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.

Pellegrini, R., Serani, A., Liuzzi, G., Rinaldi, F., Lucidi, S., Campana, E.F., et al. (2018). Hybrid global/local derivative-free multi-objective optimization via deterministic particle swarm with local linesearch. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.198-209). Springer Verlag [10.1007/978-3-319-72926-8_17].

Hybrid global/local derivative-free multi-objective optimization via deterministic particle swarm with local linesearch

Serani, Andrea;Iemma, Umberto;DIEZ, Matteo
2018

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.
9783319729251
Pellegrini, R., Serani, A., Liuzzi, G., Rinaldi, F., Lucidi, S., Campana, E.F., et al. (2018). Hybrid global/local derivative-free multi-objective optimization via deterministic particle swarm with local linesearch. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.198-209). Springer Verlag [10.1007/978-3-319-72926-8_17].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/337601
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