基于信息素算法的校园物联网多路传输优化

Multi-channel Transmission Optimization of Campus Internet of Things Based on Pheromone Genetic Algorithm

针对校园物联网信息多路传输选取和优化困难的问题,提出一种基于智能优化算法的网络多路传输优化算法。该算法以标准遗传算法为基础,借鉴蚁群算法中的信息素浓度概念,通过控制个体的进化方向提升算法全局寻优能力和收敛效率,并设计和构建了符合物联网信息多路传输优化特点的评估指标数学模型,实现了基于熵权理想点法的网络多路传输综合评分,最终通过多代进化获取满足工程需求的最优网络路径。仿真结果表明:信息素遗传算法较标准遗传算法的全局寻优能力更强,收敛速度更快,为校园物联网信息多路传输优化问题提供了可行解法。

Aiming at the difficulty in selecting and optimizing the multi-path transmission of campus Internet of Things information, an optimization algorithm of network multi-path transmission based on intelligent optimization algorithm is proposed. Based on the standard genetic algorithm and the concept of pheromone concentration in ant colony algorithm, this algorithm improves the global optimization ability and convergence efficiency by controlling the evolution direction of individuals, and designs and constructs an evaluation index mathematical model which conforms to the characteristics of multi-channel information transmission optimization in the Internet of Things. The mathematical model of the evaluation index realizes the multi-channel comprehensive scoring based on the entropy weight ideal point method.Finally the optimal network path that meets the engineering requirements is obtained through multi-generation evolution. The simulation results show that the pheromone genetic algorithm has stronger global optimization ability and faster convergence speed than the standard genetic algorithm,which provides a feasible solution for the multiplex optimization problem of the campus IoT information.