Regional-segmentation Self-adapting Variation Particle Swarm Optimization

CHEN Kan-song; RUAN Yu-long; DAI Lei; LAN Zhi-gao; SHAO Jian-she

doi:10.3969/j.issn.0372-2112.2017.08.007

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Regional-segmentation Self-adapting Variation Particle Swarm Optimization

更新时间：2025-07-16

- Regional-segmentation Self-adapting Variation Particle Swarm Optimization
- Acta Electronica Sinica Vol. 45, Issue 8, Pages: 1849-1855(2017)
- 作者机构：
  
  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
  3. 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
  4. 黄冈师范学院电子信息学院,湖北,黄冈,438000
- 作者简介：
- 基金信息：
  
  National Key Technology Research and Development Program of the Ministry of Science and Technology (NO.2015BAK03B02)
- DOI：10.3969/j.issn.0372-2112.2017.08.007
  CLC： TP18
- Published Online：25 August 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
CHEN Kan-song, RUAN Yu-long, DAI Lei, et al. Regional-segmentation Self-adapting Variation Particle Swarm Optimization[J]. Acta Electronica Sinica, 2017, 45(8): 1849-1855.
DOI：

CHEN Kan-song, RUAN Yu-long, DAI Lei, et al. Regional-segmentation Self-adapting Variation Particle Swarm Optimization[J]. Acta Electronica Sinica, 2017, 45(8): 1849-1855. DOI： 10.3969/j.issn.0372-2112.2017.08.007.

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.

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.

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