Portfolio Optimization Using Quantum-Inspired Dynamic Flower Pollination Optimizer

This paper presents a novel approach to portfolio optimization using a quantum-inspired dynamic flower pollination optimizer on a single objective function. The proposed techniques use an improved gamma function to enhance the traditional flower pollination algorithm, resulting in improved efficiency. Furthermore, the suggested enhancement has been implemented within the quantum-inspired domain, and the outcomes have been compared. The approach is validated through extensive simulations using real-world financial data, demonstrating its ability to generate optimal portfolios with better risk-return profiles compared to traditional techniques. The results indicate that the quantum-inspired dynamic flower pollination optimizer holds great promise in addressing the challenges of portfolio optimization in finance.