An improved discrete artificial bee colony algorithm to minimize the makespan on hybrid flow shop problems

As a typical NP-hard combinatorial optimization problem, the hybrid flow shop (HFS) problem is widely existing in manufacturing systems. In this article, the HFS problem is modeled by vector representation, and then an improved discrete artificial bee colony (IDABC) algorithm is proposed for this problem to minimize the makespan. The proposed IDABC algorithm combines a novel differential evolution and a modified variable neighborhood search to generate new solutions for the employed and onlooker bees, and the destruction and construction procedures are used to obtain solutions for the scout bees. Moreover, an orthogonal test is applied to efficiently configure the system parameters, after a small number of training trials. The simulation results demonstrate that the proposed IDABC algorithm is effective and efficient comparing with several state-of-the-art algorithms on the same benchmark instances.