New hybrid method with detection of promising areas for combinatorial optimization problems

• Grant number: 14/00580-0
• Support Opportunities: Scholarships in Brazil - Master
• Effective date (Start): April 01, 2014
• Effective date (End): February 29, 2016
• Field of knowledge: Engineering - Production Engineering - Operational Research
• Principal Investigator: Antônio Augusto Chaves
• Grantee: Eliseu Júnio Araújo
• Host Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
• Associated research grant: 12/17523-3 - New hybrid methods to resolve combinatorial optimization problems, AP.JP

Abstract

Over the recent years, meta-heuristics have been the subject of research aimed at finding those with greatest efficiency to solve optimization problems. It was noted during this exploration, the hybrid meta-heuristics are a good choice to accentuate the qualities of these methods. This project is centered on the hybrid method Clustering Search (CS), focusing on the improvement and development of the new alternative for it, seeking it becomes an efficient, robust and flexible in terms of quality of solutions as well as in computational time. The CS seeks to combine metaheuristics and local search heuristics, intensifying the search in regions of the search space considered promising. In this project will be proposed a new way to detect promising areas, based on different clustering algorithms and neural networks. This method will be applied to solve two combinatorial optimization problems with many practical applications, the periodic vehicle routing applied to solid waste collection and the hierarchical traveling salesman problem. In computational tests will be used available instances in the literature, and, if possible, real instances obtained from the companies located in the region. (AU)

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. (16/07135-7, 14/00580-0)