Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (5): 1336-1341.DOI: 10.11772/j.issn.1001-9081.2015.05.1336

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Particle swarm optimization algorithm using opposition-based learning and adaptive escape

LYU Li, ZHAO Jia, SUN Hui   

  1. School of Information Engineering, Nanchang Institute of Technology, Nanchang Jiangxi 330099, China
  • Received:2014-12-09 Revised:2015-02-09 Online:2015-05-10 Published:2015-05-14

具有反向学习和自适应逃逸功能的粒子群优化算法

吕莉, 赵嘉, 孙辉   

  1. 南昌工程学院 信息工程学院, 南昌 330099
  • 通讯作者: 吕莉
  • 作者简介:吕莉(1982-),女,江西贵溪人,副教授,硕士,主要研究方向:计算智能、目标检测与跟踪; 赵嘉(1981-),男,安徽桐城人,副教授,硕士,主要研究方向:计算智能、大数据; 孙辉(1959-),男,江西九江人,教授,博士,主要研究方向: 智能算法、遥感影像处理.
  • 基金资助:

    国家自然科学基金资助项目(61261039,61263029);江西省自然科学基金资助项目(20132BAB211031,20142BAB207018);江西省科技支撑计划项目(20142BBG70034);江西省高校科技落地计划项目(KJLD13096);南昌市科技计划项目(2013HZCG011,2014HZZC008, 2013HZCG006).

Abstract:

To overcome slow convergence velocity of Particle Swarm Optimization (PSO) which falls into local optimum easily, the paper proposed a new approach, a PSO algorithm using opposition-based learning and adaptive escape. The proposed algorithm divided states of population evolution into normal state and premature state by setting threshold. If popolation is in normal state, standard PSO algorithm was adopted to evolve; otherwise, it falls into "premature", the algorithm with opposition-based learning strategy and adaptive escape was adopted, the individual optimal location generates the opposite solution by opposition-based learning, increases the learning ability of particle, enhances the ability to escape from local optimum, and raises the optimizing rate. Experiments were conducted on 8 classical benchmark functions, the experimental results show that the proposed algorithm has better convergence velocity and precision than classical PSO algorithm, such as Fully Imformed Particle Swarm optimization (FIPS), self-organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients (HPSO-TVAC), Comprehensive Learning Particle Swarm Optimizer (CLPSO), Adaptive Particle Swarm Optimization (APSO), Double Center Particle Swarm Optimization (DCPSO) and Particle Swarm Optimization algorithm with Fast convergence and Adaptive escape (FAPSO).

Key words: Particle Swarm Optimization (PSO) algorithm, Opposition-Based Learning (OBL), algorithm's state, adaptive escape

摘要:

为克服粒子群优化算法进化后期收敛速度慢、易陷入局部最优等缺点,提出一种具有反向学习和自适应逃逸功能的粒子群优化算法.通过设定的阈值,算法将种群进化状态划分为正常状态和"早熟"状态: 若算法处于正常的进化状态,采用标准粒子群优化算法的进化模式;当粒子陷入"早熟"状态,运用反向学习和自适应逃逸功能,对个体最优位置进行反向学习,产生粒子的反向解,增加粒子的反向学习能力,增强算法逃离局部最优的能力,提高算法寻优率.在固定评估次数的情况下,对8个基准测试函数进行仿真,实验结果表明:所提算法在收敛速度、寻优精度和逃离局部最优的能力上明显优于多种经典粒子群优化算法,如充分联系的粒子群优化算法(FIPS)、基于时变加速度系数的自组织分层粒子群优化算法(HPSO-TVAC)、综合学习的粒子群优化算法(CLPSO)、自适应粒子群优化算法(APSO)、双中心粒子群优化算法(DCPSO)和具有快速收敛和自适应逃逸功能的粒子群优化算法(FAPSO)等.

关键词: 粒子群优化算法, 反向学习, 算法状态, 自适应逃逸

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