Quantum Particle Swarm Optimization Algorithm for Training RBF Network Weights

This program implements a Quantum Particle Swarm Optimization (QPSO) algorithm for training Radial Basis Function (RBF) network weights, serving as a reference implementation for the research community. The algorithm represents an enhanced version of the standard Particle Swarm Optimization (PSO) technique, incorporating quantum computing principles to achieve more effective global search capabilities for optimal solutions. The implementation features quantum-inspired position updates using wave function probability distributions, replacing traditional velocity vectors with quantum state transitions. Key functions include quantum-behaved particle movement using mean best positions and potential well centers, with contraction-expansion coefficients dynamically adjusting search scope. This algorithm demonstrates applications across various domains including artificial intelligence systems, machine learning model optimization, and data mining pattern recognition. Prior to implementation, users should understand fundamental concepts of RBF networks (including hidden layer structure and Gaussian activation functions) and weight training methodologies. Additionally, familiarity with quantum computing fundamentals (such as superposition and quantum states) and standard PSO operation mechanisms (including particle position updates and fitness evaluation) is recommended. The code structure includes modular components for population initialization, fitness calculation based on RBF network error minimization, quantum state updates, and convergence checking. We hope this implementation proves valuable for your research projects, and please feel free to inquire about any technical aspects requiring clarification.