MATLAB的变色龙算法代码

以下是MATLAB中的变色龙算法代码：

function [bestSolution, bestFitness] = chameleon_algorithm(fitnessFunction, populationSize, numberOfIterations, lowerBounds, upperBounds)

% 确定初始变色龙位置
population = rand(populationSize, numel(lowerBounds)) .* (upperBounds - lowerBounds) + lowerBounds;

% 计算初始适应度
fitness = fitnessFunction(population);

% 找到最好的解决方案和适应度
[bestFitness, bestIndex] = min(fitness);
bestSolution = population(bestIndex, :);

% 开始迭代
for i = 1:numberOfIterations
    % 计算每个变色龙的适应度和斑点大小
    for j = 1:populationSize
        % 计算变色龙的适应度
        currentFitness = fitnessFunction(population(j, :));
        
        % 如果当前适应度比最佳适应度好，更新最佳适应度和解决方案
        if currentFitness < bestFitness
            bestFitness = currentFitness;
            bestSolution = population(j, :);
        end
        
        % 计算变色龙的斑点大小
        spotSize = 1 / (1 + exp(-10 * (currentFitness - bestFitness) / bestFitness));
        
        % 更新变色龙的位置和斑点
        for k = 1:numel(lowerBounds)
            % 计算斑点的方向和大小
            direction = randn(1);
            spot = spotSize * direction;
            
            % 将斑点添加到变色龙的位置
            population(j, k) = population(j, k) + spot;
            
            % 保持变色龙在边界内
            if population(j, k) < lowerBounds(k)
                population(j, k) = lowerBounds(k);
            elseif population(j, k) > upperBounds(k)
                population(j, k) = upperBounds(k);
            end
        end
    end
end
end

其中，`fitnessFunction`是一个函数句柄，输入为一个变色龙位置，输出为该位置的适应度值；`populationSize`是变色龙种群数量；`numberOfIterations`是迭代次数；`lowerBounds`和`upperBounds`分别是变色龙位置的上下界。函数的输出是最优解和最优适应度。