浣熊优化算法matlab代码

浣熊优化算法（Raccoon Optimization Algorithm，ROA）是一种仿生优化算法，灵感来源于浣熊的觅食行为。下面是一个简单的浣熊优化算法的 MATLAB 代码示例：

% 参数设置
maxIter = 100; % 迭代次数
popSize = 50; % 种群大小
dim = 2; % 优化问题的维度
lb = [-10, -10]; % 变量的下界
ub = [10, 10]; % 变量的上界

% 初始化种群
population = lb + (ub - lb) .* rand(popSize, dim);

% 计算适应度函数值
fitness = calculateFitness(population);

% 寻找最优解
[bestFitness, bestIndex] = min(fitness);
bestSolution = population(bestIndex, :);

% 迭代优化过程
for iter = 1:maxIter
    % 更新种群
    newPopulation = population;
    
    for i = 1:popSize
        % 随机选择两个个体
        p1 = randi(popSize);
        p2 = randi(popSize);
        
        % 浣熊的觅食行为
        if fitness(p1) < fitness(p2)
            newPopulation(i, :) = population(i, :) + rand(1, dim) .* (population(p1, :) - population(i, :));
        else
            newPopulation(i, :) = population(i, :) + rand(1, dim) .* (population(i, :) - population(p2, :));
        end
        
        % 边界处理
        newPopulation(i, :) = max(lb, min(ub, newPopulation(i, :)));
    end
    
    % 更新种群适应度
    newFitness = calculateFitness(newPopulation);
    
    % 更新最优解
    [newBestFitness, newBestIndex] = min(newFitness);
    
    if newBestFitness < bestFitness
        bestFitness = newBestFitness;
        bestSolution = newPopulation(newBestIndex, :);
    end
    
    % 更新种群和适应度
    population = newPopulation;
    fitness = newFitness;
end

% 适应度函数示例（需根据具体问题自行定义）
function fitness = calculateFitness(population)
    % 计算种群中每个个体的适应度值
    fitness = sum(population.^2, 2); % 示例：目标函数为多个变量平方和
end

该代码实现了浣熊优化算法的基本流程，包括种群初始化、适应度计算、迭代优化等步骤。你可以根据具体的优化问题，自行定义适应度函数和问题的维度。