Alternative Nature-Inspired Optimizers: An Attempt to Solve the Coverage and Connectivity Problem in Wireless Sensor Network Deployment

Ensuring adequate coverage and connectivity are key obstacles in deploying Wireless Sensor Networks, significantly affecting network performance. This study tackles these challenges in planned deployments by exploring alternative optimization approaches beyond conventional algorithms, such as Genetic Algorithms and Particle Swarm Optimization. Specifically, it investigates the effectiveness of Differential Evolution, Grey Wolf Optimizer, and Cuckoo Search. The main contribution lies in adapting these algorithms to make a better outcome while fulfill connectivity requirements, laying a groundwork for future investigations into WSNs deployment using diverse meta-heuristic algorithms, despite the simplicity of the model employed in this study.