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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Adaptive biased random-key genetic algorithm with local search for the capacitated centered clustering problem

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Autor(es):
Chaves, Antonio Augusto [1] ; Goncalves, Jose Fernando [2, 3] ; Nogueira Lorena, Luiz Antonio [1]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Fed Sao Paulo, BR-12231280 Sao Jose Dos Campos - Brazil
[2] Univ Porto, INESC TEC, Porto - Portugal
[3] Univ Porto, Fac Econ, Porto - Portugal
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: COMPUTERS & INDUSTRIAL ENGINEERING; v. 124, p. 331-346, OCT 2018.
Citações Web of Science: 3
Resumo

This paper proposes an adaptive Biased Random-key Genetic Algorithm (A-BRKGA), a new method with on-line parameter control for combinatorial optimization problems. A-BRKGA has only one problem-dependent component, the decoder and all other parts can be reused. To control diversification and intensification, a novel adaptive strategy for parameter tuning is introduced. This strategy is based on deterministic rules and self adaptive schemes. For exploitation of specific regions of the solution space we propose a local search in promising communities. The proposed method is evaluated on the Capacitated Centered Clustering Problem (CCCP), which is an NP-hard problem where a set of n points, each having a given demand, is partitioned into m clusters each with a given capacity. The objective is to minimize the sum of the Euclidean distances between the points and their geometric cluster centroids. Computational results show that the A-BRKGA with local search is competitive with other methods of literature. (AU)

Processo FAPESP: 16/07135-7 - Desenvolvimento de um método híbrido flexível com calibração automática de parâmetros
Beneficiário:Antônio Augusto Chaves
Linha de fomento: Bolsas no Exterior - Pesquisa