摘要
惯性权重的取值对改善微粒群优化(Particle Swarm Optimization,PSO)算法的收敛性起着关键作用。针对惯性权重的取值问题,提出一种基于T-S模型的模糊自适应PSO(T-SPSO)算法。算法根据当前种群最优适应值和惯性权重,自适应更新惯性权重取值,改善了算法收敛性。最后以典型优化问题的实例仿真验证了所提出算法有效性。
Inertia weight is one of the most crucial factors affecting the performance of particle swarm optimization. To efficiently control the value of inertia weight, a fuzzy adaptive particle swarm optimization strategy based on T-S model （T-SPSO） was proposed, in which the inertia weight was updated adaptively according to the best current fitness and inertia weight. The simulation for the typical benchmark function shows： compared with the contrast method, the T-SPSO has the better convergence accuracy and faster evolution velocity.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2009年第14期4335-4338,共4页
Journal of System Simulation
