IEEJ Transactions on Electronics, Information and Systems
Online ISSN : 1348-8155
Print ISSN : 0385-4221
ISSN-L : 0385-4221
<Softcomputing, Learning>
Local Descent Direction Vector Based Differential Evolution for Multiobjective Optimization
Daichi KamiyamaKenichi TamuraKeiichiro Yasuda
Author information
JOURNAL FREE ACCESS

2012 Volume 132 Issue 8 Pages 1356-1361

Details
Abstract
Differential Evolution (DE) is an effective optimization method for global continuous optimization problems. Recently, we developed Local Descent Direction Vector Based Differential Evolution (LDDVDE) which uses local descent direction vectors based on the operation vectors in order to improve local search performance of DE. In this paper, we extend LDDVDE to multiobjective optimization problems. We adopt Hyper-Volume indicator to order the operation vectors to make the local descent direction vectors for the case of multiobjective optimization problems. The effectiveness of the proposed method is confirmed through some numerical experiments for typical benchmark problems.
Content from these authors
© 2012 by the Institute of Electrical Engineers of Japan
Previous article Next article
feedback
Top