Particle swarm optimization: an alternative in marine propeller optimization?
Journal article, 2017

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

marine propeller

cavitation constraints

Multi-objective optimization

particle swarm optimization

Author

Florian Vesting

Chalmers, Shipping and Marine Technology, Marine Technology

Rickard Bensow

Chalmers, Shipping and Marine Technology, Marine Technology

Engineering Optimization

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

Vol. 4 83 1-19

Driving Forces

Sustainable development

Innovation and entrepreneurship

Areas of Advance

Transport

Energy

Subject Categories

Computational Mathematics

Vehicle Engineering

Roots

Basic sciences

DOI

10.1080/0305215X.2017.1302438

More information

Created

10/8/2017