Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/73494


Title: A hybrid EDA with ACS for solving permutation flow shop scheduling
Authors: 陳春龍
Tzeng, Yeu-Ruey;Chen, Chun-Lung;Chen, Chuen-Lung
Contributors: 資管系
Keywords: Ant colony system;Estimation of distribution algorithm;Permutation flow shop scheduling;Makespan
Date: 2012-06
Issue Date: 2015-02-12 14:18:43 (UTC+8)
Abstract: This paper proposes a hybrid estimation of distribution algorithm (EDA) with ant colony system (ACS) for the minimization of makespan in permutation flow shop scheduling problems. The core idea of EDA is that in each iteration, a probability model is estimated based on selected members in the iteration along with a sampling method applied to generate members from the probability model for the next iteration. The proposed algorithm, in each iteration, applies a new filter strategy and a local search method to update the local best solution and, based on the local best solution, generates pheromone trails (a probability model) using a new pheromone-generating rule and applies a solution construction method of ACS to generate members for the next iteration. In addition, a new jump strategy is developed to help the search escape if the search becomes trapped at a local optimum. Computational experiments on Taillard’s benchmark data sets demonstrate that the proposed algorithm generated high-quality solutions by comparing with the existing population-based search algorithms, such as genetic algorithms, ant colony optimization, and particle swarm optimization.
Relation: The International Journal of Advanced Manufacturing Technology,60(9-12),1139-1147
Data Type: article
DOI 連結: http://dx.doi.org/10.1007/s00170-011-3671-1
Appears in Collections:[資訊管理學系] 期刊論文

Files in This Item:

File Description SizeFormat
1139-1147.pdf178KbAdobe PDF1338View/Open


All items in 學術集成 are protected by copyright, with all rights reserved.


社群 sharing