Dolphin Pod Optimization

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


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.


Dolphin pod optimization Deterministic optimization Global optimization Derivative-free optimization 



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 ( administered by Dr Giampaolo Liuzzi.


  1. 1.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the Fourth IEEE Conference on Neural Networks, Piscataway, pp. 1942–1948 (1995)Google Scholar
  2. 2.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Bristol (2008)Google Scholar
  3. 3.
    Yang, X.S., Deb, S.: Cuckoo search via levy flights. In: Proceedings of World Congress on Nature and Biologically Inspired Computing (NaBic 2009), Coimbatore, India, pp. 210–214 (2009)Google Scholar
  4. 4.
    Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Gonzlez, J., Pelta, D., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) (NICSO 2010). Studies in Computational Intelligence, vol. 284, pp. 65–74. Springer, Berlin Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Formato, R.A.: Central force optimization: a new metaheuristic with applications in applied electromagnetics. Prog. Electromagn. Res. 77, 425–491 (2007)CrossRefGoogle Scholar
  6. 6.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009). Special Section on High Order Fuzzy SetsCrossRefzbMATHGoogle Scholar
  7. 7.
    Serani, A., Leotardi, C., Iemma, U., Campana, E.F., Fasano, G., Diez, M.: Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems. Appl. Soft Comput. 49, 313–334 (2016)CrossRefGoogle Scholar
  8. 8.
    Campana, E.F., Diez, M., Iemma, U., Liuzzi, G., Lucidi, S., Rinaldi, F., Serani, A.: Derivative-free global ship design optimization using global/local hybridization of the DIRECT algorithm. Optim. Eng. 17(1), 127–156 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Jones, D., Perttunen, C., Stuckman, B.: Lipschitzian optimization without the Lipschitz constant. J. Optim. Theory Appl. 79(1), 157–181 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Diez, M., et al.: Multi-objective hydrodynamic optimization of the DTMB 5415 for resistance and seakeeping. In: Proceedings of the 13th International Conference on Fast Sea Transportation, FAST 2015, Washington, D.C., USA (2015)Google Scholar
  11. 11.
    Diez, M., Campana, E.F., Stern, F.: Design-space dimensionality reduction in shape optimization by Karhunen-Loève expansion. Comput. Methods Appl. Mech. Eng. 283, 1525–1544 (2015)CrossRefGoogle Scholar
  12. 12.
    Diez, M., Serani, A., Campana, E.F., Volpi, S., Stern, F.: Design space dimensionality reduction for single- and multi-disciplinary shape optimization. In: AIAA/ISSMO Multidisciplinary Analysis and Optimization (MA&O), AVIATION 2016, Washington D.C., USA, 13–17 June 2016Google Scholar
  13. 13.
    Serani, A., Fasano, G., Liuzzi, G., Lucidi, S., Iemma, U., Campana, E.F., Stern, F., Diez, M.: Ship hydrodynamic optimization by local hybridization of deterministic derivative-free global algorithms. Appl. Ocean Res. 59, 115–128 (2016)CrossRefzbMATHGoogle Scholar
  14. 14.
    Bassanini, P., Bulgarelli, U., Campana, E.F., Lalli, F.: The wave resistance problem in a boundary integral formulation. Surv. Math. Indus. 4, 151–194 (1994)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Meyers, W.G., Baitis, A.E.: SMP84: improvements to capability and prediction accuracy of the standard ship motion program SMP81. Technical report SPD-0936-04, David Taylor Naval Ship Research and Development Center, September 1985Google Scholar

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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