Volume 7, Issue 4, August 2014, Pages 733 - 747
Hybrid Multiobjective Differential Evolution Incorporating Preference Based Local Search
Ning Dong, Yuping Wang
Available Online 1 August 2014.
- https://doi.org/10.1080/18756891.2013.858906How to use a DOI?
- multiobjective optimization, hybrid differential evolution, preference, sparse region, dynamical adjustment
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Ning Dong AU - Yuping Wang PY - 2014 DA - 2014/08 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 - https://doi.org/10.1080/18756891.2013.858906 ID - Dong2014 ER -