狼群算法matlab代码

狼群算法是一种基于自然界狼群行为的优化算法，可用于解决优化问题。下面是一个简单的狼群算法的MATLAB代码示例。

function [bestSolution, bestFitness] = wolfPackAlgorithm(populationSize, dimensionality, lowerBound, upperBound, maxIterations)
    % 初始化种群
    population = lowerBound + (upperBound - lowerBound) * rand(populationSize, dimensionality);
    
    % 初始化每个狼的适应度值
    fitness = evaluateFitness(population);
    
    % 初始化最优解和最优适应度值
    bestSolution = population(1, :);
    bestFitness = fitness(1);
    
    % 开始迭代
    for iteration = 1:maxIterations
        a = 2 - iteration * (2 / maxIterations);  % a参数控制狼对领导者的跟随程度
        
        % 更新每个狼的位置
        for i = 1:populationSize
            for j = 1:dimensionality
                r1 = rand();  % 随机数1
                r2 = rand();  % 随机数2
                
                % 更新狼的位置
                if r1 < 0.5
                    population(i, j) = population(i, j) + a * (bestSolution(j) - population(i, j)) + r2 * (upperBound - lowerBound);
                else
                    population(i, j) = population(i, j) - a * (bestSolution(j) - population(i, j)) + r2 * (upperBound - lowerBound);
                end
                
                % 边界处理
                population(i, j) = max(population(i, j), lowerBound);
                population(i, j) = min(population(i, j), upperBound);
            end
        end
        
        % 更新每个狼的适应度值
        fitness = evaluateFitness(population);
        
        % 更新最优解和最优适应度值
        [minFitness, index] = min(fitness);
        if minFitness < bestFitness
            bestFitness = minFitness;
            bestSolution = population(index, :);
        end
    end
end

function fitness = evaluateFitness(population)
    % 计算适应度值（根据具体问题的目标函数自定义）
    % 假设目标函数是f(x) = x^2
    fitness = sum(population .^ 2, 2);
end

这段代码实现了一个简单的狼群算法，其中包括了种群初始化、狼群位置更新、适应度值计算以及更新最优解和最优适应度值等关键步骤。其中，evaluateFitness函数需要根据具体问题的目标函数进行自定义。