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2018, Machine Learning, Optimization, and Big Data Third International Conference, MOD 2017 Volterra, Italy, September 14 – 17, 2017 Revised Selected Papers, Pages 198-209 (volume: 10710)

Hybrid global/local derivative-free multi-objective optimization via deterministic particle swarm with local linesearch (04b Atto di convegno in volume)

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

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.
ISBN: 9783319729251; 978-3-319-72926-8
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