Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5825
Title: An adaptive learning particle swarm optimizer for function optimization
Authors: Li, C
Yang, S
Keywords: Convergence;Learning (artificial intelligence);Particle swarm optimisation
Issue Date: 2009
Publisher: IEEE
Citation: IEEE Congress on Evolutionary Computation (CEC 2009): 381 - 388, 2009-05-18 - 2009-05-21
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.
Description: This article is posted here with permission of the IEEE - Copyright @ 2009 IEEE
URI: http://bura.brunel.ac.uk/handle/2438/5825
DOI: http://dx.doi.org/10.1109/CEC.2009.4982972
ISBN: 978-1-4244-2958-5
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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