Particle swarm optimization: an alternative in marine propeller optimization?
Artikel i vetenskaplig tidskrift, 2018

This article deals with improving and evaluating the performance of two evolutionary algorithm approaches for automated engineering design optimization. Here a marine propeller design with constraints on cavitation nuisance is the intended application. For this purpose, the particle swarm optimization (PSO) algorithm is adapted for multi-objective optimization and constraint handling for use in propeller design. Three PSO algorithms are developed and tested for the optimization of four commercial propeller designs for different ship types. The results are evaluated by interrogating the generation medians and the Pareto front development. The same propellers are also optimized utilizing the well established NSGA-II genetic algorithm to provide benchmark results. The authors' PSO algorithms deliver comparable results to NSGA-II, but converge earlier and enhance the solution in terms of constraints violation.

NSGA-II

particle swarm optimization

cavitation constraints

marine propeller

Multi-objective optimization

Författare

[Person 1bcfd85c-a44d-4be8-9a1b-4f6468e9eac5 not found]

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

[Person 73248e9d-58f8-42bc-ab81-dd9410f54ecd not found]

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

Engineering Optimization

0305-215X (ISSN) 1029-0273 (eISSN)

Vol. 4 83 1-19

Drivkrafter

Hållbar utveckling

Innovation och entreprenörskap

Styrkeområden

Transport

Energi

Ämneskategorier

Beräkningsmatematik

Farkostteknik

Fundament

Grundläggande vetenskaper

DOI

10.1080/0305215X.2017.1302438

Mer information

Senast uppdaterat

2020-03-03