A Multi-strategy Enhanced Evolutionary Algorithm for Persistent Perception in Software-Defined Radio Sensor Networks

Persistent multi-target perception in Software-Defined Radio (SDR) networks presents a challenging large-scale multi-objective optimization (LSMOO) task. Traditional evolutionary algorithms struggle to balance perception quality and task continuity because high-dimensional decision spaces are often extremely sparse. To address these challenges, we propose a Multi-Strategy Enhanced Evolutionary Algorithm (MSEA). Instead of passively searching infeasible regions, MSEA compresses the decision space through a target-pool-aware 2D integer encoding scheme. Furthermore, we introduce a Local-Bayesian collaborative engine to refine the search. In this engine, a 3D posterior probability tensor provides macro-level global guidance to accelerate convergence. Simultaneously, a continuity-oriented intelligent repair operator resolves micro-level perceptual fragmentation while strictly enforcing hardware budgets. We validated MSEA using sensor network topologies derived from publicly shared, real-world SDR nodes. Extensive experiments demonstrate that MSEA achieves superior convergence stability and task continuity compared to mainstream algorithms, including NSGA-II, SPEA2, and MOPSO.