EMOPP-IRS: Evolutionary Multi-Objective Path Planning for Intelligent IRS-Assisted IoT Networks

Intelligent Reflecting Surfaces (IRS) have recently emerged as a transformative technology in the realm of satellite communication networks, particularly in Space-Air-Ground Integrated Networks (SAGIN), offering significant improvements, particularly in remote and last-mile terrestrial areas affected by urban blockage, non-Line-of-Sight (NLoS) links, and multipath fading. This paper proposes EMOPP-IRS, an innovative solution aimed at improving overall network performance through multi-objective optimization in IRS-assisted communication networks. EMOPP-IRS integrates a wide range of parameters and employs the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously optimize multiple conflicting objectives such as received power, latency, and energy efficiency. A comprehensive evaluation demonstrates that the proposed EMOPP-IRS significantly outperforms existing solutions by achieving a well-balanced trade-off under varying operational conditions. EMOPP-IRS adapts effectively to dynamic network scenarios while preserving diversity among optimal solutions, leading to substantial improvements in network performance, scalability, and power efficiency during communications through the IRS-assisted network at the ground segment of the satellite network.