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一种使用非支配树改进非支配排序的新算法。

A New Algorithm Using the Non-Dominated Tree to Improve Non-Dominated Sorting.

机构信息

School of Engineering, University of Skövde, Skövde, 54134, Sweden

出版信息

Evol Comput. 2018 Spring;26(1):89-116. doi: 10.1162/EVCO_a_00204. Epub 2017 Jan 19.

Abstract

Non-dominated sorting is a technique often used in evolutionary algorithms to determine the quality of solutions in a population. The most common algorithm is the Fast Non-dominated Sort (FNS). This algorithm, however, has the drawback that its performance deteriorates when the population size grows. The same drawback applies also to other non-dominating sorting algorithms such as the Efficient Non-dominated Sort with Binary Strategy (ENS-BS). An algorithm suggested to overcome this drawback is the Divide-and-Conquer Non-dominated Sort (DCNS) which works well on a limited number of objectives but deteriorates when the number of objectives grows. This article presents a new, more efficient algorithm called the Efficient Non-dominated Sort with Non-Dominated Tree (ENS-NDT). ENS-NDT is an extension of the ENS-BS algorithm and uses a novel Non-Dominated Tree (NDTree) to speed up the non-dominated sorting. ENS-NDT is able to handle large population sizes and a large number of objectives more efficiently than existing algorithms for non-dominated sorting. In the article, it is shown that with ENS-NDT the runtime of multi-objective optimization algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) can be substantially reduced.

摘要

非支配排序是进化算法中常用的一种技术,用于确定群体中解的质量。最常见的算法是快速非支配排序(Fast Non-dominated Sort,FNS)。然而,该算法的性能随着种群规模的增长而下降。同样的缺点也适用于其他非支配排序算法,如基于二进制策略的有效非支配排序(Efficient Non-dominated Sort with Binary Strategy,ENS-BS)。为了克服这一缺点,提出了一种算法,即分治非支配排序(Divide-and-Conquer Non-dominated Sort,DCNS)。该算法在目标数量有限的情况下表现良好,但随着目标数量的增加,性能会下降。本文提出了一种新的、更有效的算法,称为基于非支配树的有效非支配排序(Efficient Non-dominated Sort with Non-Dominated Tree,ENS-NDT)。ENS-NDT 是 ENS-BS 算法的扩展,它使用一种新颖的非支配树(NDTree)来加速非支配排序。ENS-NDT 能够更有效地处理大规模的种群和大量的目标,优于现有的非支配排序算法。文章表明,使用 ENS-NDT 可以显著减少多目标优化算法(如非支配排序遗传算法 II(Non-Dominated Sorting Genetic Algorithm II,NSGA-II))的运行时间。

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