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Particle swarm optimization algorithm with environmental factors for clustering analysis

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Abstract

In view of the model of bird flocking, particle swarm optimization (PSO) is a promising method to tackle optimization. This study benefits from the fact that the living environment affects behaviors of the bird flocking. That is, a well-defined environmental factor can improve the performance of PSO. Thus, the environment factor is taken into account to inspire the robust behaviors of bird flocking in depth. Specifically, it not only can carry out effective searching in limited flying space, but also can strengthen the social behaviors of individual. In the field of clustering, it can be regarded as a search optimization issue. Like the utilization of some useful information generated in its process, environment factor is considered and environment factor-inspired PSO (EPSO) is proposed in this study. To take full advantage of EPSO for solving issue of clustering, we divide its process into two stages. In the first stage, the environment factor is imported as a refined search technology to achieve the multi-local optimums with high probability. In the second stage, the manifold information, i.e., individual, swarm and environment factors, is considered to improve its global search capacity. Such an approach can effectively overcome the defect of PSO being prone to being trapped in local optima. To demonstrate the validity of our approach, EPSO, conventional PSO, genetic algorithm, \(K\)-means, artificial bee colony and hybrid ABC are compared with benchmark document collections. The experiment results indicate that EPSO performs better than these state-of-the-art clustering algorithms in most cases.

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Acknowledgments

The authors thank the Editors and reviewers for providing very helpful comments and suggestions. Their insight and comments led to a better presentation of the ideas expressed in this paper. This work was sponsored by National Natural Science Foundation of China (61103129, 61170121), the fourth stage of Brain Korea 21 Project. Natural Science Foundation of Jiangsu Province (SBK201122266), SRF for ROCS, SEM, and the Specialized Research Fund for the Doctoral Program of Higher Education (20100093120004).

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Authors and Affiliations

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China

    Wei Song, Wei Ma & Yingying Qiao

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  1. Wei Song
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  2. Wei Ma
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  3. Yingying Qiao
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Correspondence to Wei Song.

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Communicated by C.-S. Lee.

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Song, W., Ma, W. & Qiao, Y. Particle swarm optimization algorithm with environmental factors for clustering analysis. Soft Comput 21, 283–293 (2017). https://doi.org/10.1007/s00500-014-1458-7

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