A Radial Estimation-of-Distribution Algorithm for the Job-Shop Scheduling Problem

The job-shop environment has been widely studied under different approaches. Its practical characteristics make its research interesting. Therefore, the job-shop scheduling problem continues being attractive in developing new evolutionary algorithms. In this paper, the authors propose a new estimation of distribution algorithm coupled with a radial probability function. The aforementioned radial function comes from the hydrogen element. This approach is proposed in order to build a competitive evolutionary algorithm for the job-shop scheduling problem. The key point is to exploit the radial probability distribution to construct offspring and to tackle the inconvenient of the EDAs (i.e., lack of diversity of the solutions and poor ability of exploitation). Various instances and numerical experiments are presented to illustrate and to validate this novel research. The results, obtained from this research, permit the conclusion that using radial probability distributions is an emerging field to develop new and efficient EDAs.