Document Type
Article
Publication Date
4-1-2011
Publication Title
Engineering Applications of Artificial Intelligence
Abstract
Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BBO outperforms standard BBO. Second, we employ blended BBO to solve constrained optimization problems. Constraints are handled by modifying the BBO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BBO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm optimization 2007 (SPSO 07). The numerical results demonstrate that constrained blended BBO outperforms SGA and performs similarly to SPSO 07 for constrained single-objective optimization problems.
Repository Citation
Ma, Haiping and Simon, Daniel J., "Blended Biogeography-based Optimization for Constrained Optimization" (2011). Electrical and Computer Engineering Faculty Publications. 12.
https://engagedscholarship.csuohio.edu/enece_facpub/12
Original Citation
Haiping, M., & Simon, D. (2011). Blended biogeography-based optimization for constrained optimization. Engineering Applications of Artificial Intelligence, 24, 3, 517-25.
DOI
10.1016/j.engappai.2010.08.005
Version
Postprint
Publisher's Statement
(c) 2011 Elsevier
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Volume
24
Issue
3
