Reactive max-min ant system with recursive local search and its application to TSP and QAP

Ant colony optimization is a successful metaheuristic for solving combinatorial optimization problems. However, the drawback of premature exploitation arises in ant colony optimization when coupled with local searches, in which the neighborhood’s structures of the search space are not completely...

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Main Authors: Sagban, Rafid, Ku-Mahamud, Ku Ruhana, Abu Bakar, Muhamad Shahbani
Format: Article
Language:English
Published: Taylor & Francis Group 2016
Subjects:
Online Access:https://repo.uum.edu.my/id/eprint/18475/1/IASC%202016%201-8.pdf
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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 a successful metaheuristic for solving combinatorial optimization problems. However, the drawback of premature exploitation arises in ant colony optimization when coupled with local searches, in which the neighborhood’s structures of the search space are not completely traversed.This paper proposes two algorithmic components for solving the premature exploitation, i.e. the reactive heuristics and recursive local search technique.The resulting algorithm is tested on two well-known combinatorial optimization problems arising in the artificial intelligence problems field and compared experimentally to six (6) variants of ACO with local search. Results showed that the enhanced algorithm outperforms the six ACO variants.
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spelling uum-184752016-08-08T04:16:34Z https://repo.uum.edu.my/id/eprint/18475/ Reactive max-min ant system with recursive local search and its application to TSP and QAP Sagban, Rafid Ku-Mahamud, Ku Ruhana Abu Bakar, Muhamad Shahbani QA75 Electronic computers. Computer science Ant colony optimization is a successful metaheuristic for solving combinatorial optimization problems. However, the drawback of premature exploitation arises in ant colony optimization when coupled with local searches, in which the neighborhood’s structures of the search space are not completely traversed.This paper proposes two algorithmic components for solving the premature exploitation, i.e. the reactive heuristics and recursive local search technique.The resulting algorithm is tested on two well-known combinatorial optimization problems arising in the artificial intelligence problems field and compared experimentally to six (6) variants of ACO with local search. Results showed that the enhanced algorithm outperforms the six ACO variants. Taylor & Francis Group 2016 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/18475/1/IASC%202016%201-8.pdf Sagban, Rafid and Ku-Mahamud, Ku Ruhana and Abu Bakar, Muhamad Shahbani (2016) Reactive max-min ant system with recursive local search and its application to TSP and QAP. Intelligent Automation & Soft Computing. pp. 1-8. ISSN 1079-8587 http://doi.org/10.1080/10798587.2016.1177914 doi:10.1080/10798587.2016.1177914 doi:10.1080/10798587.2016.1177914
spellingShingle QA75 Electronic computers. Computer science
Sagban, Rafid
Ku-Mahamud, Ku Ruhana
Abu Bakar, Muhamad Shahbani
Reactive max-min ant system with recursive local search and its application to TSP and QAP
title Reactive max-min ant system with recursive local search and its application to TSP and QAP
title_full Reactive max-min ant system with recursive local search and its application to TSP and QAP
title_fullStr Reactive max-min ant system with recursive local search and its application to TSP and QAP
title_full_unstemmed Reactive max-min ant system with recursive local search and its application to TSP and QAP
title_short Reactive max-min ant system with recursive local search and its application to TSP and QAP
title_sort reactive max min ant system with recursive local search and its application to tsp and qap
topic QA75 Electronic computers. Computer science
url https://repo.uum.edu.my/id/eprint/18475/1/IASC%202016%201-8.pdf
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