Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (1): 148-153.DOI: 10.11772/j.issn.1001-9081.2018061342

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Improved particle swarm optimization algorithm based on hierarchical autonomous learning

YUAN Xiaoping, JIANG Shuo   

  1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou Jiangsu 221116, China
  • Received:2018-06-27 Revised:2018-08-05 Online:2019-01-10 Published:2019-01-21
  • Supported by:
    This work is partially supported by the National Key Technology Research and Development Program (2013BAK06B08).


袁小平, 蒋硕   

  1. 中国矿业大学 信息与控制工程学院, 江苏 徐州 221116
  • 通讯作者: 蒋硕
  • 作者简介:袁小平(1966-),男,江苏扬州人,教授,博士,CCF会员,主要研究方向:智能算法、机器学习、图像处理;蒋硕(1992-),男,湖北仙桃人,硕士研究生,主要研究方向:智能优化算法、智能控制。
  • 基金资助:

Abstract: Focusing on the shortages of easily falling into local optimal, low convergence accuracy and slow convergence speed in Particle Swarm Optimization (PSO) algorithm, an improved Particle Swarm Optimization based on HierarChical autonomous learning (HCPSO) algorithm was proposed. Firstly, according to the particle fitness value and the number of iterations, the population was dynamically divided into three different classes. Then, according to characteristics of different classes of particles, local learning model, standard learning model and global learning model were respectively adopted to increase particle diversity and reflect the effect of individual difference cognition on performance of algorithm and improve the convergence speed and convergence precision of algorithm. Finally, HCPSO algorithm was compared with PSO algorithm, Self-adaptive Multi-Swarm PSO algorithm (PSO-SMS) and Dynamic Multi-Swarm PSO (DMS-PSO) algorithm on 6 typical test functions respectively. The simulation results show that the convergence speed and convergence accuracy of HCPSO algorithm are obviously higher than these of the given algorithms, and the execution time difference of the proposed algorithm and basic PSO algorithm is within 0.001 orders of magnitude. The performance of the proposed algorithm is improved without increasing complexity.

Key words: group intelligence, Particle Swarm Optimization (PSO) algorithm, particle difference, population diversity, autonomous learning

摘要: 针对粒子群优化(PSO)算法容易陷入局部最优、收敛精度不高、收敛速度较慢的问题,提出一种基于分层自主学习的改进粒子群优化(HCPSO)算法。首先,根据粒子适应度值和迭代次数将种群动态地划分为三个不同阶层;然后,根据不同阶层粒子特性,分别采用局部学习模型、标准学习模型以及全局学习模型,增加粒子多样性,反映出个体差异的认知对算法性能的影响,提高算法的收敛速度和收敛精度;最后,将HCPSO算法与PSO算法、自适应多子群粒子群优化(PSO-SMS)算法以及动态多子群粒子群优化(DMS-PSO)算法分别在6个典型的测试函数上进行对比仿真实验。仿真结果表明,HCPSO算法的收敛速度和收敛精度相对给出的对比算法均有明显提升,并且算法执行时间和基本PSO算法执行时间差距在0.001量级内,在不增加算法复杂度的情况下算法性能更高。

关键词: 群体智能, 粒子群优化算法, 粒子差异性, 种群多样性, 自主学习

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