摘要
针对无线传感器网络中k重覆盖率、能耗、可靠性难以协调的问题,在节点呈泊松分布的假设下,提出了多目标优化的覆盖控制。针对多目标差分进化算法在种群初始化、参数控制和种群维护中的不足,分别设计了种群正交初始化、参数自适应控制和动态种群维护策略,提出了改进的多目标差分进化(I-DEMO)算法对模型进行求解。仿真结果表明,该控制策略能够在达到81.2%的3重覆盖率的同时有效降低能耗并保障可靠性,I-DEMO可以支配传统算法76%的Pareto前沿。该算法同样适用于求解其他多目标问题。
A multi-objective optimization coverage control was proposed for solving the intractable problem of k-coverage rate, energy consumption and reliability in wireless sensor networks on the assumption that nodes are in Poisson distribution. In order to overcome the shortcomings of population initialization,parameter control and population maintenance in multi-objective differential evolution algorithm,the author designed tactics of swarm orthogonal initialization, parameter self-adaptive control and dynamic swarm maintenance strategy separately, and an improved multi-objective differential evolutional algorithm （I-DEMO） was proposed to solve this model. The results show that the control strategy can effectively achieve the three-coverage rate of 81.2%, reduce the energy consumption effectively, and ensure the reliability. This algorithm can dominate 76% Pareto fronts of the traditional algorithm and be applied to the solution of other multi-objective problems.
出处
《计算机应用》
CSCD
北大核心
2013年第7期1820-1824,1832,共6页
journal of Computer Applications
基金
北京市教育委员会共建项目
关键词
无线传感器网络
泊松分布
k重覆盖率
能耗
可靠性
多目标差分进化算法
Wireless Sensor Network （WSN） Poisson distribution k-coverage rate energy consumption reliability multi-objective differential evolution algorithm
