A hybrid discrete biogeography-based optimization for the permutation flow shop scheduling problem

The permutation flow shop scheduling problem (PFSP) which is known to be NP-hard has been widely investigated in recent years. In this paper, an effective hybrid discrete biogeography-based optimization (HDBBO) algorithm is proposed for solving the PFSP with the objective to minimise the makespan. Opposition-based learning method and the NEH heuristic are utilised in the HDBBO to generate an initial population with certain quality and diversity. Moreover, a novel variable local search strategy is presented and incorporated within the biogeography-based optimization framework to improve the exploitation ability. Computational results on two typical benchmark suits and comparisons with some state-of-the-art algorithms are presented to show the effectiveness of the HDBBO scheme.