Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (4): 1040-1044.DOI: 10.11772/j.issn.1001-9081.2015.04.1040

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Particle swarm optimization with adaptive task allocation

LIN Guohan1,2, ZHANG Jing2, LIU Zhaohua3   

  1. 1. College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan Hunan 411101, China;
    2. College of Electrical and Information Engineering, Hunan University, Changsha Hunan 410082, China;
    3. School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan Hunan 411021, China
  • Received:2014-10-29 Revised:2014-12-22 Online:2015-04-10 Published:2015-04-08

自适应任务分配的粒子群优化算法

林国汉1,2, 章兢2, 刘朝华3   

  1. 1. 湖南工程学院 电气信息学院, 湖南 湘潭 411101;
    2. 湖南大学 电气与信息工程学院, 长沙 410082;
    3. 湖南科技大学 信息与电气工程学院, 湖南 湘潭 411021
  • 通讯作者: 林国汉
  • 作者简介:林国汉(1973-),男,广东高州人,博士研究生,主要研究方向:智能计算、复杂系统计算机控制; 章兢(1957-),男,湖南湘潭人,教授,博士生导师,主要研究方向: 复杂工业系统优化控制、智能优化、并行计算、云计算; 刘朝华(1983-),男,湖南娄底人,博士,主要研究方向:复杂系统、并行计算、云计算。
  • 基金资助:

    国家自然科学基金资助项目(61174140); 中国博士后科学基金资助项目(2013M540628); 湖南省自然科学基金资助项目(14JJ3107)。

Abstract:

Conventional Particle Swarm Optimization (PSO) algorithm has disadvantage of premature convergence and is easily trapped in local optima. An improved PSO algorithm with adaptive task allocation was proposed to avoid those disadvantages. Adaptive task allocation was applied to particles according to their distribution status and fitness. All the particles were divided into exploration particles and exploitation particles, and carried out different tasks with global model and dynamic local model respectively. This strategy can make better trade-off between exploration and exploitation and enhance the diversity of particle. Dynamic neighborhood strategy broadened the search space and effectively inhibited the premature stagnation. Gaussian disturbance learning was applied to the stagnant elite particles to help them jump out from local optima region. The superior performance of the proposed algorithm in global search ability and solution accuracy was validated by optimizing six complicated composition test functions.

Key words: Particle Swarm Optimization (PSO) algorithm, diversity, adaptive task allocation, elitist learning, dynamic neighborhood

摘要:

针对基本粒子群优化(PSO)算法早熟收敛、易陷入局部极值的缺陷,提出自适应任务分配的粒子群优化算法。该算法根据粒子的多样性动态分配粒子任务,把种群粒子分为开发和探索两种类型,分别采用全局模型和动态邻域局部模型执行开发和探索任务以平衡算法的全局和局部搜索能力,维持种群多样性。动态邻域模型扩大了解的搜索空间,能有效抑制早熟停滞现象,采用高斯扰动对处于停滞状态的精英粒子进行学习,协助精英粒子跳出局部最优,进入解空间的其他区域继续进行搜索。针对6个标准复合测试函数进行实验,结果表明所提算法具有更强的全局搜索能力,求解精度更高。

关键词: 粒子群优化算法, 多样性, 自适应任务分配, 精英学习, 动态邻域

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