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Development of a flexible hybrid method with automatic tuning of parameters

Grant number: 16/07135-7
Support type:Scholarships abroad - Research
Effective date (Start): January 09, 2017
Effective date (End): November 08, 2017
Field of knowledge:Engineering - Production Engineering - Operational Research
Principal Investigator:Antônio Augusto Chaves
Grantee:Antônio Augusto Chaves
Host: Jose Fernando Goncalves
Home Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
Local de pesquisa : Universidade do Porto (UP), Portugal  

Abstract

The study of efficient metaheuristics to solve optimization problems has been the subject of research by the scientific community. To find good results in terms of solutions of quality and computational time is important to define which parameter setting should be used. This process is often hard due to relation between the parameters, the best values for the parameters depend on the problem, the instances and the search time. Thus, the main idea of this project is improve the hybrid Clustering Search method (CS), using the concept of random keys by the Biased Random-Key Genetic Algorithms (BRKGA) and studying new automated ways to detect promising regions and change the parameter values during the search. We intend to design a method with easiness of implementation and robustness in terms of quality of solution and search time. To analyze the performance of the proposed method we will solve two optimization problems: berth allocation and cranes scheduling problem and point-feature cartographic label placement problem. In the evaluation we should be used instances available in public libraries and, if possible, some real-life instances. Methods from statistical analysis will be use to conduct the performance assessment of the designed method.

Scientific publications
(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)
ARAUJO, ELISEU J.; CHAVES, ANTONIO A.; LORENA, LUIZ A. N. Improving the Clustering Search heuristic: An application to cartographic labeling. APPLIED SOFT COMPUTING, v. 77, p. 261-273, APR 2019. Web of Science Citations: 0.
BIAJOLI, FABRICIO LACERDA; CHAVES, ANTONIO AUGUSTO; NOGUEIRA LORENA, LUIZ ANTONIO. A biased random-key genetic algorithm for the two-stage capacitated facility location problem. EXPERT SYSTEMS WITH APPLICATIONS, v. 115, p. 418-426, JAN 2019. Web of Science Citations: 5.
CHAVES, ANTONIO AUGUSTO; GONCALVES, JOSE FERNANDO; NOGUEIRA LORENA, LUIZ ANTONIO. Adaptive biased random-key genetic algorithm with local search for the capacitated centered clustering problem. COMPUTERS & INDUSTRIAL ENGINEERING, v. 124, p. 331-346, OCT 2018. Web of Science Citations: 3.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.