Enhanced Archimedes optimization algorithm for quality of service-aware routing in internet of things-enabled wireless sensor networks

Abstract This paper presents an advanced technique of routing optimization for Wireless Sensor Networks (WSNs) within the Internet of Things (IoT) context featuring an enhanced variation of the Archimedes optimization algorithm (AOA), called EAOA. Traditional WSNs have suffered significantly due to routing optimization challenges resulting from intrinsic constraints related to energy, latency, and network lifetime. Conventional meta-heuristic algorithms, including standard AOA, often suffer from poor convergence speed and tend to quickly become stuck in a local optimum. To address these issues, EAOA employs two strategic enhancements: a local escape operator (LEO) for improved local search and orthogonal learning (OL) for balancing exploration and exploitation. These features enable EAOA to outperform conventional algorithms in finding the optimal routing path, resulting in minimal energy consumption, reduced latency, and improved node longevity. Experimental outcomes reveal that EAOA is significantly more efficient than alternatives from the perspective of network robustness, energy efficiency, and data throughput. This work opens possibilities for enhancements in WSN routing performance in IoT applications by using EAOA to support sustainable and efficient IoT networks.