TY - DATA T1 - An extended continuous estimation of distribution algorithm for solving the permutation flow-shop scheduling problem PY - 2017/01/26 AU - Zhongshi Shao AU - Dechang Pi AU - Weishi Shao UR - https://tandf.figshare.com/articles/journal_contribution/An_extended_continuous_estimation_of_distribution_algorithm_for_solving_the_permutation_flow-shop_scheduling_problem/4588309 DO - 10.6084/m9.figshare.4588309.v1 L4 - https://ndownloader.figshare.com/files/7427929 KW - Extended continuous estimation of distribution algorithm KW - local search KW - hybrid algorithm KW - permutation flow-shop scheduling problem N2 - 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. ER -