Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:ENGINEERING OPTIMIZATION

Key Words:Extended continuous estimation of distribution algorithm local search hybrid algorithm permutation flow-shop scheduling problem

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 Henon 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.

ISSN No.:0305-215X

Translation or Not:no

Date of Publication:2017-01-01

Co-author:邵仲世,邵炜世

Correspondence Author:Pi Dechang