A novel multi-objective particle swarm optimization algorithm for flow shop scheduling problems

Authors:
Wanliang Wang;Lili Chen;Jing Jie;Yanwei Zhao;Jing Zhang
Affiliations:
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China;College of Information Engineering, Zhejiang University of Technology, Hangzhou, China;College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China;College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China;College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Year:
2011

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Abstract

In this paper, a novel hybrid multi-objective particle swarm algorithm Mopsocd_BL is proposed to solve the flow shop scheduling problem with two objectives of minimizing makespan and the total idle time of machines. This algorithm bases on Baldwinian learning mechanism to improve local search ability of particle swarm optimization, and uses the Pareto dominance and crowding distance to update the solutions. Experimental results show that this algorithm can maintain the diversity of solutions and find more uniformly distributed Pareto optimal solutions.