Reactive memory model for ant colony optimization and its application to TSP
Ant colony optimization is one of the most successful examples of swarm intelligent systems. The exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is a framework for automating the exploration and exploitation in stochastic algorithms.Restart...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/13090/1/ICCSCE%20-%20rafid.pdf |
| _version_ | 1825803120099196928 |
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| author | Sagban, Rafid Ku-Mahamud, Ku Ruhana Abu Bakar, Muhamad Shahbani |
| author_facet | Sagban, Rafid Ku-Mahamud, Ku Ruhana Abu Bakar, Muhamad Shahbani |
| author_sort | Sagban, Rafid |
| collection | UUM |
| description | Ant colony optimization is one of the most successful examples of swarm intelligent systems. The exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is a framework for automating
the exploration and exploitation in stochastic algorithms.Restarting the search with the aid of memorizing the search history is the soul of reaction.It is to increase the exploration only when needed.This paper proposes a reactive memory model to overcome the limitation of the random exploration after restart because of losing the previous history of search.The proposed model is utilized to record the previous search regions
to be used as reference for ants after restart. The performances of six (6) ant colony optimization variants were evaluated to select the base for the proposed model.Based on the results,
Max-Min Ant System has been chosen as the base for the modification.The modified algorithm called RMMAS, was applied to TSPLIB95 data and results showed that RMMAS outperformed the standard MMAS. |
| first_indexed | 2024-07-04T05:51:40Z |
| format | Conference or Workshop Item |
| id | uum-13090 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:51:40Z |
| publishDate | 2014 |
| record_format | eprints |
| spelling | uum-130902016-05-25T06:38:24Z https://repo.uum.edu.my/id/eprint/13090/ Reactive memory model for ant colony optimization and its application to TSP Sagban, Rafid Ku-Mahamud, Ku Ruhana Abu Bakar, Muhamad Shahbani QA76 Computer software Ant colony optimization is one of the most successful examples of swarm intelligent systems. The exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is a framework for automating the exploration and exploitation in stochastic algorithms.Restarting the search with the aid of memorizing the search history is the soul of reaction.It is to increase the exploration only when needed.This paper proposes a reactive memory model to overcome the limitation of the random exploration after restart because of losing the previous history of search.The proposed model is utilized to record the previous search regions to be used as reference for ants after restart. The performances of six (6) ant colony optimization variants were evaluated to select the base for the proposed model.Based on the results, Max-Min Ant System has been chosen as the base for the modification.The modified algorithm called RMMAS, was applied to TSPLIB95 data and results showed that RMMAS outperformed the standard MMAS. 2014-11-28 Conference or Workshop Item NonPeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/13090/1/ICCSCE%20-%20rafid.pdf Sagban, Rafid and Ku-Mahamud, Ku Ruhana and Abu Bakar, Muhamad Shahbani (2014) Reactive memory model for ant colony optimization and its application to TSP. In: International Conference on Control System, Computing and Engineering, 28 - 30 November 2014, Penang, Malaysia. (Unpublished) http://acscrg.com/iccsce/2014/ |
| spellingShingle | QA76 Computer software Sagban, Rafid Ku-Mahamud, Ku Ruhana Abu Bakar, Muhamad Shahbani Reactive memory model for ant colony optimization and its application to TSP |
| title | Reactive memory model for ant colony optimization and its application to TSP |
| title_full | Reactive memory model for ant colony optimization and its application to TSP |
| title_fullStr | Reactive memory model for ant colony optimization and its application to TSP |
| title_full_unstemmed | Reactive memory model for ant colony optimization and its application to TSP |
| title_short | Reactive memory model for ant colony optimization and its application to TSP |
| title_sort | reactive memory model for ant colony optimization and its application to tsp |
| topic | QA76 Computer software |
| url | https://repo.uum.edu.my/id/eprint/13090/1/ICCSCE%20-%20rafid.pdf |
| work_keys_str_mv | AT sagbanrafid reactivememorymodelforantcolonyoptimizationanditsapplicationtotsp AT kumahamudkuruhana reactivememorymodelforantcolonyoptimizationanditsapplicationtotsp AT abubakarmuhamadshahbani reactivememorymodelforantcolonyoptimizationanditsapplicationtotsp |