Abstract
Cooperative coevolution (CC) was used to improve the performance of evolutionary algorithms (EAs) on complex optimization problems in a divide-and-conquer way. In this paper, we show that the CC framework can be very helpful to improve the performance of particle swarm optimization (PSO) on clustering high-dimensional datasets. Based on CC framework, the original partitional clustering problem is first decomposed to several subproblems, each of which is then evolved by an optimizer independently. We employ a very simple but efficient optimization algorithm, namely bare-bone particle swarm optimization (BPSO), as the optimizer to solve each subproblem cooperatively. In addition, we design a new centroid-based encoding schema for each particle and apply the Chernoff bounds to decide a proper population size. The experimental results on synthetic and real-life datasets illustrate the effectiveness and efficiency of the BPSO and CC framework. The comparisons show the proposed algorithm significantly outperforms five EA-based clustering algorithms, i.e., PSO, SRPSO, ACO, ABC and DE, and K-means on most of the datasets.
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Acknowledgments
This work is partly supported by the National Natural Science Foundation of China under Grant Nos. 61074054, 61070135 and Zhejiang Provincial Natural Science Foundation of under Grant Nos. Q13F030023 LY 13F030010 and LZ13F020002.
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Communicated by G. Acampora.
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Jiang, B., Wang, N. Cooperative bare-bone particle swarm optimization for data clustering. Soft Comput 18, 1079–1091 (2014). https://doi.org/10.1007/s00500-013-1128-1
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DOI: https://doi.org/10.1007/s00500-013-1128-1