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Swarm intelligence is a field of artificial intelligence that studies the collective behavior of decentralized, self-organized systems, natural or artificial. It is inspired by the observation of social insects like ants, bees, and birds, where simple individual agents interacting with their environment and each other can give rise to complex, intelligent global behavior.

Key characteristics of swarm intelligence systems include a large number of relatively simple agents, local interactions, and the absence of centralized control. Agents typically follow simple rules and communicate indirectly through modifications to their environment, a process known as stigmergy. Despite the simplicity of individual agents, the collective behavior can exhibit sophisticated problem-solving abilities, such as finding optimal paths, coordinating movements, and adapting to changing conditions.

Common algorithms inspired by swarm intelligence include ant colony optimization (ACO), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. ACO algorithms are often used for solving combinatorial optimization problems, such as the traveling salesman problem. PSO algorithms are frequently applied to optimization and search problems in continuous spaces. ABC algorithms are designed for function optimization.

Applications of swarm intelligence are diverse, spanning areas like robotics, logistics, telecommunications, and data mining. For example, swarm robotics utilizes multiple simple robots to perform tasks that would be difficult or impossible for a single, more complex robot. In logistics, swarm algorithms can optimize delivery routes. The inherent robustness and scalability of swarm systems make them attractive for tackling complex real-world challenges.