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Main effect fine-tuning of the mutation operator and the neighbourhood function for uncapacitated facility location problems

Chan, Kit Yan; Aydin, Mehmet Emin; Fogarty, Terence C.

Authors

Kit Yan Chan

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Dr Mehmet Aydin Mehmet.Aydin@uwe.ac.uk
Senior Lecturer in Networks and Mobile Computing

Terence C. Fogarty



Abstract

In both genetic algorithms (GAs) and simulated annealing (SA), solutions can be represented by gene representation. Mutation operator in GA and neighborhood function in SA are used to explore the solution space. They usually select genes for performing mutation. The rate of selection of genes can be called mutation rate. However, randomly selecting genes may not be the best way for both algorithms. This paper describes how to estimate the main effect in genes representation. The resulting estimates cannot only be used to understand the domination of gene representation, but also employed to fine-tune the mutation rate in both the mutation operator in the GA and the neighborhood function in the SA. It has been demonstrated the use of the proposed methods for solving uncapacitated facility location problems and discuss the examination of the proposed methods with some useful comparisons with both the latest developed GA and SA for solving this problem. For many well-known benchmark problems, the proposed methods yield better results in solution quality than the previously used methods. © Springer-Verlag 2006.

Citation

Chan, K. Y., Aydin, M. E., & Fogarty, T. C. (2006). Main effect fine-tuning of the mutation operator and the neighbourhood function for uncapacitated facility location problems. Soft Computing, 10(11), 1075-1090. https://doi.org/10.1007/s00500-005-0044-4

Journal Article Type Article
Publication Date Sep 1, 2006
Journal Soft Computing
Print ISSN 1432-7643
Electronic ISSN 1433-7479
Publisher Springer (part of Springer Nature)
Peer Reviewed Peer Reviewed
Volume 10
Issue 11
Pages 1075-1090
DOI https://doi.org/10.1007/s00500-005-0044-4
Keywords mutation rate, uncapacitated facility location problem, genetic algorithm, simulated annealing, main effect
Public URL https://uwe-repository.worktribe.com/output/1036842
Publisher URL http://dx.doi.org/10.1007/s00500-005-0044-4