A random key based estimation of distribution algorithm for the permutation flowshop scheduling problem.

Ayodele, Mayowa; McCall, John; Regnier-Coudert, Olivier; Bowie, Liam

Abstract

Random Key (RK) is an alternative representation for permutation problems that enables application of techniques generally used for continuous optimisation. Although the benefit of RKs to permutation optimisation has been shown, its use within Estimation of Distribution Algorithms (EDAs) has been a challenge. Recent research proposing a RK-based EDA (RKEDA) has shown that RKs can produce competitive results with state of the art algorithms. Following promising results on the Permutation Flowshop Scheduling Problem, this paper presents an analysis of RK-EDA for optimising the total flow time. Experiments show that RK-EDA outperforms other permutationbased EDAs on instances of large dimensions. The difference in performance between RK-EDA and the state of the art algorithms also decreases when the problem difficulty increases.

Citation

AYODELE, M., MCCALL, J., REGNIER-COUDERT, O. and BOWIE, L. 2017. A random key based estimation of distribution algorithm for the permutation flowshop scheduling problem. In Proceedings of the 2017 IEEE congress on evolutionary computation (CEC 2017), 5-8 June 2017, San Sebastian, Spain. New York: IEEE [online], article number 7969591, pages 2364-2371. Available from: https://doi.org/10.1109/CEC.2017.7969591

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2017 IEEE congress on evolutionary computation (CEC 2017)
• Start Date: Jun 5, 2017
• End Date: Jun 8, 2017
• Acceptance Date: Mar 7, 2017
• Online Publication Date: Jun 5, 2017
• Publication Date: Jul 7, 2017
• Deposit Date: Apr 24, 2017
• Publicly Available Date: Apr 24, 2017
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Article Number: 7969591
• Pages: 2364-2371
• DOI: https://doi.org/10.1109/CEC.2017.7969591
• Keywords: Permutation problems; Random key (RK); Estimation of distribution algorithms (EDA's)
• Public URL: http://hdl.handle.net/10059/2285
• Contract Date: Apr 24, 2017