Study on the combination of genetic algorithms and ant Colony algorithms for solving fuzzy job shop scheduling problems
Study on the combination of genetic algorithms and ant Colony algorithms for solving fuzzy job shop scheduling problems
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DOI：
10.1109/cesa.2006.4281949
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发表时间：
2006-10
期刊：
影响因子：
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通讯作者：
Xiao-yu Song;Yunlong Zhu;Chaowan Yin;Fu-ming Li
中科院分区：
文献类型：
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作者：
Xiao-yu Song;Yunlong Zhu;Chaowan Yin;Fu-ming Li
通过使用单个算法来处理模糊的车间调度问题，很难获得满意的解决方案。在本文中，我们提出了一种算法的综合策略来解决模糊的车间调度问题。该策略采用遗传算法和蚂蚁菌落算法作为平行异步搜索算法。此外，根据模糊车间安排的特征，我们提出了一个关键操作的概念，并根据概念设计了一种新的邻里搜索方法。此外，设计了一种改进的TS算法，可以提高遗传算法和蚂蚁菌落算法的局部搜索能力。基准的13个硬问题的实验结果表明，平均一致指数比平行遗传算法增加了6.37％，而比TSAB算法增加了9.45％。禁忌搜索算法提高了遗传算法的局部搜索能力，并且合并策略是有效的。
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 strategy 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 benchmarks show that, the average agreement index increases 6.37% than parallel genetic algorithms, and increases 9.45% than TSAB algorithm. Tabu search algorithm improves the local search ability of the genetic algorithm, and the combined strategy is effective.