Bee Swarm Intelligence Inspired Sustainable Swarm Air Purification Agent System with K-means Clustering - Sahai, Priya; Kumar, Rakesh; Mehrotra, Monica (SN computer science, 2025)
Most of the world’s population is living in hazardous air quality. In this paper, we have proposed an air purification peer-to-peer networked multi-agent-based intelligent system in open areas for a society complex in urban cities. In this paper, an artificial bee swarm optimization algorithm is used to provide cooperative, intelligent, and novel solutions for cleaner air in urban societies. With this system, we are able to optimize power consumption and make the system sustainable for future-centric smart city layouts. Artificial bee colony algorithm finds the best solution after evaluating the fitness value of the source. We have also used the k-means clustering algorithm to determine the physical locations of such agent units and provided a scalable solution for the problem. Implementation is done using Spyder (Anaconda 3) tool and results have shown that our proposed algorithm provides a scalable efficient solution of the identified problem.