A Multi-Population Based Self-Adaptive Migration PSO

DENG Xian-li; WEI Bo; ZENG Hui; GUI Ling; XIA Xue-wen

doi:10.3969/j.issn.0372-2112.2018.08.009

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A Multi-Population Based Self-Adaptive Migration PSO

更新时间：2025-07-16

- A Multi-Population Based Self-Adaptive Migration PSO
- Acta Electronica Sinica Vol. 46, Issue 8, Pages: 1858-1865(2018)
- 作者机构：
  
  1. 华东交通大学软件学院,江西,南昌,330013
  2. 新疆工程学院计算机工程系,新疆,乌鲁木齐,830023
  3. 华东交通大学经济管理学院,江西,南昌,330013
  4. 华东交通大学软件学院,江西,南昌,330013
  5. 新疆工程学院计算机工程系,新疆,乌鲁木齐,830023
  6. 华东交通大学经济管理学院,江西,南昌,330013
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61663009, No.61602174, No.61763010, No.61762036);Science and Technology Program of Department of Transportation of Jiangxi Province (No.2017D0038);Natural Science Foundation of Education Department of Jiangxi Province (No.GJJ160469);Higher Education Research Program Fund of Education Department of Xinjiang Uygur Autonomous Region (No.2014JYT041606);Doctoral Research Fund of Xinjiang Institute of Engineering (No.2015BQJ011712)
- DOI：10.3969/j.issn.0372-2112.2018.08.009
  CLC： TP301
- Published Online：25 August 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
DENG Xian-li, WEI Bo, ZENG Hui, et al. A Multi-Population Based Self-Adaptive Migration PSO[J]. Acta Electronica Sinica, 2018, 46(8): 1858-1865.
DOI：

DENG Xian-li, WEI Bo, ZENG Hui, et al. A Multi-Population Based Self-Adaptive Migration PSO[J]. Acta Electronica Sinica, 2018, 46(8): 1858-1865. DOI： 10.3969/j.issn.0372-2112.2018.08.009.

摘要

针对标准PSO中单一社会学习模式造成的算法容易陷入局部最优和后期收敛速度慢等问题，提出了一种基于多种群的自适应迁移PSO算法（Multi-population based self-adaptive migration PSO，MSMPSO）.通过融合两种常用的邻居拓扑结构，赋予个体更多的信息来源；在多个子种群并行进化的基础上，利用不同加速因子的组合赋予各子种群不同的搜索特性，进而通过周期性对子种群的历史性能进行评估，以此为基础指导个体的迁移操作，实现子种群间的协作与计算资源的合理分配，并最终提升算法的综合性能.对CEC2013测试函数的优化结果表明，MSMPSO在求解精度、收敛速度等方面均表现出较好的性能.

Abstract

The performance of particles' social-learning ability directly affects the search capability of PSO.To overcome some shortcomings caused by mono-social-learning model

such as premature convergence and slow convergence speed at later evolution stage

a multi-population based self-adaptive migration PSO (MSMPSO) is proposed.In MSMPSO

the two common neighbor typologies are integrated into particle's social-learning part aiming to give more information source for the particles.Furthermore

the entire population is divided into three sub-populations which are evolved in parallel.Based on the multi-population mechanism

different search characteristics caused by three different combinations of acceleration coefficients are assigned to the three sub-populations.To take advantage of different merits of different sub-populations

and realize the reasonable allocation of computing resources

individuals carry out a migration operator based on subpopulations' historical performance during the last period.Simulation results based on CEC2013 test suite manifest that the favorable comprehensive performance of MSMPSO

in terms of convergence speed and solutions accuracy.

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