Constrained Laplacian biogeography-based optimization algorithm

Garg, Vanita; Deep, Kusum

doi:10.1007/s13198-016-0539-7

Cited by 11 publications

(2 citation statements)

References 21 publications

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“…In [9], LX-BBO has been proposed and tested on a vast set of benchmark functions. LX-BBO has outperformed blended BBO on the basis of IEEE CEC 2014 benchmark functions.…”

Section: Laplacian Biogeography Based Optimizationmentioning

confidence: 99%

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Portfolio optimization using Laplacian biogeography based optimization

Garg

Deep

2019

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Portfolio optimization is defined as the most appropriate allocation of assets so as to maximize returns subject to minimum risk. This constrained nonlinear optimization problem is highly complex due to the presence of a number of local optimas. The objective of this paper is to illustrate the effectiveness of a well-tested and effective Laplacian biogeography based optimization and another variant called blended biogeography based optimization. As an illustration the model and solution methodology is implemented on data taken from Indian National Stock Exchange, Mumbai from 1st April, 2015 to 31st March, 2016. From the analysis of results, it is concluded that as compared to blended BBO, the recently proposed LX-BBO algorithm is an effective tool to solve this complex problem of portfolio optimization with better accuracy and reliability.

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“…In [9], LX-BBO has been proposed and tested on a vast set of benchmark functions. LX-BBO has outperformed blended BBO on the basis of IEEE CEC 2014 benchmark functions.…”

Section: Laplacian Biogeography Based Optimizationmentioning

confidence: 99%

“…For details the reader may consult [14] for blended BBO and [9] for LX-BBO. Both these variants of BBO are used to solve portfolio optimization problem which is implemented on real data as explained in next section.…”

Section: Deb's Feasibility Rulesmentioning

confidence: 99%