CAJ | 학술논문

　　研究使用混合 GA-BP 神经网络算法来解决交通路径规划中的非线性问题。反向传播(Back-Propagation, BP)神经网络虽然能够很好地解决非线性问题,但它存在着容易陷入局部极小的不足,而遗传算法(Genetic Algorithm, GA)具有很强的宏观搜索能力和良好的全局优化性能,可以弥补BP的不足。用A*算法快速粗算出的几条可选路径作为 GA 的初始种群,然后用混合的 GA-BP 神经网络算法进行路径规划精算。仿真结果显示混合GA-BP神经网络算法在寻找路径规划的全局最优解上具有一定的优势。
　　연구사용혼합 GA-BP 신경망락산법래해결교통로경규화중적비선성문제。반향전파(Back-Propagation, BP)신경망락수연능구흔호지해결비선성문제,단타존재착용역함입국부겁소적불족,이유전산법(Genetic Algorithm, GA)구유흔강적굉관수색능력화량호적전국우화성능,가이미보BP적불족。용A*산법쾌속조산출적궤조가선로경작위 GA 적초시충군,연후용혼합적 GA-BP 신경망락산법진행로경규화정산。방진결과현시혼합GA-BP신경망락산법재심조로경규화적전국최우해상구유일정적우세。
In this paper, we solved nonlinear problems in the traffic path planning with the hybrid GA-BP neural network algorithm. Although Back-Propagation neural network (BP) is able to solve nonlinear problems properly, it is tend to fall into the deficiency of local minimum. In the meanwhile, genetic algorithm (GA) is good at macro-searching and performs well at global optimization, which can make up for the deficiencies of BP. In this paper, using the A *algorithm, we rough calculated several alternative paths quickly, which serve as the initial population of the GA. Then we conducted path planning precisely with the mixed GA-BP neural network algorithm. The simulation results showed that the hybrid GA-BP neural network algorithm has some advantages in the global optimal solution for path planning.