Abstract
The need for effective coordination and collaboration methods has increased due to the increasing use of multi-agent autonomous systems (MAS) in fields ranging from wireless sensor networks and healthcare to robotics and unmanned aerial vehicles (UAVs). The limited scalability, flexibility, and fault tolerance of centralized control techniques make them unsuitable for use in expansive, dynamic, and unpredictable situations. Swarm intelligence (SI) is a decentralized, self-organizing paradigm designed to address these issues. It is based on the collective actions of natural systems, such as ant colonies and bird flocks. When collaboration enables agents to share information, utilize one another to grow, and achieve goals beyond individual realization, coordination in MAS based on SI allows agents to coordinate activities, avoid disputes, and optimize task assignments effectively. The framework of emerging intelligence in autonomous systems is formed from these techniques considered collectively. With a focus on their implementation in robotic swarms, UAV formations, energy-aware sensor networks, and secure healthcare systems, this paper examines existing and recent SI concepts. A theoretical framework with levels for understanding, decision-making, and cooperation is provided to allow robust MAS functioning. This review presents SI as an essential tool for the next generation of intelligent, robust, and adaptable multi-agent autonomous systems by encouraging coordination and collaboration through swarm principles. The study discusses in depth the different algorithms with examples of swarm intelligence and compares these algorithms to determine which performs best in coordination and collaboration based on MAS.
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