Swarm Intelligence Algorithms Including Ant Colony, Particle Swarm Optimization, and Artificial Immune Algorithms

Swarm intelligence algorithms include ant colony optimization, particle swarm optimization, and others. Ant colony optimization mimics ant foraging behavior by simulating how ants establish optimal paths through pheromone trails - typically implemented using probabilistic path selection matrices and pheromone evaporation mechanisms. Particle swarm optimization emulates bird flock foraging behavior through collaborative individual movement, where code implementations generally involve velocity and position updates based on personal and global best solutions. Additionally, artificial immune algorithms simulate human immune system responses to problem-solving, often featuring antibody diversity maintenance and antigen recognition mechanisms in their code structure. These swarm intelligence algorithms demonstrate significant potential in solving complex optimization problems and function optimization tasks, with common applications ranging from combinatorial optimization to continuous parameter space exploration.