An Innovative Hunting-based Quantum-inspired Jaguar Algorithm for Combinatorial Optimization

Quantum-inspired optimization (QIO) is a promising technique that simulates quantum behaviors on classical computers to leverage both classical and quantum advantages in tackling complex optimization problems. This study first proposes the quantum-inspired jaguar algorithm (QJA) to solve combinatorial optimization problems (COP). The quantum property helps address COP efficiently when the element increases in the search space. Besides, QJA extracts the crucial concept of the hunting mechanism from the jaguar algorithm (JA), which effectively addresses numerical optimization in classical computing. The featured hunting mechanism enables QJA to automatically assess the algorithm's situation and adaptively update the critical parameters in QIO, thereby performing an efficient search. QJA tackles a complex financial application: portfolio optimization, which requires an effective solution quickly. In the experimental result, this study utilizes real-world financial data to demonstrate the effectiveness of QJA and to compare it with other QIOs, facilitating advancements in quantum finance. The convergence results indicate that QJA can identify higher quality portfolios in the early stages and continue to enhance them throughout the search process. In summary, this study transfers the knowledge and insights gained from the hunting mechanism in classical computing to QIO, integrating both classical and quantum computing techniques to address COP effectively.