A Hybrid Escalating Multi-objective Evolutionary Algorithm with Its Application to Flow Shop Problems
混合递进多目标进化算法及其在flow shop 排序中的应用

A hybrid escalating multi-objective evolutionary algorithm(HEMEA),which has a new evolution structure compared with the existing ones,was proposed in this paper.The new algorithm enhanced the efficiency of optimization by using an innovative escalating evolutionary scheme with an elitism selection and variable Pareto local search strategy.A series of bi-objective flow shop optimization problems from OR-Library and one typical tri-objective flow shop optimization problem which was first studied in Bagchi's work,were re-optimized by NSGA-II,MOGLS,ENGA and our HEMEA respectively.The comparison of the optimization results have shown the outstanding performance of HEMEA with respect to the others',which were well-known for their good performance in multi-objective evolutionary computation.thus,the effectiveness and efficiency of HEMEA was demonstrated.