DEPSO算法matlab
时间: 2023-09-06 08:11:11 浏览: 70
DEPSO(Discrete Enhanced Particle Swarm Optimization)是一种离散粒子群优化算法,用于解决离散优化问题。这里提供一个DEPSO算法的matlab实现代码,供参考:
```matlab
function [gbest, fitGbest] = DEPSO(func, bounds, np, maxiter)
% func: 目标函数
% bounds: 取值范围
% np: 粒子数
% maxiter: 最大迭代次数
% 算法参数
wMax = 0.9;
wMin = 0.4;
phiP = 2.0;
phiG = 2.0;
epsilon = 1e-5;
% 初始化粒子
dim = numel(bounds(:,1));
pos = randi([bounds(:,1), bounds(:,2)], np, dim);
vel = randi([-1,1], np, dim);
pbest = pos;
fitPbest = feval(func, pbest);
% 初始化全局最优解
[fitGbest, idx] = min(fitPbest);
gbest = pbest(idx,:);
% 迭代
for iter = 1:maxiter
% 更新速度和位置
w = wMax - iter * (wMax - wMin) / maxiter;
for i = 1:np
randP = rand(dim,1);
randG = rand(dim,1);
vel(i,:) = w * vel(i,:) ...
+ phiP * randP' .* (pbest(i,:) - pos(i,:)) ...
+ phiG * randG' .* (repmat(gbest, np, 1) - pos(i,:));
pos(i,:) = pos(i,:) + vel(i,:);
% 位置越界处理
for j = 1:dim
if pos(i,j) < bounds(j,1)
pos(i,j) = bounds(j,1);
elseif pos(i,j) > bounds(j,2)
pos(i,j) = bounds(j,2);
end
end
end
% 更新个体最优解
fitPos = feval(func, pos);
idx = fitPos < fitPbest;
pbest(idx,:) = pos(idx,:);
fitPbest(idx) = fitPos(idx);
% 更新全局最优解
[tmp, idx] = min(fitPbest);
if tmp < fitGbest
gbest = pbest(idx,:);
fitGbest = tmp;
end
% 收敛判断
if fitGbest < epsilon
break;
end
end
end
```
其中,`func`是需要优化的目标函数,`bounds`是各个维度的取值范围,`np`是粒子数,`maxiter`是最大迭代次数。
使用时,可以先定义目标函数,比如:
```matlab
% 目标函数
function y = sphere(x)
y = sum(x.^2, 2);
end
```
然后调用DEPSO函数进行优化:
```matlab
% 范围
bounds = [-5.12, 5.12; -5.12, 5.12];
% 粒子数
np = 50;
% 最大迭代次数
maxiter = 100;
% DEPSO优化
[gbest, fitGbest] = DEPSO(@sphere, bounds, np, maxiter);
% 输出结果
fprintf('Optimal solution: x = (%f, %f), f(x) = %f\n', gbest(1), gbest(2), fitGbest);
```