Hybrid Global/Local Derivative-Free Multi-objective Optimization via Deterministic Particle Swarm with Local Linesearch View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-12-21

AUTHORS

Riccardo Pellegrini , Andrea Serani , Giampaolo Liuzzi , Francesco Rinaldi , Stefano Lucidi , Emilio F. Campana , Umberto Iemma , Matteo Diez

ABSTRACT

A multi-objective deterministic hybrid algorithm (MODHA) is introduced for efficient simulation-based design optimization. The global exploration capability of multi-objective deterministic particle swarm optimization (MODPSO) is combined with the local search accuracy of a derivative-free multi-objective (DFMO) linesearch method. Six MODHA formulations are discussed, based on two MODPSO formulations and three DFMO activation criteria. Forty five analytical test problems are solved, with two/three objectives and one to twelve variables. The performance is evaluated by two multi-objective metrics. The most promising formulations are finally applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions and compared to MODPSO and DFMO, showing promising results. More... »

PAGES

198-209

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-72926-8_17

DOI

http://dx.doi.org/10.1007/978-3-319-72926-8_17

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1099749007


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