Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (9): 111-115.

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Particle Swarm Optimization based on adaptive disturbance

WANG Min1,2, TANG Jun3   

  1. 1.Department of Information Engineering, Hunan Mechanical & Electrical Polytechnic, Changsha 410151, China
    2.College of Computer and Communication, Hunan University, Changsha 410082, China
    3.Department of Equipment Engineering, Hunan Urban Construction College, Xiangtan, Hunan 411101, China
  • Online:2014-05-01 Published:2014-05-14

基于自适应扰动的粒子群优化算法

王  敏1,2,唐  俊3   

  1. 1.湖南机电职业技术学院 信息工程系,长沙 410151
    2.湖南大学 计算机与通信学院,长沙 410082
    3.湖南城建职业技术学院 设备工程系,湖南 湘潭 411101

Abstract: In order to avoid premature convergence of Particle Swarm Optimization(PSO), a new PSO algorithm based on Adaptive Disturbance(ADPSO) is proposed to help trapped particles escape from local minima. Experiments are conducted on nine multimodal functions, including four rotated functions, to verify the effectiveness of ADPSO. Simulation results demonstrate that this approach outperforms five other PSO algorithms.

Key words: Particle Swarm Optimization(PSO), adaptive disturbance, multimodal function, global optimization

摘要: 为了避免粒子群优化算法(PSO)早熟收敛,提出了一种自适应扰动的PSO算法(ADPSO),以帮助停滞的粒子跳出局部最优。为了验证算法的有效性,实验测试了九个多峰函数,包括四个旋转函数。仿真结果表明,该算法优于其他五种PSO算法。

关键词: 粒子群优化算法, 自适应扰动, 多峰函数, 全局优化