Fish Migration Optimization with Dynamic Grouping Strategy for Solving Job-Shop Scheduling Problem

Abstract

Aiming at the job-shop scheduling problem (JSP), a dynamic grouping fish migration optimization (DFMO) is proposed to solve it. The DFMO algorithm adopts a multi-group structure to improve the convergence ability of the algorithm. And the opposition-based learning (OBL) strategy is applied to the group with poor overall fitness value to improve its solving environment. This paper proposes three different communication strategies to exchange information between different groups. In order to better determine the communication time between groups, a dynamic detection method based on population diversity is proposed. Compared with the static method of determining the communication time between groups, the proposed method can make the group more fully explore the current area and more hopefully find the optimal solution. The experiment in this paper is divided into two parts, one part is the numerical experiment test, the other part is the JSP problem standard library test. From the experimental results, the DFMO algorithm can obtain good results in both parts of the experiment, and has a good problem optimization ability.