MATLAB Implementation of Quantum Particle Swarm Optimization Algorithm

In the field of computer science, optimization algorithms refer to processes that seek optimal or near-optimal solutions, typically employed to solve complex problems. Quantum Particle Swarm Optimization (QPSO) is a population-based probabilistic algorithm derived from traditional Particle Swarm Optimization. Unlike its conventional counterpart, QPSO incorporates quantum mechanics concepts including quantum states, measurement, and interference. These principles enable QPSO to explore the entire solution space more effectively while overcoming the traditional PSO limitation where velocity restrictions confine particles to local regions. The MATLAB implementation typically involves initializing particle positions using quantum-state-inspired probability distributions, updating positions through quantum-behavior operators, and evaluating fitness functions without velocity calculations. Consequently, QPSO demonstrates high efficiency and precision in solving complex problems, with applications spanning pattern recognition, image processing, and machine learning. Key algorithmic components include quantum potential well modeling and contraction-expansion coefficient control for balancing exploration and exploitation phases.