A Hybrid Niching-based Evolutionary PSO for Numerical Optimization Problems

Hsieh, T; Cheng, C; Yeh, W

A Hybrid Niching-based Evolutionary PSO for Numerical Optimization Problems

Hsieh, T Cheng, C Yeh, W

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE International Conference Computational Intelligence and Cybernetics, 2012, pp. 133 - 137
Issue Date:: 2012-01

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Field	Value	Language
dc.contributor.author	Hsieh, T	en_US
dc.contributor.author	Cheng, C	en_US
dc.contributor.author	Yeh, W	en_US
dc.contributor.editor	Hsieh, TJ	en_US
dc.contributor.editor	Cheng, CL	en_US
dc.contributor.editor	Yeh, WC	en_US
dc.date	2012-07-12	en_US
dc.date.issued	2012-01	en_US
dc.identifier.citation	IEEE International Conference Computational Intelligence and Cybernetics, 2012, pp. 133 - 137	en_US
dc.identifier.isbn	978-1-4673-0891-5	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22992
dc.description.abstract	Particle swarm optimization (PSO) is a populationbased optimization algorithm which has great potential because of its simplicity and malleability. PSO is a typical global searching heuristic, but there is still an insufficiency in PSO regarding solution exploitation and diversity. In view of this, inspired by the pseudo bacterial genetic algorithm (PBGA), we enhance the variety of solution exploitation by incorporating the PBGA processâ chromosome mutation. In addition to this, a modified niching method is utilized to preserve the solution diversity, and to avoid premature convergence in search process. We call the proposed algorithm Niching-based Evolutionary PSO (NEPSO). The experimental results test several commonly used numerical benchmark functions, and show that NEPSO has very promising optimization performance.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	IEEE International Conference Computational Intelligence and Cybernetics	en_US
dc.relation.isbasedon	10.1109/CyberneticsCom.2012.6381633	en_US
dc.rights	© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	A Hybrid Niching-based Evolutionary PSO for Numerical Optimization Problems	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Bali, Indonesia	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Bali, Indonesia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2012-07-12	en_US

Abstract:

Particle swarm optimization (PSO) is a populationbased optimization algorithm which has great potential because of its simplicity and malleability. PSO is a typical global searching heuristic, but there is still an insufficiency in PSO regarding solution exploitation and diversity. In view of this, inspired by the pseudo bacterial genetic algorithm (PBGA), we enhance the variety of solution exploitation by incorporating the PBGA processâ chromosome mutation. In addition to this, a modified niching method is utilized to preserve the solution diversity, and to avoid premature convergence in search process. We call the proposed algorithm Niching-based Evolutionary PSO (NEPSO). The experimental results test several commonly used numerical benchmark functions, and show that NEPSO has very promising optimization performance.

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http://hdl.handle.net/10453/22992