Paper Info
Title
An artificial bee colony algorithm with a modified choice function for the Traveling Salesman Problem.
Abstract
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.
Year
DOI
Venue
2019
10.1016/j.swevo.2018.08.004
Swarm and Evolutionary Computation
Keywords
DocType
Volume
Hyper-heuristic,Metaheuristic,Bee algorithm,Combinatorial optimisation problem,Neighbourhood search,Lin-Kernighan
Journal
44
ISSN
Citations 
PageRank 
2210-6502
8
0.42
References 
Authors
18
3
Name
Order
Citations
PageRank
Shin Siang Choong1142.20
Li-Pei Wong21098.32
Chee Peng Lim31459122.04