Swarm Intelligence (SI) plays a significant role in artificial intelligence by mimicking the collective behavior of decentralized systems found in nature, such as ant colonies and bird flocks. It emphasizes decentralized control, where simple agents follow local rules, leading to the emergence of complex, intelligent behaviors without centralized oversight. This approach enhances scalability and adaptability in problem-solving across various applications, from robotics to optimization tasks like ant colony optimization[1][2].
Furthermore, SI has been increasingly integrated with machine learning and deep learning to improve optimization processes and handle complex tasks in real-time, leveraging the strengths of both paradigms. This integration addresses challenges like local optima stagnation and computational costs, enabling more effective solutions for real-world problems[3][4].
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