计算机工程与应用2019，Vol.55Issue(4)：66-71,90,7.DOI:10.3778/j.issn.1002-8331.1803-0106
基于随机鞭策机制的散漫度粒子群算法
Dispersion Particle Swarm Optimization Algorithm Based on Random Whip Mechanism
袁罗 1葛洪伟2
作者信息
- 1. 轻工过程先进控制教育部重点实验室(江南大学),江苏 无锡 214122
- 2. 江南大学 物联网工程学院,江苏 无锡 214122
- 折叠
摘要
Abstract
The Particle Swarm Optimization(PSO)has problems as being trapped in local minima due to premature con-vergence and weakness of global search capability. To overcome these disadvantages, Dispersion Particle Swarm Optimi-zation Algorithm based on Random Whip Mechanism(EGPSO)is proposed. Firstly, the concept of particles’dispersion is presented. In order to avoid falling into local optimum, the algorithm determines the state of the loose particles and marks them by evaluating the dispersion of each particle dynamically, and then uses random whip mechanism to deal with loose particles. Secondly, in order to further improve the algorithm’s convergence speed and accuracy, EGPSO handles active particles by using the optimal location of history. Experimental results on eleven standard benchmark functions demon-strate that EGPSO outperforms original PSO and the other related algorithms in terms of the solution quality and the stability.关键词
粒子群算法(PSO)/随机鞭策机制/散漫度/寻优精度/收敛速度Key words
Particle Swarm Optimization(PSO)/ random whip mechanism/ dispersion/ optimization accuracy/ conver-gence speed分类
信息技术与安全科学引用本文复制引用
袁罗,葛洪伟..基于随机鞭策机制的散漫度粒子群算法[J].计算机工程与应用,2019,55(4):66-71,90,7.
