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- W4241729440 abstract "The balanced minimum sum-of-squares clustering problem (BMSSC) is an important combinatorial optimization problem with many applications. The goal of this problem is to divide N data points in Euclidean space into K clusters such that the sizes of clusters differ at most by one. In this paper, we propose a memetic algorithm (denoted by MABMSSC) for the BMSSC problem. The algorithm employs an iterated variable neighborhood search algorithm (IVNS) as its local optimization method and a crossover operator to generate the offspring solutions. The experimental results on a number of benchmark instances show that the proposed algorithm is very efficient in terms of solution-quality. In particular, the proposed algorithm obtains a better solution compared with the best performing algorithm in the literature for many instances tested." @default.
- W4241729440 created "2022-05-12" @default.
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- W4241729440 date "2021-10-22" @default.
- W4241729440 modified "2023-09-27" @default.
- W4241729440 title "A memetic algorithm for the balanced minimum sum-of-squares clustering problem" @default.
- W4241729440 doi "https://doi.org/10.1109/cac53003.2021.9727258" @default.
- W4241729440 hasPublicationYear "2021" @default.
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