改进的离散果蝇优化算法在WSNs覆盖中的应用

针对无线传感器网络( WSNs)随机部署产生的区域覆盖率低、节点利用率差问题,提出一种改进的离散果蝇优化算法( FOA)对WSNs覆盖进行优化.新算法引入自适应步长的分类嗅觉随机搜索和基于移民操作及精英库的多种群协同进化机制,提高了优化精度和效率.仿真实验结果表明:新算法有效解决了WSNs覆盖问题,在确保网络覆盖率最大化的同时节点利用率较大,延长网络寿命.

Application of improved discrete fruit-fly optimization algorithm in WSNs coverage

An improved discrete fruit-fly optimization algorithm( FOA)is proposed,aiming at problems of low area coverage rate and poor utilization rate of node caused by random deployment of wireless sensor networks ( WSNs). Adaptive classify smell-based random search step size and different random search methods for different steps adopted,migration-based operation and elite library are introduced in multiple population co-evolution mechanism,which improves precision and efficiency of optimization. Simulation result shows that this new algorithm effectively resolve problem of WSNs coverage,it has the highest nodes usage while maximizing the network coverage rate and extend the network lifetime.