云基智能机器人实验室
新闻详情

陈科博士的论文被TOP期刊《Information Sciences》在线发表 - 山东大学云基智能机器人实验室

955
发表时间:2017-09-13 10:17

A Hybrid Particle Swarm Optimizer with Sine Cosine Acceleration Coefficients

Ke Chen, Fengyu Zhou*, Lei Yin, ShuqianWang, Yugang Wang, Fang Wan

Abstract: Particle swarm optimization (PSO) has been widely used to solve complex global optimization tasks due to its implementation simplicity and inexpensive computational overhead. However, PSO has premature convergence, is easily trapped in the local optimum solutionand is ineffective in balancing exploration and exploitation, especially in complex multi-peak search functions. To overcome the shortcomings of PSO, a hybrid particle swarm optimizer with sine cosine acceleration coefficients (H-PSO-SCAC) is proposed to solve these problems. It is verified by the application of twelve numerical optimization problems. In H-PSO-SCAC, we make the following improvements: First, we introduce sine cosine acceleration coefficients (SCAC) to efficiently control the local search and convergence to the global optimum solution. Second, the opposition-based learning (OBL) is adopted to initialize the population. Additionally, we utilize a sine map to adjust the inertia weight w. Finally, we propose a modified position update formula. Experimental results show that, in the majority of cases, the H-PSO-SCAC approach is capable of efficiently solving numerical optimization tasks and outperforms the existing similar population-based algorithms and PSO variants proposed in recent years. Therefore, the H-PSO-SCAC algorithm is successfully employed as a novel optimization strategy.

Keywords:Particle swarm optimizer; Sine cosine acceleration coefficients;  Opposition-based learning;  Sine map

Highlights:

Ø  The sine cosine acceleration coefficients (SCAC) as a new parameter adjustment strategy for the cognitive component c1      and the social component c2, respectively.

Ø  The opposition-based learning (OBL) is adopted to initialize population.

Ø  The sine map is utilized to adjust theinertia weight w.

Ø  Dynamic weight, acceleration coefficientand best-so-far position introduced to update the new position with original      

     update formula.


Information Sciences (SCI, IF=4.832)。  https://doi。org/10。1016/j。ins。2017。09。015。


会员登录
登录
我的资料
留言
回来顶部
1分快3 pk10手机投注计算 pk10手机投注软件 大发时时彩 pk10手机投注

免责声明: 本站资料及图片来源互联网文章-|,本网不承担任何由内容信息所引起的争议和法律责任。所有作品版权归原创作者所有,与本站立场无关-|,如用户分享不慎侵犯了您的权益,请联系我们告知,-|我们将做删除处理!