Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (9): 2516-2520.DOI: 10.11772/j.issn.1001-9081.2016.09.2516

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Enhanced multi-species-based particle swarm optimization for multi-modal function

XIE Hongxia1, MA Xiaowei2, CHEN Xiaoxiao2, XING Qiang2   

  1. 1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 221116, China;
    2. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou Jiangsu 221008, China
  • Received:2016-02-01 Revised:2016-02-25 Online:2016-09-10 Published:2016-09-08
  • Supported by:
    This work is partially supported by the Jiangsu Provincial Natural Science Foundation (BK2013 0205).

基于多种群的改进粒子群算法多模态优化

谢红侠1, 马晓伟2, 陈晓晓2, 邢强2   

  1. 1. 中国矿业大学 计算机科学与技术学院, 江苏 徐州 221116;
    2. 中国矿业大学 信息与电气工程学院, 江苏 徐州 221008
  • 通讯作者: 谢红侠
  • 作者简介:谢红侠(1980-),女,江苏徐州人,副教授,博士,主要研究方向:煤矿电力设备故障诊断与设备维护、数据挖掘;马晓伟(1992-),男,山东滨州人,硕士研究生,主要研究方向:电气设备故障诊断、粒子群优化算法;陈晓晓(1992-),女,重庆人,硕士研究生,主要研究方向:电气设备故障诊断;邢强(1990-),男,江苏南京人,硕士研究生,主要研究方向:电能质量检测。
  • 基金资助:
    江苏省自然科学基金资助项目(BK2013 0205)。

Abstract: It is difficult to balance local development and global exploration in a multi-modal function optimization process, therefore, an Enhanced Multi-Species-based Particle Swarm Optimization (EMSPSO) was proposed. An improved multi-species evolution strategy was introduced to Species-based Particle Swarm Optimization (SPSO). Several species which evolved independently were established by selecting seed in the individual optimal values to improve the stability of algorithm convergence. A redundant particle reinitialization strategy was introduced to the algorithm in order to improve the utilization of the particles, and enhance global search capability and search efficiency of the algorithm. Meanwhile, in order to prevent missing optimal extreme points in the optimization process, the rate update formula was also improved to effectively balance the local development and global exploration capability of the algorithm. Finally, six typical test functions were selected to test the performance of EMSPSO. The experimental results show that, EMSPSO has high multi-modal optimization success rate and optimal performance of global extremum search.

Key words: multi-modal function optimization, Particle Swarm Optimization (PSO) algorithm, niche technology, multi-species, redundant particle

摘要: 针对多模态函数寻优过程中开发与探索能力难以平衡的问题,提出一种基于多种群的改进粒子群算法(EMSPSO)。该算法在基于种群的粒子群算法(SPSO)的基础上改进了种群生成策略,通过在个体最优值中选择种子,将粒子群分为若干独立进化的种群,增强了算法收敛的稳定性;为了提高粒子的利用率、算法的全局搜索能力和搜索效率,引入冗余粒子重新初始化策略;同时为了防止算法在寻优的过程中遗漏适应度较优的极值点,对速度更新公式进行改进,使算法的开发与探索能力得到了有效的均衡。最后选用6个典型的测试函数进行对比实验,实验结果表明,EMSPSO具有较高的多模态寻优成功率与较优的全局极值搜索性能。

关键词: 多模态函数优化, 粒子群算法, 小生境技术, 多种群, 冗余粒子

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