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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Chaves, Antonio Augusto [1] ; Goncalves, Jose Fernando [2, 3] ; Nogueira Lorena, Luiz Antonio [1]
Total Authors: 3
Affiliation:
[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
Total Affiliations: 3
Document type: Journal article
Source: COMPUTERS & INDUSTRIAL ENGINEERING; v. 124, p. 331-346, OCT 2018.
Web of Science Citations: 3
Abstract

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)

FAPESP's process: 16/07135-7 - Development of a flexible hybrid method with automatic tuning of parameters
Grantee:Antônio Augusto Chaves
Support type: Scholarships abroad - Research