Enhanced Biogeography-Based Optimization for Flow-Shop Scheduling

Flow-shop scheduling problem (FSP) is a well-known NP-hard combinatorial optimization problem that occurs in many practical applications. Traditional algorithms are only capable of solving small-size FSP instances, and thus many metaheuristic algorithms have been proposed for efficiently solving large-size instances. However, most existing algorithms still suffer from low convergence speed and/or premature convergence. In this paper, we propose an enhanced biogeography-based optimization (BBO) algorithm framework for FSP, which uses the largest ranked value representation for solution encoding, employs the NEH method to improve the initial population, and designs a reinsertion local search operator based on the job with the longest waiting time (JLWT) to enhance exploitation ability. We respectively use the original BBO migration, blended migration, hybrid BBO and DE migration, and ecogeography-based migration to implement the framework. Experimental results on test instances demonstrate the effectiveness of the proposed BBO algorithms, among which the ecogeography-based optimization (EBO) algorithm version exhibits the best performance.