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An artificial bee colony algorithm with a Modified Choice Function for the traveling salesman problem

Version 2 2024-06-06, 08:10
Version 1 2018-09-10, 14:07
journal contribution
posted on 2024-06-06, 08:10 authored by SS Choong, LP Wong, Chee Peng Lim
The 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 computation

Volume

44

Pagination

622-635

Location

Amsterdam, The Netherlands

ISSN

2210-6502

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, Elsevier B.V.

Publisher

Elsevier