Quantum Particle Swarm Optimization Algorithm Implementation in MATLAB

The MATLAB-implemented Quantum Particle Swarm Optimization (QPSO) algorithm is an optimization technique inspired by quantum particle behavior in nature. This algorithm simulates collective quantum particle dynamics and quantum mechanical principles to search for optimal solutions in complex problems. The implementation typically involves key components such as quantum state initialization using rand() function, wave function collapse simulation through probability amplitude calculations, and quantum rotation gates for position updates. QPSO demonstrates superior performance in handling various optimization challenges including multimodal functions and high-dimensional search spaces. The package includes benchmark test functions such as Sphere, Rastrigin, and Rosenbrock functions, which employ function handles and vectorized operations for efficient performance assessment. These test functions enable comprehensive evaluation of convergence speed, solution accuracy, and stability across different problem domains, facilitating deeper analysis of QPSO's adaptability and optimization capabilities.