量上限
% 进行多次测试
for run = 1:numRuns
% 初始化种群
pop.Position = rand(nPop, nVar) .* (ub - lb) + lb;
pop.Velocity = zeros(nPop, nVar);
pop.Cost = zeros(nPop, 1);
% 计算适应度值
for i = 1:nPop
pop.Cost(i) = CostFunction(pop.Position(i,:));
end
% 初始化个体最优位置和适应度值
pop.Best.Position = pop.Position;
pop.Best.Cost = pop.Cost;
% 初始化全局最优位置和适应度值
[globalBestCost, globalBestIndex] = min(pop.Cost);
globalBest.Position = pop.Position(globalBestIndex, :);
% 迭代寻找最优解
for iter = 1:maxIter
for i = 1:nPop
% 更新粒子速度
pop.Velocity(i,:) = w * pop.Velocity(i,:)...
+ c1 * rand(1,nVar) .* (pop.Best.Position(i,:) - pop.Position(i,:))...
+ c2 * rand(1,nVar) .* (globalBest.Position - pop.Position(i,:));
% 更新粒子位置
pop.Position(i,:) = pop.Position(i,:) + pop.Velocity(i,:);
% 处理越界情况
pop.Position(i,:) = max(pop.Position(i,:), lb);
pop.Position(i,:) = min(pop.Position(i,:), ub);
% 计算适应度值
pop.Cost(i) = CostFunction(pop.Position(i,:));
% 更新个体最优位置和适应度值
if pop.Cost(i) < pop.Best.Cost(i)
pop.Best.Position(i,:) = pop.Position(i,:);
pop.Best.Cost(i) = pop.Cost(i);
end
% 更新全局最优位置和适应度值
if pop.Cost(i) < globalBestCost
globalBest.Position = pop.Position(i,:);
globalBestCost = pop.Cost(i);
end
end
% 输出迭代过程中的最优解
disp(['Run ' num2str(run) ', Iteration ' num2str(iter) ': Best Cost = ' num2str(globalBestCost)]);
end
% 输出本次测试结果
disp(['Run ' num2str(run) ' finished.']);
disp(['Best Solution: ' num2str(globalBest.Position)]);
disp(['Best Cost: ' num2str(globalBestCost)]);
end
% 适应度函数
function cost = CostFunction(x)
cost = sum(x.^2);
end
```
我主要做了以下几个修改:
1. 根据实验测试参数设置,修改了种群大小、搜索维度、迭代代数和重复测试次数等参数。
2. 修改了变量下限和上限的设置,将其改为与搜索维度相符合的形式。
3. 在代码中添加了多次测试的循环。
4. 在每次迭代结束后,输出本次测试的最优解和适应度值。
希望这样的修改能够符合你的要求。如果还有什么需要修改的地方,请随时告诉我。