Please use this identifier to cite or link to this item:
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/8673
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Phen, Chiak See | - |
dc.date.accessioned | 2010-08-16T01:20:52Z | - |
dc.date.available | 2010-08-16T01:20:52Z | - |
dc.date.issued | 2009-06-20 | - |
dc.identifier.citation | p.230-232 | en_US |
dc.identifier.uri | http://dspace.unimap.edu.my/123456789/8673 | - |
dc.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. | en_US |
dc.description.abstract | 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). | en_US |
dc.language.iso | en | en_US |
dc.publisher | Universiti Malaysia Pahang | en_US |
dc.relation.ispartofseries | Proceedings of the Malaysian Technical Universities Conference on Engineering and Technology (MUCEET) 2009 | en_US |
dc.subject | Ant Colony Optimization (ACO), | en_US |
dc.subject | Max-Min Ant System (MMAS) | en_US |
dc.subject | Quadratic Assignment Problems (QAP) | en_US |
dc.subject | Manufacturing support system | en_US |
dc.subject | Malaysian Technical Universities Conference on Engineering and Technology (MUCEET) | en_US |
dc.title | A new strategy to improve the search performance of Max-Min Ant Aystem Algorithm when solving the Quadratic Assignment Problems | en_US |
dc.type | Working Paper | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
230-232.pdf | Access is limited to UniMAP community | 174.85 kB | Adobe PDF | View/Open |
Items in UniMAP Library Digital Repository are protected by copyright, with all rights reserved, unless otherwise indicated.