Source Code for Various Artificial Intelligence Algorithms

Artificial intelligence algorithm source code encompasses numerous classical and contemporary intelligent computing methods. Ant Colony Optimization (ACO) mimics ant foraging behavior, utilizing pheromone trails to guide path searches, commonly applied to combinatorial optimization problems like the Traveling Salesman Problem (TSP). Code implementation typically involves initializing pheromone matrices, designing probability transition rules, and implementing pheromone update mechanisms with evaporation rates. Particle Swarm Optimization (PSO) draws inspiration from bird flock movement, where particles continuously update their positions by tracking both individual and global best solutions. Key code components include velocity calculation using inertia weights, position updates with cognitive and social parameters, and boundary constraint handling for continuous space optimization. Genetic Algorithms (GA) simulate natural selection processes, evolving populations through selection, crossover, and mutation operations to handle complex nonlinear problems. Implementation requires chromosome encoding schemes, fitness function evaluation, tournament or roulette wheel selection mechanisms, and crossover/mutation operator customization. Neural Networks construct multi-layer interconnected neuron models, adjusting weights through backpropagation algorithms, demonstrating exceptional performance in image recognition and prediction tasks. Core implementation involves layer initialization with activation functions (ReLU, sigmoid), loss function calculation (cross-entropy, MSE), gradient computation via chain rule, and optimization using techniques like stochastic gradient descent with momentum. These algorithm source codes generally comprise core modules including parameter initialization, iterative update procedures, and termination condition checks. Different algorithms can mutually reference conceptual approaches or undergo hybrid improvements, such as combining GA's global search capability with PSO's convergence speed for enhanced optimization performance.