Swarm Intelligence in Autonomous Systems: Principles, Applications, and Future Directions

Abstract

Swarm intelligence (SI) is a bio-inspired concept of computing that allows self-organizing autonomous agents to cooperate and work together without the need for centralized control to overcome complex tasks. Based on the collective nature of social insects, birds and fish, SI uses the concepts of local interactions, emergent behavior, and decentralized decision-making to provide strong and scalable solutions. This survey discusses the basics of swarm intelligence, which includes major algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Firefly Algorithms and their applications in autonomous systems. The application of SI in swarm robotics, unmanned aerial vehicle (UAVs) swarm, autonomous underwater vehicle (AUVs), smart transportation, Internet of Things (IoT), logistics, and healthcare shows the flexibility and effectiveness of SI. Also, the paper discusses the synergy of swarm intelligence and artificial intelligence methods, such as reinforcement learning and deep learning, that can be used to improve adaptive behaviors, decision-making, and real-time responsiveness. Problems of scalability, energy efficiency, security, dynamic environments, and standardization are addressed, as well as the new directions in swarms that have the ability to self-evolving swarms, human-swarm interaction, bioinspired models, and ethical issues. The present survey gives a broad overview of the present condition, uses, and future research of swarm intelligence in autonomous systems.