A Hybrid Escalating Evolutionary Algorithm for Multi-objective Flow-Shop Scheduling

A hybrid escalating evolutionary algorithm, which aims at solving multi-objective flow-shop scheduling problems, is proposed in this paper. The new algorithm takes an escalating evolutionary structure, which helps it escaping from premature, and an elitism strategy is introduced to improve its convergence, besides, a problem-dependent meta- heuristic variable local search strategy is adopted to enhance its local search ability. To assess the performance and demonstrate the effectiveness of the new algorithm, a series of standard bi-objective test problems and a typical tri-objective case study are employed. Empirical results have shown that, our new algorithm has outperformed some well-known algorithms like NSGA-II, ENGA and MOGLS.