Multi-objective Quantum-inspired Tabu Search Algorithm for Weighted Portfolio Model in Financial Optimization

Quantum-inspired evolutionary computation offers a practical approach to complex optimization by simulating quantum principles on classical systems. This study proposes a multi-objective weighted portfolio model (MoWPM) based on trend ratio evaluation, along with a multi-objective quantum-inspired tabu search algorithm (MoQTS) for portfolio allocation. MoQTS incorporates superposition and an enhanced entanglement mechanism, which effectively improves convergence and expands the Pareto front. Experimental results indicate that the proposed method performs robustly and shows strong potential in supporting diverse financial decision-making needs.