差分进化jade代码复现matlab
时间: 2024-01-27 17:05:41 浏览: 35
差分进化算法(Differential Evolution,DE)是一种优化算法,用于解决连续优化问题。差分进化算法的一个变种是差分进化JADE算法(JADE,Jittered Adaptive Differential Evolution),它在收敛性能方面优于其他经典或自适应DE算法。
以下是一个用Matlab复现差分进化JADE算法的示例代码:
```matlab
function [bestSolution, bestFitness] = JADE(fitnessFunc, dim, bounds, maxGenerations, populationSize, CR, F)
% 初始化种群
population = initializePopulation(populationSize, dim, bounds);
% 计算初始种群的适应度
fitness = evaluatePopulation(fitnessFunc, population);
% 初始化最佳解和最佳适应度
[bestFitness, bestIndex] = min(fitness);
bestSolution = population(bestIndex, :);
% 迭代更新种群
for generation = 1:maxGenerations
% 生成新的个体
newPopulation = zeros(populationSize, dim);
for i = 1:populationSize
% 选择三个不同的个体作为参考向量
r = randperm(populationSize, 3);
% 生成新的个体
mutant = population(r(1), :) + F * (population(r(2), :) - population(r(3), :));
% 交叉操作
crossoverMask = rand(1, dim) < CR;
trial = population(i, :);
trial(crossoverMask) = mutant(crossoverMask);
% 选择操作
trialFitness = fitnessFunc(trial);
if trialFitness < fitness(i)
newPopulation(i, :) = trial;
fitness(i) = trialFitness;
% 更新最佳解和最佳适应度
if trialFitness < bestFitness
bestFitness = trialFitness;
bestSolution = trial;
end
else
newPopulation(i, :) = population(i, :);
end
end
% 更新种群
population = newPopulation;
end
end
function population = initializePopulation(populationSize, dim, bounds)
population = rand(populationSize, dim) .* (bounds(:, 2) - bounds(:, 1)) + bounds(:, 1);
end
function fitness = evaluatePopulation(fitnessFunc, population)
populationSize = size(population, 1);
fitness = zeros(populationSize, 1);
for i = 1:populationSize
fitness(i) = fitnessFunc(population(i, :));
end
end
```
使用该代码,你需要提供以下参数:
- `fitnessFunc`:适应度函数,用于评估个体的适应度。
- `dim`:问题的维度。
- `bounds`:问题的边界,一个dim×2的矩阵,每一行表示一个维度的上下界。
- `maxGenerations`:最大迭代次数。
- `populationSize`:种群大小。
- `CR`:交叉概率。
- `F`:缩放因子。
该代码会返回最佳解和最佳适应度。