Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (12): 3298-3302.DOI: 10.11772/j.issn.1001-9081.2016.12.3298

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Particle swarm optimization algorithm with firefly behavior and Levy flight

FU Qiang1, GE Hongwei1,2, SU Shuzhi1   

  1. 1. School of Internet of Things, Jiangnan University, Wuxi Jiangsu 214122, China;
    2. Ministry of Education Key Laboratory of Advanced Process Control for Light Industry(Jiangnan University), Wuxi Jiangsu 214122, China
  • Received:2016-05-23 Revised:2016-07-15 Online:2016-12-10 Published:2016-12-08
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61402203), the Research Innovation Program for College Graduates of Jiangsu Province (KYLX15_1169).


付强1, 葛洪伟1,2, 苏树智1   

  1. 1. 江南大学 物联网工程学院, 江苏 无锡 214122;
    2. 轻工过程先进控制教育部重点实验室(江南大学), 江苏 无锡 214122
  • 通讯作者: 葛洪伟
  • 作者简介:付强(1990-),男,江苏徐州人,硕士研究生,CCF会员,主要研究方向:粒子群优化算法;葛洪伟(1967-),男,江苏无锡人,教授,博士,主要研究方向:人工智能、模式识别、机器学习、图像处理与分析;苏树智(1987-),男,山东泰安人,博士研究生,主要研究方向:模式识别、机器学习。
  • 基金资助:

Abstract: Particle Swarm Optimization (PSO) is easy to fall into local minimum, and has poor global search ability. Many improved algorithms cannot optimize PSO performance fully by using a single search strategy in a way. In order to solve the problem, a novel PSO with Firefly Behavior and Levy Flight (FBLFPSO) was proposed. The local search ability of PSO was improved to avoid falling into local optimum by using improved self-regulating step firefly search strategy. Then, the principle of Levy flight was taken to enhance population diversity and improve the global search ability of PSO, which contributed to escape from local optimal solution. The simulation results show that, compared with the existing correlation algorithms, the global search ability and the search accuracy of FBLFPSO are greatly improved.

Key words: Particle Swarm Optimization (PSO), self-regulating step, firefly search strategy, Levy flight

摘要: 粒子群优化(PSO)算法具有易陷入局部最小值和全局搜索能力差的缺陷,对PSO算法的改进大多只是在某一方面利用单一搜索策略进行改进,针对这种改进策略不能全面优化PSO算法性能的问题,提出一种引入萤火虫行为和Levy飞行的粒子群优化(FBLFPSO)算法。根据改进的自调节步长的萤火虫搜索策略改善PSO的局部搜索能力,避免PSO陷入局部最小值;后期利用Levy飞行策略增强种群多样性,提高PSO全局搜索能力,跳出局部最优解。仿真实验结果表明,与现有相关算法相比,FBLFPSO的全局搜索能力和搜索精度都有较大提高。

关键词: 粒子群优化, 自调节步长, 萤火虫搜索策略, Levy飞行

CLC Number: