改进的免疫算法在物流中心选址问题matlab代码
时间: 2023-08-27 08:04:04 浏览: 61
以下是改进的免疫粒子群算法在物流中心选址问题的MATLAB代码示例。这里使用的是MATLAB中的Global Optimization Toolbox。
```
% 定义适应度函数
function f = logistics_center_fitness(x, num_centers, num_customers)
total_distance = 0;
for i = 1:num_customers
min_distance = Inf;
for j = 1:num_centers
distance = norm(x(j,:) - x(num_centers+i,:));
if distance < min_distance
min_distance = distance;
end
end
total_distance = total_distance + min_distance;
end
f = total_distance;
end
% 定义主函数
function [best_fitness, best_solution] = improved_immune_pso(num_centers, num_customers, max_distance)
% 定义问题
problem = createOptimProblem('fmincon','objective',@(x)logistics_center_fitness(x, num_centers, num_customers),...
'x0',rand(num_centers*2,2)*max_distance,...
'lb',zeros(num_centers*2,2),'ub',ones(num_centers*2,2)*max_distance);
% 定义算法
algorithm = 'int-ibea';
options = optimoptions('gamultiobj','PopulationSize',50,'MaxGenerations',100);
% 进行优化
[x,fval] = gamultiobj(problem,2,[],[],[],[],[],[],options);
% 返回最优解
best_fitness = fval(1);
best_solution = x(1:num_centers,:);
end
% 测试算法
num_centers = 5;
num_customers = 20;
max_distance = 100.0;
[best_fitness, best_solution] = improved_immune_pso(num_centers, num_customers, max_distance);
disp(['Best Fitness: ', num2str(best_fitness)]);
disp(['Best Solution: ', mat2str(best_solution)]);
```
需要注意的是,这里的代码仅供参考,具体实现方式可能因问题的不同而有所差异。如果您有任何问题或疑问,欢迎随时向我提问。