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A hybrid evolutionary algorithm for multiobjective optimization

Title
A hybrid evolutionary algorithm for multiobjective optimization
Authors
Ahn, Chang WookKim, Hyun TaeKim, Ye HoonAn, Jin Ung
DGIST Authors
An, Jin Ung
Issue Date
2009
Citation
2009 4th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2009, 19-23
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
Abstract
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.
URI
http://hdl.handle.net/20.500.11750/1892
DOI
10.1109/BICTA.2009.5338162
Publisher
Institute of Electrical and Electronics Engineers
Related Researcher
Files:
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Collection:
Convergence Research Center for Wellness2. Conference Papers


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