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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Universiti Malaysia Pahang
2010
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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 |
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| 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) |
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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 |
13.154949 |