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Study on the combination of genetic algorithms and ant Colony algorithms for solving fuzzy job shop scheduling problems

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4 Author(s)
Xiaoyu Song ; Shenyang Institute of Automation , Chinese Academy of Sciences, Shenyang 110016, China; Graduate School of the Chinese Academy of Sciences, Beijing 100039, China ; Yunlong Zhu ; Chaowan Yin ; Fuming Li

by using a single algorithm to deal with fuzzy job shop scheduling problems, it is difficult to get a satisfied solution. In this paper we propose a combined strategy of algorithms to solve fuzzy job shop scheduling problems. This startegy adopts genetic algorithms and ant colony algorithms as a parallel asynchronous search algorithm. In addition, according to the characteristics of fuzzy Job Shop scheduling, we propose a concept of the critical operation, and design a new neighborhood search method based on the concept. Furthermore, an improved TS algorithm is designed, which can improve the local search ability of genetic algorithms and ant colony algorithms. The experimental results on 13 hard problems of bench-marks show that, the average agreement index increases 6.37% than parallel genetic algorithms, and increases 9.45% than TSAB algorithm. Taboo search algorithm improves the local search ability of the genetic algorithm, and the combined strategy is effective.

Published in:

Computational Engineering in Systems Applications, IMACS Multiconference on

Date of Conference:

Oct. 2006