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V-Shaped BAS: Applications on Large Portfolios Selection Problem

V-Shaped BAS: Applications on Large Portfolios Selection Problem The beetle antennae search (BAS) algorithm is a memetic meta-heuristic optimization algorithm capable of solving combinatorial optimization problems. In this paper, the binary version of BAS (BBAS) is modified by adding a V-shaped transfer function. In this way, we introduce the V-shaped transfer function-based binary BAS (VSBAS) algorithm, which is a more effective and efficient version of BBAS in the case of large input data. Applications using real-world data sets on a binary Markowitz-based portfolio selection (BMPS) problem validate the excellent performance of VSBAS on large input data and demonstrate that it is a marvelous alternative against other ordinary memetic meta-heuristic optimization algorithms. Note that, because the meta-heuristic algorithms compared in this paper are directly applicable only to unconstrained optimization, the penalty function method was used to keep their solutions in the feasible district. In order to support and promote the findings of this work, we have constructed a complete MATLAB package for the interested user, which is freely available through GitHub. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Economics Springer Journals

V-Shaped BAS: Applications on Large Portfolios Selection Problem

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References (49)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
ISSN
0927-7099
eISSN
1572-9974
DOI
10.1007/s10614-021-10184-9
Publisher site
See Article on Publisher Site

Abstract

The beetle antennae search (BAS) algorithm is a memetic meta-heuristic optimization algorithm capable of solving combinatorial optimization problems. In this paper, the binary version of BAS (BBAS) is modified by adding a V-shaped transfer function. In this way, we introduce the V-shaped transfer function-based binary BAS (VSBAS) algorithm, which is a more effective and efficient version of BBAS in the case of large input data. Applications using real-world data sets on a binary Markowitz-based portfolio selection (BMPS) problem validate the excellent performance of VSBAS on large input data and demonstrate that it is a marvelous alternative against other ordinary memetic meta-heuristic optimization algorithms. Note that, because the meta-heuristic algorithms compared in this paper are directly applicable only to unconstrained optimization, the penalty function method was used to keep their solutions in the feasible district. In order to support and promote the findings of this work, we have constructed a complete MATLAB package for the interested user, which is freely available through GitHub.

Journal

Computational EconomicsSpringer Journals

Published: Dec 1, 2022

Keywords: Beetle antennae search algorithm; Meta-heuristic optimization; Integer linear programming; Binary optimization; Portfolio selection

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