Cited 0 time in
Cited 1 time in
A hybrid evolutionary algorithm for multiobjective optimization
- A hybrid evolutionary algorithm for multiobjective optimization
- Ahn, Chang Wook; Kim, Hyun Tae; Kim, Ye Hoon; An, Jin Ung
- DGIST Authors
- An, Jin Ung
- Issue Date
- 2009 4th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2009, 19-23
- Article Type
- Conference Paper
- This paper presents a hybrid evolutionary algorithm that efficiently solves multiobjective optimization problems. The idea is to bring the strength of adaptive local search (ALS) to bear upon the realm of multiobjective evolutionary optimization. The ALS is developed by harmonizing a weighted fitness policy with a restricted mutation: it applies mutation only to a set of superior individuals in accordance with the weighted fitness values. It economizes search time and efficiently traverses the problem space in the vicinity of the most-likely and least-crowded solutions. Thus, it helps achieve higher proximity and better diversity of nondominated solutions. Empirical results support the effectiveness of the proposed approach. ©2009 IEEE.
- Institute of Electrical and Electronics Engineers
- Related Researcher
There are no files associated with this item.
- Convergence Research Center for Wellness2. Conference Papers
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.