Reactive max-min ant system with recursive local search and its application to TSP and QAP. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Sagban, Rafid; Ku-Mahamud, Ku Ruhana; Abu Bakar, Muhamad Shahbani
• Is Part Of: Intelligent automation & soft computing. Volume 23:Issue 1(2017)
• Page Start: 127
• Page End: 134
• Publication Date: 2017-01-02
• Subjects: Optimization -- combinatorial problems -- metaheuristics -- swarm intelligence -- search algorithms -- ant colony optimization -- recursive local search -- reactive heuristics -- traveling salesman problem -- quadratic assignment problem; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 003.5
• DOI: 10.1080/10798587.2016.1177914
• Languages: English
• ISSNs: 1079-8587

Abstract

Abstract: 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.