"Constrained Many-objective Optimization in Wireless Sensor Networks Deployment"

"Wireless sensor networks (WSNs) play a crucial role in a wide range of modern applications, including industrial monitoring, environmental sensing, and precision agriculture. The deployment of WSNs is a challenging optimization problem that involves multiple conflicting objectives such as maximizing coverage, minimizing energy consumption, and prolonging network lifetime, while simultaneously satisfying connectivity constraints. In this paper, we formulate the WSNs deployment task as a Constrained Many-objective Optimization (CMaOO) problem that considers four objectives: Coverage, Sensing energy, Communication energy and network Lifetime, under different Connectivity constraint settings. To address this complex problem, we propose a Dynamic Weight Improve Artificial Bee Colony (DWIABC) algorithm, which employs dynamically weight vector assignment to better balance competing objectives and enhance global-local optimization capability. Unlike traditional approaches that assume connectivity is automatically satisfied under specific communication conditions, this work explicitly investigates deployment performance under different connectivity constraint settings. Extensive simulations are conducted to evaluate the effectiveness of the proposed method by comparing it with several state-of-the-art many-objective optimization algorithms. The results demonstrate that DWIABC achieves superior performance in terms of solution quality, objective balance, and constraint handling, making it a promising approach for constrained many-objective WSNs deployment problem, especially in challenging environments where conventional connectivity assumptions do not hold."