A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem

Abstract

Biogeography-based optimization (BBO) algorithm is a new kind of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. In this paper, the BBO algorithm is developed for flexible job shop scheduling problem (FJSP). It means that migration operators of BBO are developed for searching a solution area of FJSP and finding the optimum or near-optimum solution to this problem. In fact, the main aim of this paper was to provide a new way for BBO to solve scheduling problems. To assess the performance of BBO, it is also compared with a genetic algorithm that has the most similarity with the proposed BBO. This similarity causes the impact of different neighborhood structures being minimized and the differences among the algorithms being just due to their search quality. Finally, to evaluate the distinctions of the two algorithms much more elaborately, they are implemented on three different objective functions named makespan, critical machine work load, and total work load of machines. BBO is also compared with some famous algorithms in the literature.