Quantum-Inspired gravitationally guided particle swarm optimization for feature selection and classification

Population-based metaheuristic optimization algorithms have gained prominence for tackling complex optimization problems. They balance exploration and exploitation, essential for finding optimal solutions. While algorithms like Genetic Algorithms, Particle Swarm Optimization, and Gravitational Search Algorithm have shown success, they have limitations, such as premature convergence and sensitivity to parameters. To address these issues, we have introduced Quantum-Inspired Gravitationally Guided Particle Swarm Optimization (QIGPSO) for addressing complex optimization challenges, particularly in the context of medical data analysis for diagnosing Non-Communicable Diseases (NCDs). The Quantum Particle Swarm Optimization (QPSO) and Gravitational Search Algorithm (GSA) are both used in QIGPSO. It takes advantage of each algorithm's strengths in both global and local search processes. We used an absolute Gaussian random variable to improve the search, changed the position update equations and used a wrapper-based method with Support Vector Machine (SVM) for feature selection and classification. The findings suggest that QIGPSO is effective at identifying key features, achieving high accuracy rates, and lowering the number of incorrect classifications across several NCD datasets. Doctors can use QIGPSO data to make better treatment decisions for their patients. QIGPSO overcomes the limitations of conventional optimization methods by faster convergence while improving exploitation balance.