Solving rotated multi-objective optimization problems using differential evolution

Iorio, A and Li, X 2004, 'Solving rotated multi-objective optimization problems using differential evolution', in G. Webb and X. Yu (ed.) AI 2004: Advances in Artificial Intelligence, Cairns, Australia, 24 November 2004, pp. 862-872.


Document type: Conference Paper
Collection: Conference Papers

Title Solving rotated multi-objective optimization problems using differential evolution
Author(s) Iorio, A
Li, X
Year 2004
Conference name Australian Joint Conference on Artifical Intelligence
Conference location Cairns, Australia
Conference dates 24 November 2004
Proceedings title AI 2004: Advances in Artificial Intelligence
Editor(s) G. Webb
X. Yu
Publisher Springer
Place of publication Berlin, Germany
Start page 862
End page 872
Total pages 11
Abstract This paper demonstrates that the self-adaptive technique of Differential Evolution (DE) can be simply used for solving a multi-objective optimization problem where parameters are interdependent. The real-coded crossover and mutation rates within the NSGA-II have been replaced with a simple Differential Evolution scheme, and results are reported on a rotated problem which has presented difficulties using existing Multi-objective Genetic Algorithms. The Differential Evolution variant of the NSGA-II has demonstrated rotational invariance and superior performance over the NSGA-II on this problem.
Subjects Artificial Intelligence and Image Processing not elsewhere classified
Keyword(s) multi-objective optimisation
differential evolution
genetic algorithms
DOI - identifier 10.1007/b104336
Copyright notice © Springer-Verlag Berlin Heidelberg 2004
ISBN 978-3-540-24059-4
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