关键词: Pareto层级；网格拥挤度, 自适应选择, 个体排斥机制；邻域搜索

Abstract:

Three objectives were expected to be achieved simultaneously when sequencing automobiles in process,including equaling the spare parts consumption,minimizing the total  adjustment cost resulting from exchanging automobile models,calibrating the work position for each  automobile on any station.A new hybrid multi-objective grid genetic algorithm(HmoGA) was proposed based on  Pareto stratum. In the algorithm,a new rejection mechanism was first  considered in the sorting process of Pareto stratum,for the purpose of getting the even distribution of the non-dominated solutions.Then an adaptive grid selection scheme was  designed by integrating  Pareto stratum evaluation,crowding  degree  calculation and distance estimation among adjacent individuals.Thus,higher quality population can be generated,better parent chromosomes can be  selected,and the distribution of the Pareto front can be improved constantly. Finally,the 2-opt shift operator was hybridized into the proposed genetic algorithm so as to broaden the search space and escape from local optimum.Three groups of experiments have done and three metrics including  non-dominated ratio,the number of Pareto optimal solutions and the deviation of distances  between neighbors were used as the performance measures.The results reveal that the proposed HmoGA dramatically outperforms NSGA-Ⅱ in terms of convergence and diversification.

Key words: Pareto , stratum;crowding degree;adaptive grid selection;rejection mechanism;local , search