A hybrid energy-efficient framework for wireless sensor networks using artificial algae algorithm and ant colony optimization

Abstract The limited battery constraint in wireless sensor networks poses a major challenge to the longevity and reliability of such networks. Energy efficiency is, therefore, critical to consider while designing these networks. In this study, we propose a novel hybrid energy-efficient framework that uses two bio-inspired algorithms. We integrate the Artificial Algae Algorithm and Ant Colony Optimization for CH selection and routing, respectively. Through extensive simulations, the performance of the proposed method is evaluated against six established algorithms, such as LEACH, Grey Wolf Optimizer, Particle Swarm Optimization, Sperm Swarm Optimization, Bermuda Triangle Optimizer, and Chernobyl Disaster Optimizer, using different metrics such as residual energy, number of alive nodes, Simulation results demonstrate that Artificial Algae Algorithm- Ant Colony Optimization (AAA-ACO) consistently outperforms existing methods in terms of stability and energy conservation. This study not only highlights the effectiveness of hybrid bio-inspired algorithms in wireless sensor networks but also opens avenues for future enhancements involving distributed execution, mobility support, and integration with intelligent network control systems.