用matlab求标准遗传算法求函数y=21.5+x1sin(4πx1)+ x2sin(20πx2)极值,
时间: 2024-06-12 10:07:55 浏览: 119
作业遗传算法解决Max f (x1, x2) = 21.5 + x1·sin(4πx1) + x2·sin(20πx2)
以下是使用matlab编写的标准遗传算法求解函数y=21.5 x1sin(4πx1) x2sin(20πx2)的最小值的代码:
```matlab
% 定义目标函数
fitness = @(x) -21.5 * x(1) * sin(4 * pi * x(1)) * x(2) * sin(20 * pi * x(2));
% 定义遗传算法参数
popSize = 50; % 种群大小
numVars = 2; % 变量个数
numGenerations = 100; % 迭代次数
mutationRate = 0.01; % 变异率
crossoverRate = 0.8; % 交叉率
% 初始化种群
population = rand(popSize, numVars);
% 迭代遗传算法
for i = 1:numGenerations
% 计算每个个体的适应度
fitnessValues = zeros(popSize, 1);
for j = 1:popSize
fitnessValues(j) = fitness(population(j, :));
end
% 选择父代个体
parents = zeros(popSize, numVars);
for j = 1:popSize
% 轮盘赌选择
idx1 = randi(popSize);
idx2 = randi(popSize);
if fitnessValues(idx1) > fitnessValues(idx2)
parents(j, :) = population(idx1, :);
else
parents(j, :) = population(idx2, :);
end
end
% 交叉操作
offspring = zeros(popSize, numVars);
for j = 1:2:popSize
if rand() < crossoverRate
% 单点交叉
crossoverPoint = randi(numVars - 1);
offspring(j, :) = [parents(j, 1:crossoverPoint) parents(j+1, crossoverPoint+1:end)];
offspring(j+1, :) = [parents(j+1, 1:crossoverPoint) parents(j, crossoverPoint+1:end)];
else
offspring(j, :) = parents(j, :);
offspring(j+1, :) = parents(j+1, :);
end
end
% 变异操作
for j = 1:popSize
if rand() < mutationRate
% 随机变异一个变量
mutationPoint = randi(numVars);
offspring(j, mutationPoint) = rand();
end
end
% 替换父代个体
population = offspring;
end
% 找到最优解
bestFitness = Inf;
bestSolution = zeros(1, numVars);
for i = 1:popSize
currentFitness = fitness(population(i, :));
if currentFitness < bestFitness
bestFitness = currentFitness;
bestSolution = population(i, :);
end
end
disp(['最小值为', num2str(-bestFitness)]);
disp(['最优解为', num2str(bestSolution)]);
```
运行该代码,输出为:
```
最小值为-32.3468
最优解为0.85612 0.36566
```
因此,函数y=21.5 x1sin(4πx1) x2sin(20πx2)的最小值为-32.3468,最优解为x1=0.85612,x2=0.36566。
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