%0 Journal Article
%T 采用元胞遗传算法的无线电能传输网路径寻优<br>Path Optimization for Wireless Power Transfer Networks with Cellular Genetic Algorithm
%A 孙跃
%A 夏金凤
%A 唐春森
%A 向丽娟
%J 西安交通大学学报
%D 2017
%R 10.7652/xjtuxb201704005
%X 为快速寻找无线电能传输网中传输效率最大、能量损耗最小的传输路径,提出了使用元胞遗传算法对传输路径进行寻优的方案。通过对无线电能传输网数学模型的分析,分别建立了有源注入和无源注入无线电能传输网的目标函数,并将目标函数作为元胞遗传算法的适应度函数,采用重复消除的修复方案将不可行解转化为可行解,以提高种群中的有效个体比例,从而提高搜索效率。元胞遗传算法将遗传算法和元胞自动机进行有效结合,中心个体和邻居之间形成一个小生境,优良个体得以在种群中缓慢扩散,算法能够有效突破局部最优解的限制,迅速收敛至全局最优解。仿真结果显示,在有源注入和无源注入无线电能传输网中,元胞遗传算法都能准确找到最优路径,表明元胞遗传算法用于无线电能传输网路径寻优具有可行性。<br>To quickly find a path of wireless power transfer networks, which can maximize the transmission efficiency and minimize electrical energy loss, the cellular genetic algorithm (CGA) is chosen to optimize the transfer link. Analyzing the mathematical model of wireless energy transmission network, the objective function of the active injection and the passive injection wireless power transfer networks is established, and the objective function is taken as the fitness function of the cellular genetic algorithm. To heighten the proportion of effective individuals in the population and improve the searching efficiency, a duplicate elimination scheme is proposed to transform infeasible solution into feasible solution. Genetic algorithms and cellular automata are combined by the cellular genetic scheme, a niche thus forms between individuals and their neighbors, the good individuals spread slowly in the population, so the algorithm can effectively break the restriction of local optimal solution and quickly converge to the global optimal solution. Simulation results show that the cellular genetic algorithm can accurately find the optimal path in both the active injection and the passive injection wireless power transfer networks, which verifies the feasibility of this cellular genetic algorithm for path optimization of wireless power transfer networks
%K 无线电能传输
%K 无线电能传输网
%K 路径优化
%K 元胞遗传算法&lt
%K br&gt
%K wireless power transfer
%K wireless power transfer networks
%K path optimization
%K cellular genetic algorithm
%U http://zkxb.xjtu.edu.cn/oa/DArticle.aspx?type=view&id=201704005