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An artificial bee colony algorithm with a Modified Choice Function for the traveling salesman problem
journal contribution
posted on 2019-02-01, 00:00 authored by S S Choong, L P Wong, Chee Peng LimChee Peng LimThe Artificial Bee Colony (ABC) algorithm is a swarm intelligence approach which has initially been proposed to solve optimisation of mathematical test functions with a unique neighbourhood search mechanism. This neighbourhood search mechanism could not be directly applied to combinatorial discrete optimisation problems. In order to tackle combinatorial discrete optimisation problems, the employed and onlooker bees need to be equipped with problem-specific perturbative heuristics. However, a large variety of problem-specific heuristics are available, and it is not an easy task to select an appropriate heuristic for a specific problem. In this paper, a hyper-heuristic method, namely a Modified Choice Function (MCF), is applied such that it can regulate the selection of the neighbourhood search heuristics adopted by the employed and onlooker bees automatically. The Lin-Kernighan (LK) local search strategy is integrated to improve the performance of the proposed model. To demonstrate the effectiveness of the proposed model, 64 Traveling Salesman Problem (TSP) instances available in TSPLIB are evaluated. On average, the proposed model solves the 64 instances to 0.055% from the known optimum within approximately 2.7 min. A performance comparison with other state-of-the-art algorithms further indicates the effectiveness of the proposed model.
History
Journal
Swarm and evolutionary computationVolume
44Pagination
622 - 635Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
2210-6502Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2018, Elsevier B.V.Usage metrics
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Keywords
Hyper-heuristicMetaheuristicBee algorithmCombinatorial optimisation problemNeighbourhood searchLin-KernighanScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Theory & MethodsComputer ScienceITERATED LOCAL SEARCHVEHICLE-ROUTING PROBLEMPARTICLE SWARM OPTIMIZATIONDISCRETE BAT ALGORITHMTIME WINDOWSINTELLIGENCEHEURISTICSFRAMEWORKSTRATEGYOPERATORArtificial Intelligence and Image ProcessingComputation Theory and Mathematics
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