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

ABSTRACT: 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.

SUBMITTER: Malik S 

PROVIDER: S-EPMC12488977 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature