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Evaluation of Harmony Search and Differential Evolution Optimization Algorithms on Solving the Booster Station Optimization Problems in Water Distribution Networks

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Recent Advances in Swarm Intelligence and Evolutionary Computation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 585))

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Abstract

Disinfection of water in distribution networks is usually achieved by chlorine injection at outlet of the treatment plants. However, such disinfection applications cause non-uniform chlorine residuals since chlorine decays during its propagation in the network. To maintain chlorine residuals within allowable limits, additional chlorine injection locations called as the booster stations are installed at some strategic locations of distribution networks. Therefore, estimation of the numbers, locations, and injection rates of the booster stations becomes an important problem. For this purpose, this chapter evaluates the performance of Harmony Search (HS) and Differential Evolution (DE) optimization algorithms for solving the booster station optimization problems.

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Acknowledgments

The work given in this chapter is supported by The Turkish Academy of Sciences (TÜBA)—The Young Scientists Award Programme (GEBIP). The second author thanks TÜBA for their support of this study.

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Authors and Affiliations

  1. Department of Civil Engineering, Afyon Kocatepe University, Afyonkarahisar, Turkey

    Şerife Gökçe

  2. Department of Civil Engineering, Pamukkale University, Denizli, Turkey

    M. Tamer Ayvaz

Authors
  1. Şerife Gökçe
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  2. M. Tamer Ayvaz
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Correspondence to M. Tamer Ayvaz .

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Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

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Gökçe, Ş., Ayvaz, M.T. (2015). Evaluation of Harmony Search and Differential Evolution Optimization Algorithms on Solving the Booster Station Optimization Problems in Water Distribution Networks. In: Yang, XS. (eds) Recent Advances in Swarm Intelligence and Evolutionary Computation. Studies in Computational Intelligence, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-319-13826-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-13826-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13825-1

  • Online ISBN: 978-3-319-13826-8

  • eBook Packages: EngineeringEngineering (R0)

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