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Development of a hybrid metaheuristic with adaptive control flow and parameters


The study of efficient metaheuristics to solve optimization problems has been the subject of much research by the scientific community. To obtain good results in terms of solution quality and computational time it is important to have a good configuration of the metaheuristic. This process of specifying control flow and parameter values of a method is a hard task. Thus, this project has as main idea the development and improvement of the adaptive Biased Random-key Genetic Algorithm (A-BRKGA) method to choose which components will be used and in which sequence (A-BRKGA flow) and which parameters to use while an instance of a problem is being solved. To this end, machine learning techniques and adaptive and reactive mechanisms will be studied to construct an A-BRKGA with online configuration of parameters and control flow. The goal is to generate an efficient algorithm to solve combinatorial optimization problems and make the code easy to reuse. In order to evaluate the proposed method, four optimization problems with industrial and logistical applications will be studied: field technician scheduling problem, multicommodity traveling salesman problem with priority prizes, two-stage capacitated facility location problem, and facility location problem with overlapping. The computational tests will use test problems available in the literature and real case studies. The method will be compared with state-of-the-art algorithms through statistical analysis. (AU)

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Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MAURI, GERALDO REGIS; BIAJOLI, FABRICIO LACERDA; RABELLO, ROMULO LOUZADA; CHAVES, ANTONIO AUGUSTO; RIBEIRO, GLAYDSTON MATTOS; LORENA, LUIZ ANTONIO NOGUEIRA. Hybrid metaheuristics to solve a multiproduct two-stage capacitated facility location problem. International Transactions in Operational Research, v. 28, n. 6, p. 3069-3093, NOV 2021. Web of Science Citations: 1.
DA SILVA, TIAGO TIBURCIO; CHAVES, ANTONIO AUGUSTO; YANASSE, HORACIO HIDEKI. A new multicommodity flow model for the job sequencing and tool switching problem. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, v. 59, n. 12, p. 3617-3632, JUN 18 2021. Web of Science Citations: 2.
ARAUJO, ELISEU J.; CHAVES, ANTONIO A.; LORENA, LUIZ A. N. A mathematical model for the coverage location problem with overlap control. COMPUTERS & INDUSTRIAL ENGINEERING, v. 146, AUG 2020. Web of Science Citations: 0.
DA SILVA, TIAGO TIBURCIO; CHAVES, ANTONIO AUGUSTO; YANASSE, HORACIO HIDEKI; LOUREIRO LUNA, HENRIQUE PACCA. The multicommodity traveling salesman problem with priority prizes: a mathematical model and metaheuristics. COMPUTATIONAL & APPLIED MATHEMATICS, v. 38, n. 4 DEC 2019. Web of Science Citations: 0.

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