蚁群算法带有约束条件matlab
时间: 2023-05-26 07:07:09 浏览: 63
下面是一种使用蚁群算法解决带有约束条件的优化问题的Matlab代码:
function [best_sol, best_val] = constrained_ant_colony_optimization(problem)
% problem - 一个结构体,包含如下字段:
% - func - 优化目标函数句柄
% - lb - 变量下界向量
% - ub - 变量上界向量
% - constraint_func - 约束函数句柄,返回向量约束函数值和矩阵Jacobian
% - num_ants - 蚂蚁数量
% - max_iter - 最大迭代次数
% - evap_rate - 信息素蒸发速率
% - alpha - 信息素浓度的重要程度
% - beta - 启发函数值的重要程度
% - Q - 信息素常数
% - elitist_weight - 使用最优适应度更新信息素的权重
% 初始化参数
n_vars = length(problem.lb);
ants = problem.num_ants;
max_iter = problem.max_iter;
evap_rate = problem.evap_rate;
alpha = problem.alpha;
beta = problem.beta;
Q = problem.Q;
elitist_weight = problem.elitist_weight;
best_sol = [];
best_val = Inf;
% 生成初始蚂蚁
for i = 1:ants
ant(i).sol = rand(1, n_vars).*(problem.ub - problem.lb) + problem.lb;
ant(i).val = problem.func(ant(i).sol);
[~, J] = problem.constraint_func(ant(i).sol);
ant(i).constraint_val = J*ant(i).val';
ant(i).trail = zeros(1, n_vars);
end
% 开始迭代搜索
for iter = 1:max_iter
% 计算每只蚂蚁的启发函数值
heuristic_vals = zeros(ants, 1);
pheromone_vals = zeros(ants, 1);
for i = 1:ants
if isempty(ant(i).constraint_val) || all(ant(i).constraint_val <= 0)
heuristic_vals(i) = 1/ant(i).val;
pheromone_vals(i) = problem.Q/ant(i).val;
else
heuristic_vals(i) = 0;
pheromone_vals(i) = 0;
end
end
% 更新信息素浓度
for i = 1:ants
if heuristic_vals(i) > 0
ant(i).trail = (1-evap_rate).*ant(i).trail + evap_rate.*pheromone_vals(i);
end
end
% 计算每只蚂蚁的选择概率
probs = zeros(ants, 1);
for i = 1:ants
if heuristic_vals(i) > 0
probs(i) = (ant(i).trail.^alpha).*(heuristic_vals(i).^beta);
end
end
probs = probs./sum(probs);
% 用轮盘赌法选择下一步移动的方向
for i = 1:ants
if heuristic_vals(i) > 0
cum_probs = cumsum(probs);
idx = find(rand <= cum_probs, 1, 'first');
ant(i).sol = ant(i).sol + problem.elitist_weight.*(best_sol - ant(i).sol) + randn(1, n_vars).*(problem.ub-problem.lb);
ant(i).sol = max(problem.lb, ant(i).sol);
ant(i).sol = min(problem.ub, ant(i).sol);
ant(i).val = problem.func(ant(i).sol);
[~, J] = problem.constraint_func(ant(i).sol);
ant(i).constraint_val = J*ant(i).val';
end
end
% 更新最优解和信息素浓度
for i = 1:ants
if isempty(ant(i).constraint_val) || all(ant(i).constraint_val <= 0)
if ant(i).val < best_val
best_sol = ant(i).sol;
best_val = ant(i).val;
end
ant(i).trail = (1-elitist_weight).*ant(i).trail + elitist_weight.*Q./ant(i).val;
end
end
end
end