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A new strategy to improve the search performance of Max-Min Ant Aystem Algorithm when solving the Quadratic Assignment Problems

Malaysian Technical Universities Conference on Engineering and Technology organized by Universiti Malaysia Pahang in collaboration with Universiti Tun Hussein Onn Malaysia, Universiti Teknikal Malaysia Melaka & Universiti Malaysia Perlis on June 20th - 22nd, 2009, at MS Garden Hotel, Kuantan, P...

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Main Author: Phen, Chiak See
Format: Working Paper
Language:English
Published: Universiti Malaysia Pahang 2010
Subjects:
Ant Colony Optimization (ACO),
Max-Min Ant System (MMAS)
Quadratic Assignment Problems (QAP)
Manufacturing support system
Malaysian Technical Universities Conference on Engineering and Technology (MUCEET)
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/8673
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spelling my.unimap-86732010-08-16T01:20:53Z A new strategy to improve the search performance of Max-Min Ant Aystem Algorithm when solving the Quadratic Assignment Problems Phen, Chiak See Ant Colony Optimization (ACO), Max-Min Ant System (MMAS) Quadratic Assignment Problems (QAP) Manufacturing support system Malaysian Technical Universities Conference on Engineering and Technology (MUCEET) Malaysian Technical Universities Conference on Engineering and Technology organized by Universiti Malaysia Pahang in collaboration with Universiti Tun Hussein Onn Malaysia, Universiti Teknikal Malaysia Melaka & Universiti Malaysia Perlis on June 20th - 22nd, 2009, at MS Garden Hotel, Kuantan, Pahang, Malaysia. The use of Ant Colony Optimizations (ACOs) to solve Combinatorial Optimization (CO) problems has increase rapidly. Particularly, researchers have started to seek for improvement in ACOs through various innovative methodologies. Among others is the use of innovative pheromone manipulation strategy, the modification of ACOs framework, and hybridization of ACOs with other metaheuristic algorithms. This paper presents a new pheromone manipulation strategy called the Minimum Pheromone Threshold Strategy (MPTS), which is able to enhance the search performance of the Max-Min Ant System (MMAS) algorithm (a variant of ACO). 2010-08-16T01:20:52Z 2010-08-16T01:20:52Z 2009-06-20 Working Paper p.230-232 http://hdl.handle.net/123456789/8673 en Proceedings of the Malaysian Technical Universities Conference on Engineering and Technology (MUCEET) 2009 Universiti Malaysia Pahang
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Ant Colony Optimization (ACO),
Max-Min Ant System (MMAS)
Quadratic Assignment Problems (QAP)
Manufacturing support system
Malaysian Technical Universities Conference on Engineering and Technology (MUCEET)
spellingShingle Ant Colony Optimization (ACO),
Max-Min Ant System (MMAS)
Quadratic Assignment Problems (QAP)
Manufacturing support system
Malaysian Technical Universities Conference on Engineering and Technology (MUCEET)
Phen, Chiak See
A new strategy to improve the search performance of Max-Min Ant Aystem Algorithm when solving the Quadratic Assignment Problems
description Malaysian Technical Universities Conference on Engineering and Technology organized by Universiti Malaysia Pahang in collaboration with Universiti Tun Hussein Onn Malaysia, Universiti Teknikal Malaysia Melaka & Universiti Malaysia Perlis on June 20th - 22nd, 2009, at MS Garden Hotel, Kuantan, Pahang, Malaysia.
format Working Paper
author Phen, Chiak See
author_facet Phen, Chiak See
author_sort Phen, Chiak See
title A new strategy to improve the search performance of Max-Min Ant Aystem Algorithm when solving the Quadratic Assignment Problems
title_short A new strategy to improve the search performance of Max-Min Ant Aystem Algorithm when solving the Quadratic Assignment Problems
title_full A new strategy to improve the search performance of Max-Min Ant Aystem Algorithm when solving the Quadratic Assignment Problems
title_fullStr A new strategy to improve the search performance of Max-Min Ant Aystem Algorithm when solving the Quadratic Assignment Problems
title_full_unstemmed A new strategy to improve the search performance of Max-Min Ant Aystem Algorithm when solving the Quadratic Assignment Problems
title_sort new strategy to improve the search performance of max-min ant aystem algorithm when solving the quadratic assignment problems
publisher Universiti Malaysia Pahang
publishDate 2010
url http://dspace.unimap.edu.my/xmlui/handle/123456789/8673
_version_ 1643789277049389056
score 13.222552

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