期刊
APPLIED SOFT COMPUTING
卷 11, 期 8, 页码 5782-5792出版社
ELSEVIER
DOI: 10.1016/j.asoc.2011.01.046
关键词
Job shop scheduling problem; Parallel genetic algorithm; Micro GA; Asynchronous colony GA; Autonomous immigration GA
The effort of searching an optimal solution for scheduling problems is important for real-world industrial applications especially for mission-time critical systems. In this paper, a new hybrid parallel GA (PGA) based on a combination of asynchronous colony GA (ACGA) and autonomous immigration GA (AIGA) is employed to solve benchmark job shop scheduling problem. An autonomous function of sharing the best solution across the system is enabled through the implementation of a migration operator and a global mailbox. The solution is able to minimize the makespan of the scheduling problem, as well as reduce the computation time. To further improve the computation time, micro GA which works on small population is used in this approach. The result shows that the algorithm is able to decrease the makespan considerably as compared to the conventional GA. (C) 2011 Elsevier B.V. All rights reserved.
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