摘要
为了使无线传感器网络的覆盖率和能耗达到最优的效果,提出了一种改进的多目标粒子群优化算法,采用量子粒子群优化更新粒子,利用拥挤距离排序策略,并结合适应度函数值优劣特性对多目标矛盾的性能目标选择,同时通过拥挤距离对加速系数自适应调整提高算法搜索能力,得到了逼近真实前沿的Pareto解集,具有更快的收敛速度和更强的寻优能力.通过对比实验结果表明:提出的算法在解决WSN的多目标优化问题时,能够避免算法陷入局部最优解,更好地平衡网络覆盖和动态通信能耗,使整个网络的综合指数达到了6.249,均明显优于其他三种算法.
In order to optimize the coverage and energy consumption of wireless sensor networks,an improved multi-objective particle swarm optimization algorithm was proposed.The quantum particle swarm optimization was used to update the particles,and the performance objectives of multi-objective contradictions were selected using congestion distance ranking strategy and fitness nction value features,meanwhile,the acceleration coefficient was adaptively adjusted to improve the search ability of algorithm by congestion distance.The Pareto solution set closed to the real frontier was obtained,which had faster convergence speed and stronger searching ability.The contrast experiment results show that the proposed algorithm can avoid falling into local optimum solution,balance network coverage and dynamic communication energy consumption better,and make the comprehensive index of the whole network reach 6.249,which is obviously superior to the other three algorithms.
作者
程春
CHENG Chun(School of Information Engineering,Henan Mechanical and Electrical Vocational College,Zhengzhou 451191,China)
出处
《数学的实践与认识》
北大核心
2020年第4期154-161,共8页
Mathematics in Practice and Theory
关键词
无线传感器网络
量子粒子群优化
多目标优化
网络覆盖
动态通信能耗
拥挤距离
wireless sensor networks
quantum particle swarm optimization
multi-objective optimization
network coverage
dynamic communication energy consumption
congestion distance
