【智能优化算法-飞蛾火焰优化算法】基于动态惯性权值策略的飞蛾火焰优化算法求解单目标问题附matlab代码

1 内容介绍

2 部分代码

function [Best_flame_score, Best_flame_pos, Convergence_curve] = MFO(Moth_pos, N, Max_iteration, lb, ub, dim, fobj)

% % 初始化飞蛾位置

% Moth_pos = initialization(N, dim, ub, lb);

Convergence_curve = zeros(1, Max_iteration);

Iteration = 1;

%% 迭代

while Iteration < Max_iteration+1

    

    %% 弃焰行为

    % Eq. (3.14) in the paper--火焰自适应数量

    Flame_no = round(N-Iteration*((N-1)/Max_iteration));

    

    for i = 1:N

        % 边界处理

        Flag4ub = Moth_pos(i, :) > ub;

        Flag4lb = Moth_pos(i, :) < lb;

        Moth_pos(i, :) = (Moth_pos(i, :).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;

        % 计算适应度值

        Moth_fitness(i) = fobj(Moth_pos(i, :));

    end

    

    if Iteration == 1

        % 排序

        [fitness_sorted, I] = sort(Moth_fitness);

        sorted_population = Moth_pos(I, :);

        % 更新火焰

        best_flames = sorted_population;

        best_flame_fitness = fitness_sorted;

    else

        % 排序

        double_population = [previous_population; best_flames];

        double_fitness = [previous_fitness best_flame_fitness];

        

        [double_fitness_sorted,I] = sort(double_fitness);

        double_sorted_population = double_population(I, :);

        

        fitness_sorted = double_fitness_sorted(1:N);

        sorted_population = double_sorted_population(1:N, :);

        

        % 更新火焰

        best_flames = sorted_population;

        best_flame_fitness = fitness_sorted;

    end

    

    % 更新全局最优解

    Best_flame_score = fitness_sorted(1);

    Best_flame_pos = sorted_population(1,:);

    

    previous_population = Moth_pos;

    previous_fitness = Moth_fitness;

    

    %% 捕焰行为

    % a从-1到-2线性递减，以计算公式（3.12）中的t

    a = -1+Iteration*((-1)/Max_iteration);

    

    for i = 1:N

        

        for j = 1:dim

            if i <= Flame_no        % 更新飞蛾相对于相应火焰的位置

                % D in Eq. (3.13)

                distance_to_flame = abs(sorted_population(i,j)-Moth_pos(i,j));

                b = 1;

                t = (a-1)*rand+1;

                % Eq. (3.12)

                Moth_pos(i, j) = distance_to_flame*exp(b.*t).*cos(t.*2*pi)+sorted_population(i, j);

            else                           % 更新飞蛾相对于相应火焰的位置

                % Eq. (3.13)

                distance_to_flame = abs(sorted_population(i, j)-Moth_pos(i, j));

                b = 1;

                t = (a-1)*rand+1;

                % Eq. (3.12)

                Moth_pos(i, j) = distance_to_flame*exp(b.*t).*cos(t.*2*pi)+sorted_population(Flame_no, j);

            end

        end

    end

    

    Convergence_curve(Iteration) = Best_flame_score;

    

    % 显示迭代信息

    display(['MFO:At iteration ', num2str(Iteration), ' the best fitness is ', num2str(Best_flame_score)]);

   

    Iteration = Iteration+1;

end

3 运行结果

4 参考文献

[1]刘倩, 毛耀. 基于高斯变异的改进型飞蛾火焰优化算法[J]. 电脑编程技巧与维护, 2020(11):2.

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