云基智能机器人实验室
陈科博士的论文被二区SCI期刊《Knowledge-Based Systems》在线发表
507
发表时间:2017-10-13 16:39

Chaotic Dynamic Weight Particle Swarm Optimization for Numerical Function Optimization

Ke Chen, Fengyu Zhou, Aling Liu

Abstract:Particle swarm optimization(PSO), which isinspired by social behaviors of individuals in bird swarms, is a nature-inspired and global optimization algorithm. The PSO method is easy toimplement and has shown good performance for many real-world optimization tasks. However, PSO has problems with premature convergence and easy trapping into local optimum solutions. In order to overcome these deficiencies, a chaotic dynamic weight particle swarm optimization (CDW-PSO) is proposed. In the CDW-PSO algorithm, a chaotic map and dynamic weight are introduced to modifythe search process. The dynamic weight is defined as a function of the fitness.The search accuracy and performance of the CDW-PSO algorithm are verified onseventeen well-known classical benchmark functions. The experimental resultsshow that, for almost all functions, the CDW-PSO technique has superiorperformance compared with other nature-inspired optimizations and well-knownPSO variants. Namely, the proposed algorithm of CDW-PSO has better searchperformance.

Keyword:Particle swarm optimization; Chaotic map;Dynamic weight; Optimization

Highlights:

Ø The particle swarm optimization (PSO) is easily trapped into local optimum solution and premature convergence. In order toimprove the aforementioned problems, a chaotic dynamic weight particle swarm optimization called CDW-PSO is proposed.

Ø The CDW-PSO algorithm was used to optimization of seventeen well-known classical benchmark functions. Experimental results show that combined with chaotic map and dynamic weight of CDW-PSO method is effective than original PSO.

Ø In order to better show search performanceof CDW-PSO algorithm, similar intelligence methods and PSO variants are adopted to compare with CDW-PSO algorithm. The results indicate that the CDW-PSO algorithm outperforms similar intelligence methods and other PSO variants for most of the classical benchmark functions.

Knowledge-Based Systems (SCI,IF=4.529). https://doi.org/10.1016/j.knosys.2017.10.011

会员登录
登录
我的资料
留言
回到顶部