A Bayesian-Grouping Based Hybrid Distributed Cooperative Evolutionary Optimization for Large-Scale Flexible Job-Shop Scheduling Problem

Flexible job-shop scheduling problem (FJSP) is one of the most important optimization problem in manufacturing domain. With the development of manufacturing industry, the dimensionality of FJSP increases and its search space expands exponentially. It is hard to obtain a desired scheduling solution in an acceptable time using the traditional evolutionary algorithms. In this paper, we propose a hybrid distributed evolutionary model for large scale flexible job-shop scheduling problem (LSFJSP). The model is composed of two layers: division layer and coevolution layer. In the division layer, a grading mechanism and a Bayesian-grouping method are developed to decompose population and dimension respectively to obtain several subpopulation. In the coevolution layer, an individual migration mechanism and a probability selection mechanism for reference vector are used to achieve the coevolution among the subpopulations. Three typical evolutionary algorithms are integrated in the proposed model to test its superiority. The experimental results on the large scale instances of LSFJSP show that the evolutionary algorithms integrated in the proposed model have better optimization results and higher computational efficiency in comparison with the corresponding evolutionary algorithms in other distributed models.