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Explainable artificial intelligence enhanced quantum-inspired spider monkey optimization for a constrained portfolio optimization proble
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AI
DOI：10.1007/s42484-025-00338-5.png)
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Abstract
En 中文
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.
Keywords:
Portfolio optimization
Explainable AI
Spider monkey optimization (SMO)
Ageist spider monkey optimization
Quantum-inspired metaheuristics
Journal
Q
IF:
4.4
Papers:
433
Citations:
796