DECODING NATURE-INSPIRED ALGORITHMS HARNESSING SWARM INTELLIGENCE FOR REAL-TIME ENVIRONMENTAL MONITORING AND ANOMALY DETECTION IN DYNAMIC ECOSYSTEMS

The increasing complexity of environmental systems and the urgency of real-time ecological surveillance necessitate the development of intelligent, adaptive monitoring technologies. Nature-inspired algorithms, particularly those leveraging swarm intelligence (SI), offer robust, scalable, and decentralized solutions for continuous environmental monitoring and the early detection of anomalies. This paper provides a comprehensive analysis of how swarm intelligence models — inspired by collective behavior in biological systems — can be adapted for real-time ecological data processing, anomaly identification, and dynamic response in complex ecosystems. Drawing on prior work up to 2024, we explore algorithmic frameworks, simulation techniques, and sensor integration strategies that have proven effective in similar domains. Furthermore, we propose an updated system architecture tailored to modern environmental challenges such as climate variability, biodiversity loss, and pollution monitoring. Visualization tools including flow charts, comparative performance graphs, and architecture schematics are integrated to contextualize and operationalize these models.