Brunel University Research Archive (BURA) >
University >
Publications >

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/5957

Title: Force-imitated particle swarm optimization using the near-neighbor effect for locating multiple optima
Authors: Liu, L
Yang, S
Wang, D
Keywords: Particle swarm optimization
Multimodal optimization problem
Near-neighbor effect
Force-imitated particle dynamics
Publication Date: 2012
Publisher: Elsevier Inc
Citation: Information Sciences, 182(1), 139 - 155, Jan 2012
Abstract: Multimodal optimization problems pose a great challenge of locating multiple optima simultaneously in the search space to the particle swarm optimization (PSO) community. In this paper, the motion principle of particles in PSO is extended by using the near-neighbor effect in mechanical theory, which is a universal phenomenon in nature and society. In the proposed near-neighbor effect based force-imitated PSO (NN-FPSO) algorithm, each particle explores the promising regions where it resides under the composite forces produced by the “near-neighbor attractor” and “near-neighbor repeller”, which are selected from the set of memorized personal best positions and the current swarm based on the principles of “superior-and-nearer” and “inferior-and-nearer”, respectively. These two forces pull and push a particle to search for the nearby optimum. Hence, particles can simultaneously locate multiple optima quickly and precisely. Experiments are carried out to investigate the performance of NN-FPSO in comparison with a number of state-of-the-art PSO algorithms for locating multiple optima over a series of multimodal benchmark test functions. The experimental results indicate that the proposed NN-FPSO algorithm can efficiently locate multiple optima in multimodal fitness landscapes.
Description: Copyright @ Elsevier Inc. All rights reserved.
Sponsorship: This work was supported in part by the Key Program of National Natural Science Foundation (NNSF) of China under Grant 70931001, Grant 70771021, and Grant 70721001, the National Natural Science Foundation (NNSF) of China for Youth under Grant 61004121, Grant 70771021, the Science Fund for Creative Research Group of NNSF of China under Grant 60821063, the PhD Programs Foundation of Ministry of Education of China under Grant 200801450008, and in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1 and Grant EP/E060722/2.
URI: http://www.sciencedirect.com/science/article/pii/S002002551000558X
http://bura.brunel.ac.uk/handle/2438/5957
DOI: http://dx.doi.org/10.1016/j.ins.2010.11.013
ISSN: 0020-0255
Appears in Collections:Information Systems and Computing
School of Information Systems, Computing and Mathematics Research Papers
Publications

Files in This Item:

File Description SizeFormat
Fulltext.pdf355.36 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.

 


Library (c) Brunel University.    Powered By: DSpace
Send us your
Feedback. Last Updated: September 14, 2010.
Managed by:
Hassan Bhuiyan