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Dolphin Pod Optimization

  • Andrea SeraniEmail author
  • Matteo Diez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)

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.

Keywords

Dolphin pod optimization Deterministic optimization Global optimization Derivative-free optimization 

Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.CNR-INSEAN, National Research Council-Marine Technology Research InstituteRomeItaly

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