Hierarchical non-dominated sort: analysis and improvement

IF 1.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Genetic Programming and Evolvable Machines Pub Date : 2024-04-16 DOI:10.1007/s10710-024-09487-1
Ved Prakash, Sumit Mishra
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Abstract

Pareto dominance-based multiobjective evolutionary algorithms use non-dominated sorting to rank their solutions. In the last few decades, various approaches have been proposed for non-dominated sorting. However, the running time analysis of some of the approaches has some issues and they are imprecise. In this paper, we focus on one such algorithm namely hierarchical non-dominated sort (HNDS), where the running time is imprecise and obtain the generic equations that show the number of dominance comparisons in the worst and the best case. Based on the equation for the worst case, we obtain the worst-case running time as well as the scenario where the worst case occurs. Based on the equation for the best case, we identify a scenario where HNDS performs less number of dominance comparisons than that presented in the original paper, making the best-case analysis of the original paper unrigorous. In the end, we present an improved version of HNDS which guarantees the claimed worst-case time complexity by the authors of HNDS which is \({\mathcal {O}}(MN^2)\).

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分层非支配排序:分析与改进
基于帕累托优势的多目标进化算法使用非优势排序对其解决方案进行排序。在过去几十年中,人们提出了各种非支配排序方法。然而,其中一些方法的运行时间分析存在一些问题,而且不精确。在本文中,我们重点研究了运行时间不精确的分层非支配排序(HNDS)算法,并获得了显示最坏和最好情况下支配比较次数的通用方程。根据最坏情况下的等式,我们得到了最坏情况下的运行时间以及出现最坏情况的场景。根据最佳情况下的等式,我们确定了一种情况,即 HNDS 执行的优势比较次数少于原论文中的次数,从而使原论文中的最佳情况分析变得不严谨。最后,我们提出了 HNDS 的改进版本,它保证了 HNDS 作者声称的最坏情况下的时间复杂度,即 \({mathcal {O}}(MN^2)\).
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来源期刊
Genetic Programming and Evolvable Machines
Genetic Programming and Evolvable Machines 工程技术-计算机:理论方法
CiteScore
5.90
自引率
3.80%
发文量
19
审稿时长
6 months
期刊介绍: A unique source reporting on methods for artificial evolution of programs and machines... Reports innovative and significant progress in automatic evolution of software and hardware. Features both theoretical and application papers. Covers hardware implementations, artificial life, molecular computing and emergent computation techniques. Examines such related topics as evolutionary algorithms with variable-size genomes, alternate methods of program induction, approaches to engineering systems development based on embryology, morphogenesis or other techniques inspired by adaptive natural systems.
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