Routing of Military Communication Networks Based on Cloud Genetic Algorithm
Lei Ming, Wang Heng, Mao Shao-jie, Yi Kan, Jinfeng Zhang · International Journal of Hybrid Information Technology · 2017
This paper aims at practical application needs of information transmission in existing military communication networks, and proposes routing models of military communication networks that satisfy military operational requirements.As routing is NP-hard problem, it has problems such as easily being early-maturing and caught in local optimum if traditional genetic algorithms are used to solve this kind of problem.Targeting at problems above, a CGA algorithm for solution is designed, which is based on excellent characteristics of cloud model and integrates with basic principles of genetic algorithm.On the basis of cloud model, CGA algorithm can dynamically adjust the crossover probability and mutation probability according to its fitness value, which shows self-adaptive characteristics of genetic parameters and reflect randomness and uncertainty during natural search process; thus the method is with good tendency and can promote diversity of population, and it not only improves the search efficiency, speeds up convergence, but also prevents algorithm from local optimum.The algorithm is then applied to route selection of the military communication network in a C4ISR simulation system.Experimental results show that the algorithm features a better convergence, and that although the run time of CGA algorithm is longer than that of AGA, the increment rate is within the tolerance interval since it does not exceed 10%.