基于量子遗传算法的无线传感器网络路径优化

Wireless Sensor Network Path Optimization Based on Quantum Genetic Algorithm

无线传感器网络作为一种有广泛应用前景的新型信息获取和处理技术,引起了国内外学术界和产业界的高度关注;文章采用量子遗传算法对无线传感器网络进行路径优化,将节点能耗和路由恢复时间等实际因素作为路径优化问题的约束条件,按照重要性考虑各种约束条件和多种目标的优先次序,并对量子遗传算法的各个环节进行了细致的分析与设计,包括量子比特编码、适应度函数设计、量子门更新、量子变异等;仿真实验表明,使用量子遗传算法能找到无线传感器网络有效的优化路径,并且优于相关算法。

Wireless sensor networks are widely used as the prospect of a new type of information acquisition and processing techniques, both at home and abroad has aroused academic and industry attention. In this paper, quantum genetic algorithm is used to optimize the path in wireless sensor networks, and use energy consumption and routing node recovery time and other factors as the actual path of the con- straint optimization problem, in accordance with the importance of considering the various constraints and the priority of multiple goals. Quantum Genetic Algorithm on all aspects is analyzed in detail, including the quantum encoding, fitness function design, update quantum gates, quantum and other variations. The simulation results show, quantum genetic algorithm is an effective measure of optimization of wireless sensor networks routing, and better than the correlation algorithm.