eprintid: 8677
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/86/77
datestamp: 2023-11-09 16:20:35
lastmod: 2023-11-09 16:20:35
status_changed: 2023-11-09 16:13:15
type: article
metadata_visibility: show
creators_name: Masrom, S.
creators_name: Abidin, S.Z.Z.
creators_name: Omar, N.
creators_name: Rahman, A.S.A.
creators_name: Rizman, Z.I.
title: Dynamic parameterizations of particle swarm optimization and genetic algorithm for facility layout problem
ispublished: pub
note: cited By 2
abstract: Surrounded by an assortment of intelligent and efficient search entities, the hybridization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are proven to be a comprehensive tool for solving different kinds of optimization problems due to their contradictive working approaches. In addition, the two algorithms have achieved a remarkable improvement from the adaption of dynamic parameterizations. In this work, dynamic parameterized mutation and crossover are individually and in combination hybridized with a PSO implementation. The performances of different dynamic parameterizations of the hybrid algorithms in solving facility layout problem are compared with single PSO. The comparison revealed that the proposed technique is more effective.
date: 2017
publisher: Asian Research Publishing Network
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020092904&partnerID=40&md5=8ca26eb4239b59b51b6a60468038e607
full_text_status: none
publication: ARPN Journal of Engineering and Applied Sciences
volume: 12
number: 10
pagerange: 3195-3201
refereed: TRUE
issn: 18196608
citation:   Masrom, S. and Abidin, S.Z.Z. and Omar, N. and Rahman, A.S.A. and Rizman, Z.I.  (2017) Dynamic parameterizations of particle swarm optimization and genetic algorithm for facility layout problem.  ARPN Journal of Engineering and Applied Sciences, 12 (10).  pp. 3195-3201.  ISSN 18196608