混合粒子群-蝴蝶算法的WSN节点部署研究

针对无线传感器网络WSN节点在随机部署时存在分布不均匀、覆盖率低等问题，提出一种混合粒子群-蝴蝶算法HPSBA的节点覆盖优化策略。设计了Logistic映射和自适应调节策略来控制参数值，提高了混合算法的寻优速度、收敛精度和全局搜索能力。将HPSBA用于对4种基准函数进行寻优测试，仿真结果表明：对于测试函数的寻优，HPSBA具有寻优精度较高、寻优速度较快和稳定性较好的特点。最后，将HPSBA用于WSN节点部署优化，并与PSO算法、BOA和IGWO算法等其他6种典型算法的部署优化进行对比，实验结果表明：HPSBA优化部署的覆盖率更高，能有效降低节点的冗余度，延长WSN的生存时间。

A hybrid particle swarm-butterfly algorithmfor WSN node deployment

Aiming at the problems of uneven distribution and low coverage when nodes are randomly deployed in wireless sensor network (WSN), a hybrid particle swarm-butterfly algorithm (HPSBA) is proposed for node deployment optimization. Firstly, logistic mapping and adaptive adjustment strategies are designed to control parameter values, so that the optimization speed, convergence accuracy and glo- bal search capability of HPSBA are improved. Then, four benchmark functions are used to analyze the performance of HPSBA. The simulation results show that HPSBA has higher optimization accuracy, faster optimization speed, and better stability. Finally, HPSBA is used in WSN node deployment optimization and compared with other six typical algorithms such as PSO, BOA, IGWO and so on. The results show that HPSBSA has higher coverage rate, which can effectively reduce the redundancy of nodes and prolong the survival time of WSN.