A Robust Clustering Architecture for Medium Scale Underwater Acoustic Sensor Networks

针对现有水下传感器网络分簇算法负载不均衡和生命周期较短的问题,基于粒子群优化算法和遗传算法的基本思想,提出一种全局优化的智能分簇算法.为了使粒子; 初始化编码较为合理公平,根据节点近期当选过簇首的次数动态调整节点选举概率;通过对粒子整个编码区域进行循环搜索来捕获一个优良的随机交叉片段,保证了; 交叉后的粒子含有一定数量的历史较优簇首信息;通过节点编码位的变异提高算法的探索性,并确保解空间的存在性;在粒子评价函数中综合考虑簇首能量、负载均; 衡和分簇范围3个优化子目标.仿真结果表明,提出的算法更好地均衡了簇首负载,同时有效减少了网络能耗,延长了网络生命周期.For the purpose of aiming at the problem that the load imbalance and the; short life cycle of the existing underwater sensor network clustering; algorithm,a new clustering algorithm based on global optimization is; proposed.The proposedalgorithm isinspired by the basic idea of particle; swarm optimization (PSO) algorithm and genetic algorithm (GA).First,a; particle encoding method is designed.It dynamically adjusts the; probability of node election according to the recently elected cluster; head number.Then,a new method is designed to capture a good random cross; fragment in the whole coding region of the particle by the method of; cyclic search,which ensures that the cross transformed particles contain; a certain amount of history and the best information.In addition,by the; mutation of the node coding bits,the exploratory of the algorithm is; improved,and also ensures the existence of the solution; space.Finally,three optimization sub objectives,cluster head energy,load; balance and cluster range,are considered synthetically in the particle; evaluation function.Simulationresults show that the proposed algorithm; more effectively balances the load of cluster heads,and reduce the; network energy consumption,and prolong the network life cycle.国家自然科学基