The purpose of this paper is to present two different optimisation approaches for a multi-objective problem applied to PeWEC (pendulum wave energy converter), which is a pitching platform converting energy from the oscillation of a pendulum contained in a sealed hull. Two techno-economic parameters the CapEx over Productivity ratio (CoP) and the Capture Width Ratio (CWR) have been adopted as conflicting objectives. Evolutionary algorithms have been used in order to perform the optimisation. The first optimisation method is the weighted sum genetic algorithm where the multi-objective problem is transformed into several single-objective problems. The second method is the NSGAII algorithm that optimise directly the Pareto front. The results show that the NSGAII is more efficient than the weighted sum GA algorithm by identifying a better Pareto front with fewer evaluation.

Multiobjective optimisation approaches applied to a wave energy converter design / Carapellese, F.; Sirigu, S. A.; Giorgi, G.; Bonfanti, M.; Mattiazzo, G.. - (2021), pp. 2114-1-2114-8. ( 14th European Wave and Tidal Energy Conference, EWTEC 2021 Plymouth (UK) 5-9 September 2021).

Multiobjective optimisation approaches applied to a wave energy converter design

Carapellese F.;Sirigu S. A.;Giorgi G.;Bonfanti M.;Mattiazzo G.
2021

Abstract

The purpose of this paper is to present two different optimisation approaches for a multi-objective problem applied to PeWEC (pendulum wave energy converter), which is a pitching platform converting energy from the oscillation of a pendulum contained in a sealed hull. Two techno-economic parameters the CapEx over Productivity ratio (CoP) and the Capture Width Ratio (CWR) have been adopted as conflicting objectives. Evolutionary algorithms have been used in order to perform the optimisation. The first optimisation method is the weighted sum genetic algorithm where the multi-objective problem is transformed into several single-objective problems. The second method is the NSGAII algorithm that optimise directly the Pareto front. The results show that the NSGAII is more efficient than the weighted sum GA algorithm by identifying a better Pareto front with fewer evaluation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2997985