Extended Artificial Chromosomes Genetic Algorithm for Permutation Flowshop Scheduling problems
學年 100
學期 2
出版(發表)日期 2012-03-01
作品名稱 Extended Artificial Chromosomes Genetic Algorithm for Permutation Flowshop Scheduling problems
著者 Chen, Y. M.; M. C. Chen; P. C. Chang; S. H. Chen
著錄名稱、卷期、頁數 Computers & Industrial Engineering 62(2), p.536–545
摘要 In our previous researches, we proposed the artificial chromosomes with genetic algorithm (ACGA) which combines the concept of the Estimation of Distribution Algorithms (EDAs) with genetic algorithms (GAs). The probabilistic model used in the ACGA is the univariate probabilistic model. We showed that ACGA is effective in solving the scheduling problems. In this paper, a new probabilistic model is proposed to capture the variable linkages together with the univariate probabilistic model where most EDAs could use only one statistic information. This proposed algorithm is named extended artificial chromosomes with genetic algorithm (eACGA). We investigate the usefulness of the probabilistic models and to compare eACGA with several famous permutation-oriented EDAs on the benchmark instances of the permutation flowshop scheduling problems (PFSPs). eACGA yields better solution quality for makespan criterion when we use the average error ratio metric as their performance measures. In addition, eACGA is further integrated with well-known heuristic algorithms, such as NEH and variable neighborhood search (VNS) and it is denoted as eACGAhybrid to solve the considered problems. No matter the solution quality and the computation efficiency, the experimental results indicate that eACGAhybrid outperforms other known algorithms in literature. As a result, the proposed algorithms are very competitive in solving the PFSPs.
關鍵字 Evolutionary algorithm with probabilistic models;Scheduling problems;Estimation of distribution algorithms
語言 en
ISSN 1879-0550
期刊性質 國外
收錄於 SCI
國別 GBR
出版型式 ,電子版,紙本

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121406 )