Performance Optimisation Framework for Wireless Sensor Networks Based on Multi‐Algorithm Collaboration

This paper presents an integrated optimisation framework for wireless sensor networks (WSNs) designed to manage the competing demands of energy efficiency, latency reduction, throughput improvement and communication reliability under dynamic and large‐scale deployment conditions. The framework reorganises and enhances three core optimisation methods—genetic algorithm (GA), particle swarm optimisation (PSO) and an improved NSGA‐II—by embedding adaptive behaviours and topology‐aware decision logic. The GA is strengthened through a zone‐oriented crossover mechanism and a sink‐distribution‐based initialisation strategy, which enhance coverage robustness and fault tolerance. The PSO module applies self‐adjusting learning coefficients and QoS‐aware routing constraints to maintain efficient path selection under varying load conditions. The improved NSGA‐II incorporates an adaptive selection mechanism and a direction‐guided crossover operator to better balance energy consumption and delay in multi‐objective optimisation. Simulation results show that the proposed framework consistently outperforms federated DDQN and adaptive MOPSO across all performance indicators. It also demonstrates superior multi‐objective convergence quality, achieving an IGD of 0.03 and an HV of 0.87. Overall, the framework enhances the scalability, resilience and operational efficiency of WSNs and provides practical guidance for adaptive scheduling in complex real‐world environments.