Particle Swarm Optimization Based on Adaptive Mutation and Diminishing Inerita Weights

被引：0
|
作者：
Yang, Huafen [1 ]
Li, Yong [1 ]
Yang, Zuyuan [2 ]
Zhang, Lihui [3 ]
Tian, Anhong [1 ]
机构：
[1] Qujing Normal Coll, Dept Comp Sci & Engn, Qujing 655000, Peoples R China
[2] Kunming Univ, Sch Automat Control & Mech Engn, Kunming 650214, Peoples R China
[3] Kunming Univ, Met Coll logist, Kunming 650033, Peoples R China
来源：
2013 NINTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) | 2013年
关键词：
Adaptive Mutation; Inerita Weight; Styling; Particle Swarm Optimization; ALGORITHM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Adaptive mutation is introduced into improved particle swarm optimization to increase the performance of particle swarm optimization algorithms. The mutation probability is adjusted according to the variance of the population's fitness. Nonlinear decreasing strategy is used to adjust the inerita weight and enhance searching ability that can abandon the local optimal solution and find the global one. Simulation results show the algorithm proposed in this paper has better convergence accuracy and higher evolution velocity compared with the conventional particle swarm optimization algorithms. The performance of improved PSO outperformed the traditional PSO.
引用
收藏
页码：549 / 553
页数：5
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