基于精英的量子粒子群优化的Ad hoc能耗研究  

Energy Consumption in Ad hoc Based on Quantum-behaved Particle Swarm Optimization Elitist Learning Algorithm

作　　者：张峰[1,2] 贾智平[1] 蔡晓军[1] 张兰华[2] 

机构地区：[1]山东大学计算机科学与技术学院,济南250101 [2]泰山医学院信息工程学院,泰安271016

出　　处：《计算机科学》2014年第9期132-136,164,共6页Computer Science

基　　金：山东省自然科学基金(ZR2013FL031);国家安全生产重大事故防治关键技术科技项目(2013084)资助

摘　　要：在Ad hoc网络中,随着多播应用领域的日益扩大,如何构造最小能耗多播树是一个重要问题。针对选择不同的中继节点对构造最小能耗多播树产生的影响,提出了一种优化最小能耗多播树构造的基于精英学习的量子粒子群算法(QPELSO)。为了避免粒子群算法早熟收敛,采用动态逼近学习策略对精英个体进行局部更新,使其跳出局部极值点,引导种群进行有效搜索;借鉴群体早熟判断机制对停滞状态下的精英个体空间进行变尺度混沌扰动,增大种群全局搜索空间,有效平衡了算法的局部和全局搜索能力。模拟实验结果表明,改进后的粒子群算法具有较强的优化能力,并且有效地优化了最小能耗多播树的构造。In Ad hoc networks, with the emerging of multicast applications, how to construct a multicast tree of the minimum energy consumption is an important problem. For the effect of the different choices of relay nodes on the construction of the minimum energy consumption multicast tree, quantum-behaved particle and elitist learning swarm optimization algorithm （QPELSO） to optimize the construction of the minimum energy consumption multicast tree was proposed. In order to avoid the premature convergence of the particle swarm optimization algorithm. This method exerts the dynamic-approximation search strategy on the elitist particles to avoid them running into local optima and provides a good guidance for the population. While the algorithm is found to be in a dead state according to the premature judgment mechanism, the mutative-scale chaotic perturbation is used to exhibit a wide range exploration and keep the balance of exploration and exploitation. The results of simulated experiments show that the modified discrete particle swarm optimization algorithm has strong optimization ability, and can effectively optimize the construction of the minimum energy consumption multicast tree.

关 键 词：Ad HOC网络 精英学习 量子粒子群优化 多播路由 最小能耗 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]