Node coverage optimization for wireless sensor networks based on a multi-strategy fusion group teaching optimization algorithm

Optimizing node coverage in wireless sensor networks (WSNs) is critical for enhancing network performance and extending operational lifespan, especially in environments with obstacles. Traditional optimization methods often suffer from limited precision and local optima convergence. A multi-strategy fusion-based group teaching optimization algorithm (MSIGTOA) is proposed to overcome these issues. MSIGTOA improves initial population quality through chaotic inverse learning, incorporates differentiated learning during the student phase and uses a predatory mechanism to enhance global exploration. Evaluated on the CECBC-2017 benchmark functions, MSIGTOA outperforms seven other optimization algorithms (arithmetic optimization algorithm, tunicate swarm algorithm, student psychology-based-optimization, pelican optimization algorithm, sparrow search algorithm, whale optimization algorithm and sine cosine algorithm). In WSN deployment with obstacles (rectangular, trapezoidal and triangular), MSIGTOA achieves higher coverage and reduces node usage by at least 10%, substantially decreasing deployment costs for WSNs.