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Artificial Bee Colony Algorithm Based on Clustering Method and Its Application for Optimal Power Flow Problem

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Bio-inspired Computing – Theories and Applications (BIC-TA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 682))

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Abstract

In this paper, an improved multi-objective ABC algorithm based on k-means clustering, called CMOABC, is proposed. For keeping the population diversity, the multi-swarm technology based on k-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iterations, the population will be re-clustered to facilitate information exchange among different clusters. CMOABC is applied to solve the real-world Optimal Power Flow (OPF) problem that considers the cost, loss, and emission impacts as the objective functions. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.

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Authors and Affiliations

  1. Tianjin Polytechnic University, Tianjin, 300387, China

    Liling Sun & Hanning Chen

Authors
  1. Liling Sun
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  2. Hanning Chen
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Corresponding author

Correspondence to Hanning Chen .

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Editors and Affiliations

  1. Xidian University , Xi’an, China

    Maoguo Gong

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Southwest Jiaotong University , Chengdu, China

    Gexiang Zhang

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© 2016 Springer Nature Singapore Pte Ltd.

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Sun, L., Chen, H. (2016). Artificial Bee Colony Algorithm Based on Clustering Method and Its Application for Optimal Power Flow Problem. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_13

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  • DOI: https://doi.org/10.1007/978-981-10-3614-9_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3613-2

  • Online ISBN: 978-981-10-3614-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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