Advanced search
Start date
(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

Full text
Chaves, Antonio Augusto [1] ; Goncalves, Jose Fernando [2, 3] ; Nogueira Lorena, Luiz Antonio [1]
Total Authors: 3
[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

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