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<p>The present study aims to meet this requirement by developing energy-aware and reliable routing schemes for Wireless Sensor Networks (WSNs) with constrained resources and dynamic conditions. In this research, a multi-hop routing technique, based on a Modified Group Teaching Optimization Algorithm (MGTOA), is proposed to optimize clustering and routing in WSNs, through a learning-driven optimization mechanism. By integrating two educational achievement archives, MGTOA can efficiently balance exploitation and exploration in routing path optimization. A multi-objective fitness function is formulated to account for residual energy, hop count, and communication stability. Simulation results across different network scales and base station placements suggest that the proposed strategy significantly improves energy efficiency, network longevity, and data delivery performance compared with existing routing protocols. For instance, in the scenario with 150 sensor nodes deployed over a 300 × 300&#xa0;m² field, the proposed MGTOA-based routing protocol achieved up to 69.2% improvement in energy efficiency and up to 26.4% enhancement in data delivery performance compared with existing routing schemes.</p>

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Educational achievement-guided group teaching optimization algorithm for robust multi-hop routing in wireless sensor networks

  • 期刊论文

  • 发表日期：

    2026年3月21日

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  • Wang Yun

摘要

The present study aims to meet this requirement by developing energy-aware and reliable routing schemes for Wireless Sensor Networks (WSNs) with constrained resources and dynamic conditions. In this research, a multi-hop routing technique, based on a Modified Group Teaching Optimization Algorithm (MGTOA), is proposed to optimize clustering and routing in WSNs, through a learning-driven optimization mechanism. By integrating two educational achievement archives, MGTOA can efficiently balance exploitation and exploration in routing path optimization. A multi-objective fitness function is formulated to account for residual energy, hop count, and communication stability. Simulation results across different network scales and base station placements suggest that the proposed strategy significantly improves energy efficiency, network longevity, and data delivery performance compared with existing routing protocols. For instance, in the scenario with 150 sensor nodes deployed over a 300 × 300 m² field, the proposed MGTOA-based routing protocol achieved up to 69.2% improvement in energy efficiency and up to 26.4% enhancement in data delivery performance compared with existing routing schemes.