Propeller optimization by interactive genetic algorithms and machine learning
Artikel i vetenskaplig tidskrift, 2021

Marine propeller design can be carried out with the aid of automated optimization, but experience shows that a such an approach has still been inferior to manual design in industrial scenarios. In this study, the automated propeller design optimization is evolved by integrating human–computer interaction as an intermediate step. An interactive optimization methodology, based on interactive genetic algorithms (IGAs), has been developed, where the blade designers systematically guide a genetic algorithm towards the objectives. The designers visualize and assess the shape of the blade cavitation and this evaluation is integrated in the optimization method. The IGA is further integrated with a support-vector machine model, in order to avoid user fatigue, IGA's main disadvantage. The results of the present study show that the IGA optimization searches solutions in a more targeted manner and eventually finds more non-dominated feasible designs that also show a good cavitation behaviour in agreement with designer preference.

cavitation constraints


interactive genetic algorithms

machine learning

support-vector machines


marine propeller design


Ioli Gypa

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

Marcus Jansson

Kongsberg Maritime

Krister Wolff

Chalmers, Mekanik och maritima vetenskaper, Fordonsteknik och autonoma system

Rickard Bensow

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

Ship Technology Research

0937-7255 (ISSN)

Vol. In press

SAILPROP – även seglande lastfartyg behöver en energieffektiv propeller

Trafikverket, 2020-01-01 -- 2021-12-31.


Hållbar utveckling






Människa-datorinteraktion (interaktionsdesign)

Strömningsmekanik och akustik

Marin teknik



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