Characterizing Permutation-Based Combinatorial Optimization Problems in Fourier Space

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Anne Elorza;Leticia Hernando;Jose A. Lozano
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引用次数: 1

Abstract

Comparing combinatorial optimization problems is a difficult task. They are defined using different criteria and terms: weights, flows, distances, etc. In spite of this apparent discrepancy, on many occasions, they tend to produce problem instances with similar properties. One avenue to compare different problems is to project them onto the same space, in order to have homogeneous representations. Expressing the problems in a unified framework could also lead to the discovery of theoretical properties or the design of new algorithms. This article proposes the use of the Fourier transform over the symmetric group as the tool to project different permutation-based combinatorial optimization problems onto the same space. Based on a previous study (Kondor, 2010), which characterized the Fourier coefficients of the quadratic assignment problem, we describe the Fourier coefficients of three other well-known problems: the symmetric and nonsymmetric traveling salesperson problem and the linear ordering problem. This transformation allows us to gain a better understanding of the intersection between the problems, as well as to bound their intrinsic dimension.
傅立叶空间中基于置换的组合优化问题的表征
比较组合优化问题是一项艰巨的任务。它们使用不同的标准和术语来定义:重量、流量、距离等。尽管存在这种明显的差异,但在许多情况下,它们往往会产生具有类似性质的问题实例。比较不同问题的一种方法是将它们投射到同一空间,以获得齐次表示。在一个统一的框架中表达问题也可能导致发现理论性质或设计新的算法。本文提出使用对称群上的傅里叶变换作为工具,将不同的基于排列的组合优化问题投影到同一空间上。基于先前的研究(Kondor, 2010),该研究表征了二次分配问题的傅里叶系数,我们描述了其他三个著名问题的傅里叶系数:对称和非对称旅行销售人员问题以及线性排序问题。这种转换使我们能够更好地理解问题之间的交集,并限定它们的内在维度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
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