Constrained Laplacian biogeography-based optimization algorithm

Vanita Garg () and Kusum Deep ()
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Vanita Garg: Indian Institute of Technology
Kusum Deep: Indian Institute of Technology

International Journal of System Assurance Engineering and Management, 2017, vol. 8, issue 2, No 28, 867-885

Abstract: Abstract Biogeography-based optimization (BBO) is a relatively new nature inspired optimization technique proposed by Dan Simon for unconstrained optimization, which was later generalized and improved by Happing Ma and Dan Simon for constrained optimization, called blended biogeography-based optimization. In an earlier paper, the authors have proposed a Laplacian biogeography-based optimization algorithm (LX-BBO) for unconstrained optimization. The purpose of the present paper is to generalize the LX-BBO from the unconstrained case to the constrained case. This is done by using the Deb’s constrained handling method. In order to evaluate the performance of the proposed constrained LX-BBO for constrained optimization problems, five different constrained optimization problems and popular CEC 2006 benchmark collection is used. Based on the analysis of results it is shown that the proposed Constrained LX-BBO outperforms Blended BBO for constrained optimization.

Keywords: Biogeography-based optimization; Blended BBO; Laplacian BBO; Constrained optimization (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13198-016-0539-7

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