基于正交设计和自适应聚类剪枝策略的改进多目标遗传算法
An Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy
摘要：
多目标进化算法的两个重要特征是分布性和收敛性。作为一种经典的多目标遗传算法，NSGA-Ⅱ被广泛应用于多目标优化领域。然而，在NSGA-Ⅱ中，随机的种群初始化和基于距离的种群保持策略不能很好地保持种群的分布或收敛性。针对这两个缺陷，本文提出了一种改进算法OTNSGA-II II，该算法在分布性和收敛性方面具有更好的性能。该算法采用正交试验法，以一种新的不连续非支配排序和拥挤距离的方式选择个体，产生初始种群。提出了一种基于聚类的剪枝策略，根据聚类结果的类内聚集程度，自适应地对特征相似、非支配排序和拥挤距离表现较差或远离Pareto前沿的个体进行剪枝。新的剪枝策略使种群更容易收敛到Pareto前沿，并保持种群的分布。在不同类型的测试函数上对OTNSGA-II和NSGA-II进行了比较，验证了OTNSGA-II在分布性和收敛性方面的改进。
英文摘要：
Two important characteristics of multi-objective evolutionary algorithms are distribution and convergency. As a classic multi-objective genetic algorithm, NSGA-II is widely used in multi-objective optimization fields. However, in NSGA-II, the random population initialization and the strategy of population maintenance based on distance cannot maintain the distribution or convergency of the population well. To dispose these two deficiencies, this paper proposes an improved algorithm, OTNSGA-II II, which has a better performance on distribution and convergency. The new algorithm adopts orthogonal experiment, which selects individuals in manner of a new discontinuing non-dominated sorting and crowding distance, to produce the initial population. And a new pruning strategy based on clustering is proposed to self-adaptively prunes individuals with similar features and poor performance in non-dominated sorting and crowding distance, or to individuals are far away from the Pareto Front according to the degree of intra-class aggregation of clustering results. The new pruning strategy makes population to converge to the Pareto Front more easily and maintain the distribution of population. OTNSGA-II and NSGA-II are compared on various types of test functions to verify the improvement of OTNSGA-II in terms of distribution and convergency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
