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This study presents a comparative study of the hybrid WOA-GA-AI algorithm (Whale Optimization Algorithm - Genetic Algorithm – Artificial Intelligence) applied to wireless sensor network optimization, benchmarked against three classical algorithms: GA (Genetic Algorithm), PSO (Particle Swarm Optimization), and ACO (Ant Colony Optimization). Performance is evaluated based on three key criteria: network lifetime, residual energy, and volume of data transmitted to the base station. The results demonstrate that WOA-GA-AI consistently outperforms its competitors, enhancing node longevity through improved energy management and more efficient data transmission. WOA-GA-AI performs particularly well in the advanced stages of network operation, maintaining higher residual energy levels and delaying node depletion. These findings highlight its potential for resource-constrained environments. Finally, future research directions are outlined, including evaluation in dynamic and complex network scenarios, as well as enhancing robustness for industrial applications.

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Optimization of Coverage and Energy Efficiency in Wireless Sensor Networks: A Hybrid WOA-GA Approach Integrating Artificial Intelligence Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • B. A. Momar Ousmane,
  • Cheikhou Thiam

摘要

This study presents a comparative study of the hybrid WOA-GA-AI algorithm (Whale Optimization Algorithm - Genetic Algorithm – Artificial Intelligence) applied to wireless sensor network optimization, benchmarked against three classical algorithms: GA (Genetic Algorithm), PSO (Particle Swarm Optimization), and ACO (Ant Colony Optimization). Performance is evaluated based on three key criteria: network lifetime, residual energy, and volume of data transmitted to the base station. The results demonstrate that WOA-GA-AI consistently outperforms its competitors, enhancing node longevity through improved energy management and more efficient data transmission. WOA-GA-AI performs particularly well in the advanced stages of network operation, maintaining higher residual energy levels and delaying node depletion. These findings highlight its potential for resource-constrained environments. Finally, future research directions are outlined, including evaluation in dynamic and complex network scenarios, as well as enhancing robustness for industrial applications.