重采样和天牛须协同演化粒子群的WSN覆盖控制算法

为了最大程度提升无线传感器网络(WSN)的覆盖范围并降低能耗，延长网络生命周期，提出了基于重采样技术和天牛须搜索的协同演化粒子群优化(RBASPSO)算法来优化WSN的覆盖控制问题。重采样技术平衡了粒子群算法的全局搜索能力和收敛速度，增加了粒子群整体多样性，防止算法过早收敛，加强粒子在搜索过程中跳出低质量谷底的能力; 天牛须搜索依靠个体的两个触角搜索其邻域，增强了粒子群中单个粒子的搜索能力。RBASPSO算法采用覆盖率和节点休眠率的加权作为优化WSN覆盖控制的目标函数，通过重采样技术和天牛须搜索的协同演化，既加强了单个粒子的搜索能力，又确保粒子群的多样性及活跃性，提升WSN覆盖性能。实验结果表明，RBASPSO算法不仅能有效处理复杂多峰问题; 而且可以有效提高WSN网络覆盖率，延长网络生命周期。

Particle swarm optimization based on co-evolution of resampling and beetle antennae search for coverage control in WSN

In order to maximize the coverage ratio of Wireless Sensor Networks (WSN), reduce energy consumption, and extend the network life, a Particle Swarm Optimization algorithm (PSO) based on co-evolution of resampling technology and Beetle Antennae Search (BAS) is proposed to optimize the coverage control problem of WSNs. The resampling technology balances the global search capability and convergence speed of PSO, increases the overall diversity of the particle swarm, prevents early convergence, and strengthens the ability of particles to jump out of low quality bottom. BAS can search its neighborhood by relying on two tentacles of an individual, which enhances the search ability of each single particle. Based on the weighting objective function of coverage ratio and sensor sleep rate, the proposed algorithm not only strengthens the search ability of individual particles, but also ensures the diversity and activity of particle swarm to improve WSN network coverage performance. Experimental results show that our proposed algorithm can effectively deal with complex multi-peak problem, improve network coverage, and extend the network life cycle.