A Hybrid Chaotic Biogeography Based Optimization for the Sequence Dependent Setup Times Flowshop Scheduling Problem With Weighted Tardiness Objective

This paper proposes a hybrid chaotic biogeography-based optimization (HCBBO) for solving the sequence dependent setup times flowshop scheduling problem with the objective of minimizing the total weighted tardiness. First of all, a largest-order-value rule is employed to transform continuous vectors into discrete job permutations. Second, the chaotic theory and the problem-specific Nawaz-Enscore-Ham heuristic are applied to compose the initial population with the property of intensification and diversification. Third, an improved biogeography-based optimization is introduced to improve the global search ability by designing new migration and mutation schemes. Meanwhile, a further local search is proposed and embedded in HCBBO to enhance the quality of the elite habitats. In addition, an effective perturbation is applied to avoid the solutions getting trapped in the local optima. Computation comparison experiments of seven benchmark algorithms on the Taillard benchmark problems are provided to verify the efficiency of the proposed algorithm. From the experiment results, it can conclude that HCBBO beats other compared algorithms effectively with higher quality and robustness solutions.