一种随机微粒群混合算法求解约束优化问题

将微粒群算法（Particle Swarm Optimization,PSO）与随机优化方法-Alopex算法相结合,提出一种随机微粒群混合算法（APSO）求解约束优化问题。该算法使PSO算法中微粒的飞行速度无记忆性,结合Alopex算法重新生成停止进化微粒的位置;采用双群体搜索机制,一个群体保存具有可行解的微粒,用APSO算法使微粒逐步搜索到最优解,另一个群体保存具有不可行解的微粒,并且可行解群体以一定的概率接受性能较优的不可行解微粒,这种简单的群体多样性机制使微粒能够快速、准确地找到位于约束边界上或附近的最优解。结果表明该算法寻优性能优良且具有较好的稳定性。

Hybrid Algorithm Based on Stochastic Particle Swarm Optimization for Solving Constrained Optimization Problems

A hybrid algorithm, APSO, was proposed by combining particle swarm optimization (PSO) with Alopex algorithm that is a stochastic optimization method, for solving constrained optimization problems. In the algorithm, inertia weight is zero, and the position of the particle whose evolution has been stopped is produced by Alopex algorithm. The proposed algorithm does not require the use of a penalty function. Instead, it uses a double population searching mechanism. One remains the particles which have feasible solution, the other remains the particles which have infeasible solution. A simple diversity mechanism wqs added, which allows some particles which have infeasible solution of good performance to remain in the feasible population. The approach makes the particles reach the global optimum solutions located on or near the boundary of the feasible region quickly and precisely. Simulation results show that the algorithm is effective.