An extended continuous estimation of distribution algorithm for solving the permutation flow-shop scheduling problem. Issue 11 (2nd November 2017)

• Record Type: Journal Article
• Authors: Shao, Zhongshi; Pi, Dechang; Shao, Weishi
• Is Part Of: Engineering optimization. Volume 49:Issue 11(2017)
• Page Start: 1868
• Page End: 1889
• Publication Date: 2017-11-02
• Subjects: Extended continuous estimation of distribution algorithm -- local search -- hybrid algorithm -- permutation flow-shop scheduling problem; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2016.1275605
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: This article proposes an extended continuous estimation of distribution algorithm (ECEDA) to solve the permutation flow-shop scheduling problem (PFSP). In ECEDA, to make a continuous estimation of distribution algorithm (EDA) suitable for the PFSP, the largest order value rule is applied to convert continuous vectors to discrete job permutations. A probabilistic model based on a mixed Gaussian and Cauchy distribution is built to maintain the exploration ability of the EDA. Two effective local search methods, i.e. revolver-based variable neighbourhood search and Hénon chaotic-based local search, are designed and incorporated into the EDA to enhance the local exploitation. The parameters of the proposed ECEDA are calibrated by means of a design of experiments approach. Simulation results and comparisons based on some benchmark instances show the efficiency of the proposed algorithm for solving the PFSP.