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中文摘要：
为了提高非劣解向Pareto最优面收敛的速度以及解的多样性,设计了一种新的杂交算子并改进了NS-GA-Ⅱ算法。在此算法中,采用中心均值重组算子策略增强算法全局快速搜索能力,以获得最佳的Pareto近似解,同时,改进NSGA-Ⅱ快速非支配排序和拥挤机制将父代与子代的双种群进行截短,确保最优解不会丢失并保证解的多样性。数据实验表明,该算法能在解的收敛性、分布性以及自适应程度上均表现较好。
英文摘要：
This paper proposed a novel multi-objective evolutionary algorithm based on a novel crossover operation and improves NSGA-Ⅱ,in order to heighten further rate of convergence of solutions to Pareto optimal front and ensure the diversity of optimal solution.In the algorithm,the crossover operator parameter was adaptive for enhancing the ability of global fast search to achieve better Pareto approximate solutions.Moreover,it improved fast ranking mechanisms and crowing distance sorting of NSGA-Ⅱ truncates the population set formed by the parents and the new points,in order to ensure the optimal solution not be lost and to ensure the diversity of optimal solution.The experimental results show that the proposed approach is able to effectively solve the real-parameter multi-objective problems and has better performance on convergence,diversity and the degree of controlling self-adaptive.
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