NDLI: Autonomous Learning Adaptation for Particle Swarm Optimization

Content Provider	IEEE Xplore Digital Library
Author	Wenyong Dong Jiangsen Tian Xu Tang Kang Sheng Jin Liu
Copyright Year	2014
Description	Author affiliation: Comput. Sch., Wuhan Univ., Wuhan, China (Wenyong Dong; Jiangsen Tian; Kang Sheng; Jin Liu) \|\| Sch. of Resource & Environ. Sci., Wuhan Univ., Wuhan, China (Xu Tang)
Abstract	In order to improve the performance of PSO, this paper presents an Autonomous Learning Adaptation method for Particle Swarm Optimization (ALA-PSO) to automatically tune the control parameters of each particle. Although PSO is an ideal optimizer, one of its drawbacks focuses on its performance dependency on its parameters, which differ from one problem to another. In ALA-PSO, each particle is viewed as an intelligent agent and aims at improving itself performance, and can autonomously learn how to tune its parameters from its own experiment of successes and failures. For each particle, it means successful movement if the value of objective function in current position is improved than previous position, otherwise means failure. In case of successful movement, the parameters that are positive correlation with the direction of forward movement should be increased otherwise should be decreased. Meanwhile, in case of unsuccessful movement, inverse operation should be performed. The proposed parameter adaptive method is compared with several existing adaptive strategies, and the results show that ALA-PSO is not only effective, but also robust in different categories benchmarks.
Starting Page	223
Ending Page	228
File Size	1378787
Page Count	6
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900284
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Particle swarm optimization Educational institutions Intelligent agents Convergence Equations Computers
Content Type	Text
Resource Type	Article

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