Multi-strategy seagull optimization for wireless sensor network deployment with chaotic and quantum enhancements

The efficient placement of sensor nodes in a Wireless Sensor Network (WSN) ensures extensive coverage with little redundancy and energy consumption. Traditional metaheuristic approaches, such as the Seagull Optimization Algorithm (SOA), suffer from slow convergence, limited exploration, and a high risk of early local-optima trapping. To overcome these shortcomings, this research contributes an innovative Multi-Strategy Enhanced Seagull Optimization Algorithm (MSSOA) to improve WSN coverage. MSSOA couples chaotic mapping of population initialization with a nonlinear convergence coefficient to tune adaptive exploration and exploitation, an adaptive inertia term to enhance convergence precision, and an imitation quantum crossover mutation mechanism to introduce diversity and avoid local optima. The novel MSSOA was comprehensively tested in probabilistic 2D detection cases for both small- and large-scale deployments. Test results confirm that MSSOA outperforms benchmark algorithms in all configurations tested. In a small-scale case of a 50 × 50 m area with 50 sensors, MSSOA achieved a coverage of 98.8%. On a large scale, in a 100 × 100 m case, MSSOA remained the optimal choice, with a coverage of 98.9%. The experiment demonstrates the potential of MSSOA to achieve consistently superior coverage, early convergence, and maintain stability, particularly on a large scale and in higher dimensions.