Explainable artificial intelligence enhanced quantum-inspired spider monkey optimization for a constrained portfolio optimization proble

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260212" target="_blank" >RIV/61989100:27240/25:10260212 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s42484-025-00338-5" target="_blank" >https://link.springer.com/article/10.1007/s42484-025-00338-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s42484-025-00338-5" target="_blank" >10.1007/s42484-025-00338-5</a>

Result language

angličtina

Original language name

Explainable artificial intelligence enhanced quantum-inspired spider monkey optimization for a constrained portfolio optimization proble

Original language description

Optimizing portfolios has consistently posed significant challenges while being an extensively researched subject in finance and accounting. This process requires selecting and distributing appropriate assets in alignment with a set of specified objectives. This nonlinear constraint issue is not effectively solvable using traditional methods. This paper investigates the use of spider monkey optimization, ageist spider monkey optimization, and a newly proposed enhanced spider monkey optimization technique for portfolio optimization problems. The explainability of the spider monkey optimization has been improved without compromising the optimization results. It has been observed that the proposed technique marginally enhances the results of spider monkey optimization and can improve trust and risk management in the portfolio optimization problem. Furthermore, a quantum-inspired version of the proposed method is also implemented, and the results are compared using three benchmarked datasets from Dow Jones, BSE, and NASDAQ. Experimental results obtained using these benchmark datasets demonstrate that the newly introduced technique within the quantum-inspired framework marginally outperforms all other methods in the classical and quantum-inspired domains. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Quantum Machine Intelligence

ISSN

2524-4906

e-ISSN

—

Volume of the periodical

7

Issue of the periodical within the volume

2

Country of publishing house

GB - UNITED KINGDOM

Number of pages

38

Pages from-to

nestránkováno

UT code for WoS article

001615775000002

EID of the result in the Scopus database

2-s2.0-105022056733