فهرست نشریات علمی دسترسی آزاد ایران

Wireless Sensor Networks (WSNs) play a crucial role in monitoring and surveillance, yet random deployment often causes uneven coverage and redundant sensing. This study introduces a Chaos-Enhanced Superb Fairy-wren Optimization Algorithm (CE-SFOA), which integrates chaotic dynamics through a Cubic map into the position update and parameter control mechanisms. The chaotic modulation enhances population diversity, balances exploration and exploitation, and mitigates premature convergence. Experiments across three deployment scenarios show that CE-SFOA consistently achieves higher coverage and faster convergence than SFOA and seven competing metaheuristics, yielding 5.32–6.65% coverage improvement over the baseline. These findings demonstrate that chaotic modulation is an effective strategy for enhancing metaheuristic performance in WSN coverage optimization.