Enhancing Wi-Fi Signal Performance via Strategic Access Point Placement using Genetic Algorithm Approach

In this study, we tackle the challenge of optimizing Wi-Fi signal performance through strategic access point (AP) placement, employing genetic algorithms (GAs) to address the complexities of signal propagation and environmental interference. Traditional methods often fail to account for these factors comprehensively; hence, we developed a GA-based framework that evolves AP configurations to improve coverage and signal strength. Our method simulates various environmental scenarios, using a well-designed fitness function to iteratively refine placements based on coverage, strength, and interference minimization. The genetic operations of selection, crossover, and mutation explore a broad configuration space, achieving superior Wi-Fi performance by adapting to different conditions. Results from rigorous testing reveal that our approach significantly enhances network efficiency, demonstrating the GA’s ability to identify optimal configurations that traditional methods may overlook. This research not only advances network optimization but also provides practical insights for deploying effective and resilient Wi-Fi networks. Our findings underscore the potential of genetic algorithms to meet the dynamic demands of wireless network setup, suggesting a pathway toward more adaptive and intelligent network solutions. This GA-driven method represents a significant advancement in enhancing Wi-Fi signal performance, offering a robust framework for future research in network optimization.