• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2023, Vol. 45 ›› Issue (09): 1629-1638.

• 人工智能与数据挖掘 • 上一篇    下一篇

融入重心反向学习和单纯形搜索的粒子群优化算法

张文宁1,2,周清雷3,焦重阳1,梅亮2   

  1. (1.信息工程大学数学工程与先进计算国家重点实验室,河南 郑州450001;
    2.中原工学院软件学院,河南 郑州 450007;3.郑州大学信息工程学院,河南 郑州 450001)

  • 收稿日期:2022-03-02 修回日期:2022-05-16 接受日期:2023-09-25 出版日期:2023-09-25 发布日期:2023-09-12
  • 基金资助:
    河南省科技攻关计划(172102210592,212102210417) 

A particle swarm optimization algorithm with centroid opposition-based learning and simplex search

ZHANG Wen-ning1,2,ZHOU Qing-lei3,JIAO Chong-yang1,MEI Liang2   

  1. (1.State Key Laboratory of Mathematical Engineering and Advanced Computing,
    University of Information Engineering,Zhengzhou 450001;
    2.Software College,Zhongyuan University of Technology,Zhengzhou 450007;
    3.School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China)
  • Received:2022-03-02 Revised:2022-05-16 Accepted:2023-09-25 Online:2023-09-25 Published:2023-09-12

摘要: 针对粒子群优化PSO算法后期种群多样性差和易陷入局部最优解等问题,提出具备重心反向学习和单纯形搜索行为的粒子群优化COLS-PSO算法。初始时,基于混沌策略构造出搜索空间。进化过程中,基于Spearman系数选择需要进行重心反向学习的粒子,以帮助算法逃离局部极值区域。进一步引入局部搜索能力较强的单纯形搜索方法增强对最优粒子邻近区域的开发,以提高搜索精度。实验先在若干标准测试函数上进行,之后将COLS-PSO算法应用于软件测试数据生成问题。实验结果表明,COLS-PSO算法在求解精度、收敛速度和有效性方面表现较好,能够有效平衡种群多样性和算法收敛性的矛盾。

关键词: 粒子群优化算法, 混沌策略, 重心反向学习, 单纯形搜索, 测试数据生成

Abstract: The particle swarm optimization (PSO) algorithm often suffers from problems such as low population diversity and being trapped in local optimal solutions. To address these issues, a particle swarm optimization algorithm with centroid opposition based learning and simplex search (COLS-PSO) is proposed. During the initialization process, the search space is constructed based on a chaos strategy. During the evolution process, the particles that need to undergo centroid opposition-based learning are selected based on the Spearman coefficient to help the algorithm escape from local extreme value areas. Furthermore, a simplex search method with strong local search ability is introduced to enhance the development of the optimal particle's neighboring area and improve the search accuracy. The algorithm is tested on several standard test functions and then applied to software testing data generation problems. The experimental results show that the COLS-PSO algorithm performs well in terms of solution accuracy, convergence speed, and effectiveness, and can effectively balance the contradiction between population diversity and algorithm convergence. 

Key words: particle swarm optimization (PSO) algorithm, chaotic strategy, centroid opposition-based learning, simplex search, test data generation