Dolphin Pod Optimization
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.
KeywordsDolphin 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 (https://www.dis.uniroma1.it/~lucidi/DFL/) administered by Dr Giampaolo Liuzzi.
- 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.Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Bristol (2008)Google Scholar
- 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
- 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
- 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
- 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