NDLI: Demonstrator selection in a social learning particle swarm optimizer

Content Provider	IEEE Xplore Digital Library
Author	Ran Cheng Yaochu Jin
Copyright Year	2014
Description	Author affiliation: Dept. of Comput., Univ. of Surrey, Guildford, UK (Ran Cheng; Yaochu Jin)
Abstract	Social learning plays an important role in behavior learning among social animals. Different from individual (asocial) learning, social learning has the advantage of allowing individuals to learn behaviors from others without the extra costs of individual trial-and-error. Inspired by the natural social learning phenomenon, we have transplanted the social learning mechanism into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants, the SL-PSO is performed on a sorted swarm, and instead of merely learning from historical best positions, the particles are able to learn from anyone better (demonstrators) in the current swarm. A key mechanism in the SL-PSO is the learning strategy, where an imitator will learn from different demonstrators. However, in our previous work, little discussion has been focused on demonstrator selection, i.e., which demonstrators are to learn from by the imitator. In this paper, based on the analysis of the demonstrator selection in the SL-PSO, two demonstrator selection strategies are proposed. Experimental results show that, the proposed demonstrator selection strategies have significantly enhanced the performance of the SL-PSO in comparison to five representative PSO variants on a set of benchmark problems.
Starting Page	3103
Ending Page	3110
File Size	2686594
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900227
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	Sociology Statistics Indexes Particle swarm optimization Benchmark testing Gaussian distribution Learning systems
Content Type	Text
Resource Type	Article

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