文献类型：期刊文献

英文题名：Frequent Pattern-Based Search: A Case Study on the Quadratic Assignment Problem

作者：Zhou, Yangming[1,2];Hao, Jin-Kao[3,4];Duval, Beatrice[3]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[3]Univ Angers, Dept Comp Sci, LERIA, F-49045 Angers, France;[4]Inst Univ France, F-75231 Paris, France

年份：2022

卷号：52

期号：3

起止页码：1503

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录：;EI(收录号：20220911728784);WOS:【SCI-EXPANDED(收录号:WOS:000756835400021)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61903144; in part by the Shanghai Sailing Program under Grant 19YF1412400; in part by the Macao Young Scholars Program under Grant AM2020011; in part by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China under Grant 2018AAA0101302; in part by the Fundamental Research Funds for the Central Universities of China under Grant 222201817006; and in part by the Funding from the Shenzhen Institute of Artificial Intelligence and Robotics for Society. This article was recommended by Associate Editor S. Xie.

语种：英文

外文关键词：Data mining; Sociology; Statistics; Search problems; Optimization methods; Machine learning; Combinatorial optimization; heuristic design; learning-driven optimization; pattern-based optimization; quadratic assignment

摘要：We present frequent pattern-based search (FPBS) that combines data mining and optimization. FPBS is a general-purpose method that unifies data mining and optimization within the population-based search framework. The method emphasizes the relevance of a modular- and component-based approach, making it applicable to optimization problems by instantiating the underlying components. To illustrate its potential for solving difficult combinatorial optimization problems, we apply the method to the well-known and challenging quadratic assignment problem. We show the computational results and comparisons on the hardest QAPLIB benchmark instances. This work reinforces the recent trend toward closer cooperations between the optimization methods and machine learning or data mining techniques.