电子学报 ›› 2017, Vol. 45 ›› Issue (8): 1849-1855.DOI: 10.3969/j.issn.0372-2112.2017.08.007

• 学术论文 • 上一篇    下一篇

区域分割的自适应变异粒子群算法

陈侃松1,2, 阮玉龙1, 戴磊1, 兰智高2, 邵建设2   

  1. 1. 湖北大学计算机与信息工程学院物联网工程研究所, 湖北武汉 430062;
    2. 黄冈师范学院电子信息学院, 湖北黄冈 438000
  • 收稿日期:2016-03-21 修回日期:2016-07-22 出版日期:2017-08-25 发布日期:2017-08-25
  • 作者简介:陈侃松,男,1972年生,教授,博士生导师.主要研究方向为无线传感网络及应用.E-mail:kschen1999@aliyun.com;阮玉龙,男,1993年生,硕士研究生,主要研究方向为无线传感网络节点部署.
  • 基金资助:
    国家科技支撑计划(NO.2015BAK03B02)

Regional-segmentation Self-adapting Variation Particle Swarm Optimization

CHEN Kan-song1,2, RUAN Yu-long1, DAI Lei1, LAN Zhi-gao2, SHAO Jian-she2   

  1. 1. Institute of Internet of Things, School of Computer Science and Information Engineering, Hubei University, Wuhan, Hubei 430062, China;
    2. School of Electronic Information, Huanggang Normal University, Huanggang, Hubei 438000, China
  • Received:2016-03-21 Revised:2016-07-22 Online:2017-08-25 Published:2017-08-25

摘要: 为了提高粒子群算法(PSO)的收敛性及多样性,提出一种基于区域分割的自适应变异粒子群算法(RSVPSO).算法采用区域分割的思想,利用粒子间信息交叉,使粒子搜索区间快速缩小;同时在迭代后期与自适应变异策略相结合,提高粒子跳出局部最优陷阱的能力和增强粒子多样性,达到寻优的目的.将所提出的算法应用于8个测试函数,并与精英免疫克隆选择的协同进化粒子群等算法进行比较,结果表明,新算法在收敛速度、搜索精度及寻优效率等方面有较大提高.

关键词: 区域分割, 信息交叉, 自适应变异, 多样性

Abstract: To improve convergence and diversity of particle swarm optimization(PSO),an improved PSO which called regional-segmentation self-adapting variation particle swarm optimization (RSVPSO) algorithm is introduced.Regional-segmentation is adopted in the algorithm,using information cross between particles,narrow search region quickly; combining with self-adapting variation strategy in late iterations at the same time,improved capacity of jumping out local optimum trap and enhanced the diversity of particles,reach the goal of optimization.The proposed algorithm is applied to eight test functions and compared with the elite immune clonal selection co-evolutionary particle swarm optimization and so on.The results show that the proposed algorithm has considerable improvement in the convergence speed,search accuracy,optimum efficiency and so on.

Key words: regional-segmentation, information cross, self-adapting variation, diversity

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