Optimization of RBF Networks Using Particle Swarm and Genetic Algorithms

We implemented optimization of Radial Basis Function (RBF) networks using both Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), followed by comprehensive comparative analysis. The study begins with an explanation of PSO fundamentals, including particle position update mechanisms using velocity vectors and fitness evaluation functions. The algorithm was applied to optimize RBF network parameters through iterative population-based search, where each particle represents a potential solution with dimensions corresponding to RBF centers, widths, and weights. Subsequently, we detailed Genetic Algorithm operations involving selection, crossover, and mutation operators specifically adapted for RBF network optimization. The implementation features chromosome encoding schemes representing network parameters, with fitness functions evaluating network performance metrics like mean squared error. Tournament selection and uniform crossover mechanisms were employed to maintain population diversity while exploring optimal parameter combinations. The comparative analysis examined convergence behavior, computational efficiency, and solution quality between both optimization approaches. Performance metrics included training accuracy, generalization capability, and parameter sensitivity, with statistical validation methods applied to ensure robustness. Code implementation considerations covered parameter tuning strategies, stopping criteria, and memory-efficient data structures for handling large-scale RBF networks.