Cover Image

A hybrid ant colony optimization algorithm for solving facility layout problems formulated as quadratic assignment problems

Purpose – This paper aims to describe a new hybrid ant colony optimization (ACO) algorithm developed to solve facility layout problems (FLPs) formulated as quadratic assignment problems (QAPs). Design/methodology/approach – A hybrid ACO algorithm which combines max‐min ant system (MMAS) (i.e. a...

Full description

Saved in:
Bibliographic Details
Main Authors: Kuan, Yew Wong, Phen, Chiak See
Format: Article
Published: Emerald Group Publishing Limited 2010
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/id/eprint/22796/
https://doi.org/10.1108/02644401011008559
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.22796
record_format eprints
spelling my.utm.227962018-03-13T17:55:34Z http://eprints.utm.my/id/eprint/22796/ A hybrid ant colony optimization algorithm for solving facility layout problems formulated as quadratic assignment problems Kuan, Yew Wong Phen, Chiak See TJ Mechanical engineering and machinery Purpose – This paper aims to describe a new hybrid ant colony optimization (ACO) algorithm developed to solve facility layout problems (FLPs) formulated as quadratic assignment problems (QAPs). Design/methodology/approach – A hybrid ACO algorithm which combines max‐min ant system (MMAS) (i.e. a variant of ACO) with genetic algorithm (GA) has been developed. The hybrid algorithm is further improved with the use of a novel minimum pheromone threshold strategy (MPTS). Findings – The hybrid algorithm shows satisfactory results in the experimental evaluation due to the synergy and collaboration between MMAS and GA. The results also show that the use of MPTS helps them to achieve such performance, by promoting search diversification. Research limitations/implications – The experimental evaluation presented emphasizes more on the search performance or pattern of the hybrid algorithm. Detailed computational work could reveal other strengths of the algorithm. Practical implications – The developmental work presented in this paper could be used by researchers and practitioners to solve QAPs. Its use may also be expanded to solve other combinatorial optimization and engineering problems. Originality/value – This paper provides useful insights into the development of a hybrid ACO algorithm that combines MMAS with GA for solving QAPs. Emerald Group Publishing Limited 2010 Article PeerReviewed Kuan, Yew Wong and Phen, Chiak See (2010) A hybrid ant colony optimization algorithm for solving facility layout problems formulated as quadratic assignment problems. Engineering Computations, 27 (1). 117 - 128. ISSN 0264-4401 https://doi.org/10.1108/02644401011008559 DOI:10.1108/02644401011008559
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Kuan, Yew Wong
Phen, Chiak See
A hybrid ant colony optimization algorithm for solving facility layout problems formulated as quadratic assignment problems
description Purpose – This paper aims to describe a new hybrid ant colony optimization (ACO) algorithm developed to solve facility layout problems (FLPs) formulated as quadratic assignment problems (QAPs). Design/methodology/approach – A hybrid ACO algorithm which combines max‐min ant system (MMAS) (i.e. a variant of ACO) with genetic algorithm (GA) has been developed. The hybrid algorithm is further improved with the use of a novel minimum pheromone threshold strategy (MPTS). Findings – The hybrid algorithm shows satisfactory results in the experimental evaluation due to the synergy and collaboration between MMAS and GA. The results also show that the use of MPTS helps them to achieve such performance, by promoting search diversification. Research limitations/implications – The experimental evaluation presented emphasizes more on the search performance or pattern of the hybrid algorithm. Detailed computational work could reveal other strengths of the algorithm. Practical implications – The developmental work presented in this paper could be used by researchers and practitioners to solve QAPs. Its use may also be expanded to solve other combinatorial optimization and engineering problems. Originality/value – This paper provides useful insights into the development of a hybrid ACO algorithm that combines MMAS with GA for solving QAPs.
format Article
author Kuan, Yew Wong
Phen, Chiak See
author_facet Kuan, Yew Wong
Phen, Chiak See
author_sort Kuan, Yew Wong
title A hybrid ant colony optimization algorithm for solving facility layout problems formulated as quadratic assignment problems
title_short A hybrid ant colony optimization algorithm for solving facility layout problems formulated as quadratic assignment problems
title_full A hybrid ant colony optimization algorithm for solving facility layout problems formulated as quadratic assignment problems
title_fullStr A hybrid ant colony optimization algorithm for solving facility layout problems formulated as quadratic assignment problems
title_full_unstemmed A hybrid ant colony optimization algorithm for solving facility layout problems formulated as quadratic assignment problems
title_sort hybrid ant colony optimization algorithm for solving facility layout problems formulated as quadratic assignment problems
publisher Emerald Group Publishing Limited
publishDate 2010
url http://eprints.utm.my/id/eprint/22796/
https://doi.org/10.1108/02644401011008559
_version_ 1643647400087126016
score 13.211869

Similar Items