Hybrid Genetic Algorithm with Particle Swarm Optimization

The provided code implements a hybrid optimization methodology that integrates Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) strategies. This combined approach leverages GA's chromosome-based crossover and mutation operations for solution space exploration, while incorporating PSO's velocity and position update mechanisms for efficient local refinement. The algorithm maintains a population of solutions where individuals evolve through genetic operations and simultaneously adjust their search trajectories using swarm intelligence principles. Key implementation aspects include fitness evaluation functions, selection mechanisms (such as tournament or roulette wheel selection), and dynamic parameter adjustment for balancing global exploration and local exploitation. This hybrid structure enables robust performance in identifying optimal solutions for challenging optimization problems across various domains including engineering design, data mining, and computational finance.