文献类型：期刊文献

英文题名：An effective PSO and AIS-based hybrid intelligent algorithm for job-shop scheduling

作者：Ge, Hong-Wei[1,3];Sun, Liang[1];Liang, Yan-Chun[1,2];Qian, Feng[3]

机构：[1]Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China;[2]Inst High Performance Comp, Singapore 117528, Singapore;[3]E China Univ Sci & Technol, Sch Informat Sci, Automat Dept, Shanghai 200237, Peoples R China

年份：2008

卷号：38

期号：2

起止页码：358

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS

收录：;EI(收录号：20081411177500);WOS:【SCI-EXPANDED(收录号:WOS:000253601900009)】；

语种：英文

外文关键词：artificial immune system (AIS); artificial intelligence; job-shop scheduling problem (JSSP); particle swarm optimization (PSO); vaccination

摘要：The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective algorithm of combining PSO with AIS for solving the minimum makespan problem of job-shop scheduling is proposed. In the particle swarm system, a novel concept for the distance and velocity of a particle is presented to pave the way for the job-shop scheduling problem. In the artificial immune system, the models of vaccination and receptor editing are designed to improve the immune performance. The proposed algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. The algorithm is examined by using a set of benchmark instances with various sizes and levels of hardness and is compared with other approaches reported in some existing literature works. The computational results validate the effectiveness of the proposed approach.