A novel hybrid PSO-based metaheuristic for costly portfolio selection problems

Marco Corazza (), Giacomo di Tollo (), Giovanni Fasano () and Raffaele Pesenti ()
Additional contact information
Giacomo di Tollo: Università Ca’ Foscari, Venezia
Giovanni Fasano: Ca’ Foscari University of Venice
Raffaele Pesenti: Ca’ Foscari University of Venice

Annals of Operations Research, 2021, vol. 304, issue 1, No 5, 109-137

Abstract: Abstract In this paper we propose a hybrid metaheuristic based on Particle Swarm Optimization, which we tailor on a portfolio selection problem. To motivate and apply our hybrid metaheuristic, we reformulate the portfolio selection problem as an unconstrained problem, by means of penalty functions in the framework of the exact penalty methods. Our metaheuristic is hybrid as it adaptively updates the penalty parameters of the unconstrained model during the optimization process. In addition, it iteratively refines its solutions to reduce possible infeasibilities. We report also a numerical case study. Our hybrid metaheuristic appears to perform better than the corresponding Particle Swarm Optimization solver with constant penalty parameters. It performs similarly to two corresponding Particle Swarm Optimization solvers with penalty parameters respectively determined by a REVAC-based tuning procedure and an irace-based one, but on average it just needs less than 4% of the computational time requested by the latter procedures.

Keywords: Hybrid metaheuristics; Particle Swarm Optimization; Global optimization; Portfolio selection problems; Exact penalty functions; REVAC; irace (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-021-04075-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:304:y:2021:i:1:d:10.1007_s10479-021-04075-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-021-04075-3

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().