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Dolphin Pod Optimization

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Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10385))

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Abstract

A novel nature-inspired deterministic derivative-free global optimization method, namely the dolphin pod optimization (DPO), is presented for solving simulation-based design optimization problems with costly objective functions. DPO implements, using a deterministic approach, the global search ability provided by a cetacean intelligence metaphor. The method is intended for unconstrained single-objective minimization and is based on a simplified social model of a dolphin pod in search for food. A parametric analysis is conducted to identify the most promising DPO setup, using 100 analytical benchmark functions and three performance criteria, varying the algorithm parameters. The most promising setup is compared with a deterministic particle swarm optimization and a DIviding RECTangles algorithm, and applied to two hull-form optimization problems, showing a very promising performance.

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Acknowledgements

The present research is supported by the US Office of Naval Research Global, NICOP grant N62909-15-1-2016, administered by Dr Woei-Min Lin, and by the Italian Flagship Project RITMARE. The DIRECT algorithm was taken from the DFL, Derivative-Free Library (https://www.dis.uniroma1.it/~lucidi/DFL/) administered by Dr Giampaolo Liuzzi.

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Authors and Affiliations

  1. CNR-INSEAN, National Research Council-Marine Technology Research Institute, Rome, Italy

    Andrea Serani & Matteo Diez

Authors
  1. Andrea Serani
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  2. Matteo Diez
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Corresponding author

Correspondence to Andrea Serani .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Serani, A., Diez, M. (2017). Dolphin Pod Optimization. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-61824-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61823-4

  • Online ISBN: 978-3-319-61824-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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