基于差分演化和粒子群优化的改进WSN覆盖算法  

Improved WSN Coverage Algorithms Based on Differential Evolution and Particle Swarm Optimization

作　　者：易文周[1] YI Wen-zhou(School of Information Engineering, Guangdong Engineering Polytechnic College, Guangzhou 510520, China)

机构地区：[1]广东工程职业技术学院信息工程学院

出　　处：《计算机与现代化》2019年第8期33-38,62,共7页Computer and Modernization

基　　金：广东省科技计划项目(2016B090918021)

摘　　要：动态部署传感器节点随机性大,无法保证特定目标区域的覆盖质量,引入智能优化算法后有效提高了节点动态部署的质量,但一般的智能优化算法在动态部署时存在“早熟”等缺陷。为了进一步提高节点动态部署的质量,针对节点的覆盖问题进行研究,结合粒子群优化和差分演化的优点,前期用粒子群优化算法,发挥粒子群擅长前期搜索收敛较快的特点,后期用差分演化算法,发挥差分演化擅长局部搜索的特点,这样取双方所长,克服双方所短,从而使算法有更好的搜索能力。仿真结果表明,本文提出的算法相对于改良惯性权重的粒子群算法、结合虚拟力的粒子群算法以及基本差分演化算法,具有更好的搜索能力,优化后的网络覆盖率更高。Dynamic deployment of sensor nodes is random, which can not guarantee the coverage quality of specific target areas. Intelligent optimization algorithm is introduced to effectively improve the quality of dynamic deployment of sensor nodes. However, the general intelligent optimization algorithm has some shortcomings such as “premature” in dynamic deployment. In order to further improve the quality of dynamic deployment of nodes, this paper studies the coverage problem of nodes, combines the advantages of particle swarm optimization and differential evolution, uses particle swarm optimization in the early stage to give full play to the characteristics of particle swarm optimization which is good at global search, and uses differential evolution algorithm in the later stage to give full play to the characteristics of differential evolution which is good at local search, so as to take the advantages of both and overcome the shortcomings of both. The algorithm has better search ability. The simulation results show that the new algorithm has better search ability and better network coverage than the improved inertia weight particle swarm optimization algorithm, the virtual force particle swarm optimization algorithm and the basic differential evolution algorithm.

关 键 词：无线传感网络 粒子群算法 差分演化算法 节点覆盖 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]