A Novel Hybrid Whale Optimization Algorithm for Flexible Job-Shop Scheduling Problem

Abstract

The flexible job shop scheduling problem (FJSP) is an extension of the classical job shop scheduling problem and one of the more well-known NP-hard problems. To get better global optima of the FJSP, a novel hybrid whale optimization algorithm (HWOA) is proposed for solving FJSP, in which minimizing the makespan is considered as the objective. Firstly, the uniformity and extensiveness of the initial population distribution are increased with a good point set (GPS). Secondly, a new nonlinear convergence factor (NCF) is proposed for coordinating the weight of global and local search. Then, a new multi-neighborhood structure (MNS) is proposed, within which a total of three new neighborhoods are used to search for the optimal solution from different directions. Finally, a population diversity reception mechanism (DRM), which ensures to some extent that the population diversity is preserved with iteration, is presented. Seven international benchmark functions are used to test the performance of HWOA, and the results show that HWOA is more efficient. Finally, the HWOA is applied to 73 FJSP and four Ra international instances of different scales and flexibility, and the results further verify the effectiveness and superiority of the HWOA.