Vol. 2, Issue 2, Part A (2025)

The study investigates the synergistic integration of swarm intelligence (SI) principles within multi-agent system (MAS) architectures to enhance scalability, adaptability, and fault tolerance in distributed artificial intelligence environments. Swarm intelligence, inspired by collective behaviors in natural systems such as ant colonies and bird flocks, enables autonomous agents to operate through decentralized coordination and local interactions. In this research, a simulated SI-MAS framework was developed and tested using Python and MATLAB environments with agent populations ranging from 100 to 1000. Performance metrics including task completion time, fault recovery rate, task success ratio, and adaptation latency under dynamic environmental conditions were evaluated using statistical tools such as ANOVA and confidence interval analysis. The results revealed that the SI-MAS model achieved near-linear scalability, maintaining efficiency even as the number of agents increased, while exhibiting superior fault tolerance and faster recovery compared to centralized systems. Furthermore, the architecture demonstrated rapid adaptability to environmental shocks, validating the hypothesis that local interactions and stigmergic coordination lead to emergent intelligence and collective problem-solving. These findings reinforce the theoretical framework of distributed control and self-organization, providing a practical foundation for real-world applications in autonomous robotics, IoT sensor networks, industrial automation, and smart infrastructure systems. The research concludes that swarm-based MAS represents a viable paradigm for achieving robust, self-organizing, and resilient distributed AI systems capable of operating efficiently under dynamic, uncertain, and large-scale environments. Practical recommendations include the adoption of SI-MAS models in autonomous robotics, distributed sensing, and hybrid learning systems to leverage their inherent scalability and fault tolerance for real-world implementations.