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Dolphin Pod Optimization

A Nature-Inspired Deterministic Algorithm for Simulation-Based Design

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Machine Learning, Optimization, and Big Data (MOD 2017)

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Abstract

A novel nature-inspired, deterministic, global, and derivative-free optimization method, namely the dolphin pod optimization (DPO), is presented for solving simulation-based design optimization problems with costly objective functions. DPO is formulated for unconstrained single-objective minimization and 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 pod size and initialization, coefficient set, and box-constraint method, resulting in more than 140,000 optimization runs. The most promising setup is compared with deterministic particle swarm optimization, central force optimization, and DIviding RECTangles and finally applied to the optimization of a destroyer hull form for reduced resistance and improved seakeeping.

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Acknowledgements

The work is supported by ONRG, NICOP grant N62909-15-1-2016, under the administration of Dr Woei-Min Lin, Dr. Salahuddin Ahmed, and Dr. Ki-Han Kim, and by the Italian Flagship Project RITMARE.

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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 .

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Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Serani, A., Diez, M. (2018). Dolphin Pod Optimization. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-72926-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72925-1

  • Online ISBN: 978-3-319-72926-8

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