人工蜂群算法三维路径规划代码matlab

以下是一个简单的三维路径规划示例代码，使用人工蜂群算法（Artificial Bee Colony, ABC）：

% 初始化ABC算法参数
MaxIt = 100;            % 迭代次数
nPop = 50;              % 蜜蜂数量
nOnlooker = nPop;       % 观察蜂数量
Lb = [0 0 0];           % 变量下限
Ub = [10 10 10];        % 变量上限

% 初始化ABC算法种群
empty_bee.position = [];
empty_bee.cost = [];
pop = repmat(empty_bee, nPop, 1);
for i=1:nPop
    pop(i).position = unifrnd(Lb, Ub);
    pop(i).cost = CostFunction(pop(i).position);
end

% ABC算法主循环
best_sol = pop(1);
for it=1:MaxIt
    % 阶段1：蜜蜂选择
    for i=1:nPop
        k = randi([1 nPop]);
        while k==i, k = randi([1 nPop]); end
        phi = unifrnd(-1, 1, size(pop(i).position));
        newbee.position = pop(i).position + phi.*(pop(i).position-pop(k).position);
        newbee.position = max(newbee.position, Lb);
        newbee.position = min(newbee.position, Ub);
        newbee.cost = CostFunction(newbee.position);
        if newbee.cost<=pop(i).cost
            pop(i) = newbee;
        end
    end
    
    % 阶段2：观察蜂选择
    for i=1:nOnlooker
        probs = exp(-[pop.cost]/mean([pop.cost]));
        probs = probs/sum(probs);
        k = RouletteWheelSelection(probs);
        phi = unifrnd(-1, 1, size(pop(k).position));
        newbee.position = pop(k).position + phi.*(pop(k).position-pop(randi([1 nPop])).position);
        newbee.position = max(newbee.position, Lb);
        newbee.position = min(newbee.position, Ub);
        newbee.cost = CostFunction(newbee.position);
        if newbee.cost<=pop(k).cost
            pop(k) = newbee;
        end
    end
    
    % 阶段3：蜜蜂贪婪搜索
    for i=1:nPop
        phi = unifrnd(-1, 1, size(pop(i).position));
        newbee.position = pop(i).position + phi.*(best_sol.position-pop(i).position);
        newbee.position = max(newbee.position, Lb);
        newbee.position = min(newbee.position, Ub);
        newbee.cost = CostFunction(newbee.position);
        if newbee.cost<=pop(i).cost
            pop(i) = newbee;
        end
    end
    
    % 更新最优解
    for i=1:nPop
        if pop(i).cost<best_sol.cost
            best_sol = pop(i);
        end
    end
    
    % 显示迭代过程
    disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(best_sol.cost)]);
end

% 显示最优解
disp(['Best Solution: x1=' num2str(best_sol.position(1)) ' x2=' num2str(best_sol.position(2)) ' x3=' num2str(best_sol.position(3))]);

在上面的示例代码中，`CostFunction()`是用户自定义的代价函数，用于计算给定位置的代价值。`RouletteWheelSelection()`是轮盘赌选择算法，用于在观察蜂选择阶段中选择蜜蜂。