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

Author:

Chaves Antônio Augusto,Gonçalves José Fernando,Lorena Luiz Antonio Nogueira

Funder

FAPESP

CNPq

North Portugal Regional Operational Programme

European Regional Development Fund

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference57 articles.

1. Fine-tuning of algorithms using fractional experimental designs and local search;Adenso-Diaz;Operations Research,2006

2. Arabas, J., Michalewicz, Z., Mulawka, J., 1994. Gavaps-a genetic algorithm with varying population size. In Proceedings of the first IEEE conference on evolutionary computation, 1994. IEEE world congress on computational intelligence (pp. 73–78).

3. Araújo, E. J. (2016). Hybrid method with detection of promising areas based on density for point feature cartographic label placement. Master’s thesis, Federal University of São Paulo, São José dos Campos, Brazil, 84 p.

4. Improving the performance of metaheuristics: An approach combining response surface methodology and racing algorithms;Barbosa;International Journal of Engineering Mathematics,2015

5. Batista, P. L. F., Negreiros, M., & Palhano, A. (2014). Solvers resolution perspective to the capacitated centred clustering problem. In Proceedings of the XLVI symposium of the Brazilian operational research society (pp. 2458–2468).

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