{"title":"A causal framework for stochastic local search optimization algorithms","authors":"Alberto Franzin, Thomas Stützle","doi":"10.1016/j.cor.2025.107050","DOIUrl":null,"url":null,"abstract":"<div><div>Despite the multitude of optimization algorithms available in the literature and the various approaches that study them, understanding the behaviour of an optimization algorithm and explaining its results are fundamental open questions in artificial intelligence and operations research. We argue that the body of available literature is already very rich, and the main obstacle to advancements towards answering those questions is its fragmentation.</div><div>In this work, we focus on stochastic local search algorithms, a broad class of methods to compute good quality suboptimal solutions in a short time. We propose a causal framework that relates the entities involved in the solution of an optimization problem. We demonstrate how this conceptual framework can be used to relate many approaches aimed at understanding how stochastic local search algorithms work, and how it can be utilized to address open problems, both theoretical and practical.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107050"},"PeriodicalIF":4.1000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Operations Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305054825000784","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Despite the multitude of optimization algorithms available in the literature and the various approaches that study them, understanding the behaviour of an optimization algorithm and explaining its results are fundamental open questions in artificial intelligence and operations research. We argue that the body of available literature is already very rich, and the main obstacle to advancements towards answering those questions is its fragmentation.
In this work, we focus on stochastic local search algorithms, a broad class of methods to compute good quality suboptimal solutions in a short time. We propose a causal framework that relates the entities involved in the solution of an optimization problem. We demonstrate how this conceptual framework can be used to relate many approaches aimed at understanding how stochastic local search algorithms work, and how it can be utilized to address open problems, both theoretical and practical.
期刊介绍:
Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.