Title: Self-adaptive bee colony optimisation algorithm for the flexible job-shop scheduling problem

Authors: Malek Alzaqebah; Salwani Abdullah; Rami Malkawi; Sana Jawarneh

Addresses: Department of Mathematics, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, 31441, Dammam, Saudi Arabia; Basic and Applied Scientific Research Center, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, 31441, Dammam, Saudi Arabia ' Data Mining and Optimisation Research Group, Centre for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia ' Faculty of Information Technology and Computer Science, Yarmouk University, Jordan ' Department of Computer Science, Community College Dammam, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

Abstract: The bee colony optimisation (BCO) algorithm is a nature-inspired algorithm that models the natural behaviour of honey bees as they find nectar and share food sources information with other bees in the hive. This paper presents the BCO algorithm for the flexible job-shop scheduling problem (FJSP), furthermore, to improve the neighbourhood search in the BCO algorithm we introduce a self-adaptive mechanism to the BCO algorithm (self-adaptive-BCO algorithm) for adaptively selecting the neighbourhood structure to enhance the local intensification capability of the algorithm and to help the algorithm to escape from a local optimum. We perform computational experiments on three well-known benchmarks for FJSP. The BCO algorithm is compared with the self-adaptive-BCO algorithm to test the performance of the latter. The results demonstrate that the self-adaptive-BCO algorithm outperforms the BCO algorithm, the proposed approach also outperforms the best-known algorithms in some datasets and it is comparable with these algorithms in other datasets.

Keywords: bee colony optimisation; BCO; flexible job-shop; adaptive neighbourhood search strategy.

DOI: 10.1504/IJOR.2021.115417

International Journal of Operational Research, 2021 Vol.41 No.1, pp.53 - 70

Accepted: 02 Apr 2018
Published online: 02 Jun 2021 *

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