NDLI: An adaptive learning particle swarm optimizer for function optimization

Content Provider	IEEE Xplore Digital Library
Author	Changhe Li Shengxiang Yang
Copyright Year	2009
Description	Author affiliation: Department of Computer Science, University of Leicester, University Road, LE1 7RH, United Kingdom (Changhe Li; Shengxiang Yang)
Abstract	Traditional particle swarm optimization (PSO) suffers from the premature convergence problem, which usually results in PSO being trapped in local optima. This paper presents an adaptive learning PSO (ALPSO) based on a variant PSO learning strategy. In ALPSO, the learning mechanism of each particle is separated into three parts: its own historical best position, the closest neighbor and the global best one. By using this individual level adaptive technique, a particle can well guide its behavior of exploration and exploitation. A set of 21 test functions were used including un-rotated, rotated and composition functions to test the performance of ALPSO. From the comparison results over several variant PSO algorithms, ALPSO shows an outstanding performance on most test functions, especially the fast convergence characteristic.
Starting Page	381
Ending Page	388
File Size	1151495
Page Count	8
File Format	PDF
ISBN	9781424429585
DOI	10.1109/CEC.2009.4982972
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-18
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle swarm optimization Convergence Testing Learning systems Organisms Birds Marine animals Educational institutions Cognition Cultural differences
Content Type	Text
Resource Type	Article

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