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- W3128055378 abstract "The balanced minimum sum-of-squares clustering problem (BMSSC) is a NP-hard combinatorial optimization problem with many important applications. In order to solve this computationally challenging problem, we propose an efficient local optimization method called the iterated variable neighborhood search (IVNS) algorithm in this paper. The proposed IVNS algorithm combines a randomized construction procedure to generate a high-quality initial solution, a variable neighborhood descent method to improve the assignment of data points, and an updating procedure to calculate the centers of the clusters. The experimental results on a set of benchmarks widely used in the literature show that the proposed algorithm outperforms two related algorithms in the literature in terms of computational speed. Specifically, for the most benchmark instances tested, the proposed IVNS algorithm is at least 100 times faster compared with two reference algorithms from the literature, while the objective values obtained from the proposed IVNS algorithm is comparable with that of the reference algorithms." @default.
- W3128055378 created "2021-02-15" @default.
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- W3128055378 date "2020-11-06" @default.
- W3128055378 modified "2023-10-07" @default.
- W3128055378 title "An iterated variable neighborhood search algorithm for the balanced minimum sum-of-squares clustering problem" @default.
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- W3128055378 doi "https://doi.org/10.1109/cac51589.2020.9327415" @default.
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