Hybrid Multiobjective Differential Evolution Incorporating Preference Based Local Search

DOI

10.1080/18756891.2013.858906How to use a DOI?

Keywords

multiobjective optimization, hybrid differential evolution, preference, sparse region, dynamical adjustment

Abstract

The performance of Differential Evolution (DE) for multiobjective optimization problems (MOPs) can be greatly enhanced by hybridizing with other techniques. In this paper, a new hybrid DE incorporating preference based local search is proposed. In every generation, a set of nondominated solutions is generated by DE operation. Usually these solutions distribute unevenly along the obtained nondominated set. To get solutions in the sparse region of the nondominated set, a mini population and preference based local search algorithm is specifically designed, and is used to exploit the sparse region by optimizing an achievement scalarizing function (ASF) with the dynamically adjusted reference point. As a result, multiple solutions in the sparse region can be obtained. Moreover, to retain uniformly spread nondominated solutions, an improved ε-dominance strategy, which would not delete the extreme points found during the evolution, is proposed to update the external archive set. Finally, numerical results and comparisons demonstrate the efficiency of the proposed algorithm.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Ning Dong
AU  - Yuping Wang
PY  - 2014
DA  - 2014/08/01
TI  - Hybrid Multiobjective Differential Evolution Incorporating Preference Based Local Search
JO  - International Journal of Computational Intelligence Systems
SP  - 733
EP  - 747
VL  - 7
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.858906
DO  - 10.1080/18756891.2013.858906
ID  - Dong2014
ER  -