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New hybrid methods to resolve combinatorial optimization problems

Abstract

Over the last decades, studies concerning efficient metaheuristics has risen considerably among researchers in combinatorial optimization. Hybrid metaheuristics, in particular, has found the best results for many optimization problems. The main goal of this project is to develop and improve new alternatives for the hybrid Clustering Search method (CS), making it flexible, robust and efficient both in terms of the quality of the solutions and the computational effort. We intend to research this project through three different sub-research lines which include: the development of a new way of detecting promising areas, the designing of a multiobjective CS and the exploration of ways to parallelize the CS. These proposed methods are applied to resolve different optimization problems with many practical applications. The computational tests will use cases available in the literature and if possible, real cases using data from companies in the region. (AU)

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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)
CHAVES, A. A.; LORENA, L. A. N.; SENNE, E. L. F.; RESENDE, M. G. C. Hybrid method with CS and BRKGA applied to the minimization of tool switches problem. Computers & Operations Research, v. 67, p. 174-183, MAR 2016. Web of Science Citations: 11.
ARAUJO, ELISEU JUNIO; CHAVES, ANTONIO AUGUSTO; DE SALLES NETO, LUIZ LEDUINO; DE AZEVEDO, ANIBAL TAVARES. Pareto clustering search applied for 3D container ship loading plan problem. EXPERT SYSTEMS WITH APPLICATIONS, v. 44, p. 50-57, FEB 2016. Web of Science Citations: 5.

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