Inconsistency reduction in pairwise comparison matrices using genetic algorithms

IF 2.8 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning Pub Date : 2026-04-01 Epub Date: 2026-01-06 DOI:10.1016/j.ijar.2026.109626

Atiyeh Sayadi, Ryszard Janicki

引用次数: 0

Abstract

This paper discusses inconsistency reduction in qualitative and quantitative multiplicative pairwise comparison matrices by applying efficient genetic algorithms in detail. Three new algorithms are presented and discussed. One for the classical quantitative multiplicative pairwise comparisons, and two for a formal version of qualitative pairwise comparisons. For the quantitative case, a distance-based inconsistency index (Koczkodaj’s index) is used. Moreover the effects of different factors on its efficiency and the quality of results are analyzed. For the qualitative case, no numbers are used, so evaluation functions are tailored to use qualitative relations. For both quantitative and qualitative cases, the genetic algorithms perform reliably, and in the qualitative case they show strong performance compared to the existing method we evaluated.

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利用遗传算法减少两两比较矩阵的不一致性

本文详细讨论了应用高效遗传算法减少定性和定量乘法两两比较矩阵的不一致。提出并讨论了三种新的算法。一个用于经典的定量乘法两两比较，两个用于定性两两比较的正式版本。对于定量情况，使用基于距离的不一致性指数（Koczkodaj指数）。分析了不同因素对其效率和结果质量的影响。对于定性案例，没有使用数字，因此评估函数被定制为使用定性关系。对于定量和定性案例，遗传算法都表现可靠，并且在定性案例中，与我们评估的现有方法相比，它们表现出强大的性能。

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

International Journal of Approximate Reasoning 工程技术-计算机：人工智能

CiteScore

6.90

自引率

12.80%

发文量

170

审稿时长

67 days

期刊介绍： The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.