Hybrid Genetic Algorithm for Flow Shop Scheduling Problem

摘要：

The flow shop scheduling problem (FSSP) is a NPHARD combinatorial problem with strong industrial background. Among the meta-heuristics, genetic algorithms attracted a lot of attention. However, lacking the major evolution direction, the effectiveness of regular genetic algorithm is restricted. In this paper, the particle swarm optimization algorithm (PSO) is introduced for better initial group. By combining PSO with GA, a hybrid optimization algorithm for FSSP is proposed. This method is validated on a series of benchmark datasets. Experimental results indicate that this method is efficient and competitive compared to some existing methods.

关键词： hybrid algorithm genetic algorithm particle swarm optimization algorithm flow shop scheduling problem

作者: Jianchao Tang Guoji Zhang Binbin Lin Bixi Zhang

作者单位: School of Computer Science and Engineering South China University of Technology Guangzhou, China Com School of Science South China University of Technology Guangzhou, China School of Economics and Management Guangdong University of Technology Guangzhou, China

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 1619-1622

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）