基于多目标WSN网络覆盖优化的黏菌搜索算法OA

To address the challenges of low node coverage,susceptibility to local optima,and multi-objective collaborative optimization in wireless sensor networks,a virtual-force-integrated and multi-strategy enhanced slime mould algorithm(VCB-SMA)was proposed.The algorithm employs Logistic-Tent chaotic initialization to enhance population diversity and incorporates a virtual force mechanism to guide position updates.Concurrently,it utilizes Brownian motion perturbation and sine-cosine searching to balance global exploration with local development,thereby mitigating premature convergence.Simulation results demonstrate that VCB-SMA achieves the highest convergence speed and accuracy across four distinct types of benchmark functions,along with excellent optimization stability.In an obstructed monitoring environment of 100 m×100 m with 70 sensor nodes,the proposed algorithm attains a coverage rate exceeding 90%.It improves the average coverage by 44.35%,39.32%,37.71%,19.56%,and 3.18%compared to benchmark algorithms,respectively,achieving optimal blind area reduction.The approach simultaneously optimizes coverage rate,node movement distance,and redundancy,with all performance metrics surpassing those of comparative algorithms.