A hybrid PSO/GA algorithm for job shop scheduling problem

Authors:
Jianchao Tang;Guoji Zhang;Binbin Lin;Bixi Zhang
Affiliations:
,School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China;College of Science, South China University of Technology, Guangzhou, China;College of Science, South China University of Technology, Guangzhou, China;School of Economics and Management, Guangdong University of Technology, Guangzhou, China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2010

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Abstract

The job shop scheduling problem is a well-known NP hard problem, on which genetic algorithm is widely used However, due to the lack of the major evolution direction, the effectiveness of the regular genetic algorithm is restricted In this paper, we propose a new hybrid genetic algorithm to solve the job shop scheduling problem The particle swarm optimization algorithm is introduced to get the initial population, and evolutionary genetic operations are proposed We validate the new method on seven benchmark datasets, and the comparisons with some existing methods verify its effectiveness.