Exploration of Classical Particle Swarm Optimization Algorithm and Introduction of Quantum Particle Swarm Optimization (QPSO)

This paper investigates the classical Particle Swarm Optimization algorithm and introduces a novel approach called Quantum Particle Swarm Optimization (QPSO). The QPSO algorithm not only preserves the efficient global search capabilities of the classical version but also incorporates quantum computing concepts, significantly enhancing the algorithm's exploratory power during the optimization process. By implementing quantum-inspired updates to particle positions and velocities—often using quantum wave functions for position probability distributions instead of traditional velocity vectors—the QPSO algorithm demonstrates superior adaptability to complex problem landscapes. In practical optimization scenarios, the algorithm exhibits robust performance through mechanisms like quantum state collapse and potential well-based updates, typically implemented using exponential decay functions or Monte Carlo sampling techniques. Compared to conventional optimization methods, QPSO shows distinct advantages in handling complex optimization challenges, providing a innovative framework for solving optimization problems with improved convergence properties and escape mechanisms from local optima.