An improved particle swarm optimization algorithm for flowshop scheduling problem

Authors:
Changsheng Zhang;Jigui Sun;Xingjun Zhu;Qingyun Yang
Affiliations:
Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China;Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China;Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China;Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, China
Venue:
Information Processing Letters
Year:
2008

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Abstract

The flowshop scheduling problem has been widely studied and many techniques have been applied to it, but few algorithms based on particle swarm optimization (PSO) have been proposed to solve it. In this paper, an improved PSO algorithm (IPSO) based on the ''alldifferent'' constraint is proposed to solve the flow shop scheduling problem with the objective of minimizing makespan. It combines the particle swarm optimization algorithm with genetic operators together effectively. When a particle is going to stagnate, the mutation operator is used to search its neighborhood. The proposed algorithm is tested on different scale benchmarks and compared with the recently proposed efficient algorithms. The results show that the proposed IPSO algorithm is more effective and better than the other compared algorithms. It can be used to solve large scale flow shop scheduling problem effectively.