基于改进鲸鱼优化算法的WSN覆盖优化

针对无线传感器网络在随机部署移动节点时,存在分布不均匀导致的覆盖率较低的问题,以网络覆盖率最大化为目标建立网络覆盖优化模型,提出一种基于改进鲸鱼优化算法(IWOA)的网络覆盖优化策略;首先,采用量子位Bloch球面坐标编码初始化种群,提升种群多样性,扩展搜索空间的遍历能力;其次,提出一种基于步长改进的位置更新方式,平衡算法的全局探索和局部搜索能力;最后采用莱维飞行,对个体进行扰动更新,提高跳出局部最优的能力。仿真结果表明,将改进后的鲸鱼优化算法应用在WSN覆盖优化中,与标准鲸鱼优化算法和其他文献中的算法相比,有效减少了传感器节点冗余,表现出更快的收敛速度和更高的覆盖率,进而改善网络监测质量,延长网络生存时间。

WSN Coverage Optimization Based on Improved Whale Optimization Algorithm

Aiming at the problem of low coverage caused by uneven distribution in random deployment of mobile nodes in wireless sensor networks,a network coverage optimization model is established to maximize network coverage,and a network coverage optimization strategy based on improved whale optimization algorithm(IWOA)is proposed.Firstly,this algorithm improves the diversity of the population and expands the traversal ability of the search space by using Bloch coordinates of quantum bit encoding initial population.Secondly,an improved position updating method based on step size is proposed to balance the global exploration and local search ability of the algorithm.Finally,levy flight is used to perturb individual position update and improve the ability of jumping out of local optimum.The simulation results show that compared with the standard whale optimization algorithm and other algorithms in the literature,the improved whale optimization algorithm used in WSN coverage optimization reduces sensor node redundancy effectively,shows faster optimization speed and higher coverage rate,improves network monitoring quality and lifetime.