作　　者： ; ; ;

机构地区： 深圳信息职业技术学院

出　　处： 《系统工程》 2015年第4期120-125,共6页

摘　　要： 针对传统细菌觅食优化算法(Bacterial Foraging Optimization,BFO)缺乏信息交流,容易陷入局部最优且收敛缓慢的缺点,将群体全面学习策略、趋化步长自适应更新机制嵌入到原始BFO算法中,提出一种自适应全面学习细菌觅食算法(Adaptive Comprehensive Learning Bacterial Foraging Optimization,ACLBFO)。为了验证所提算法的有效性,将其应用于考虑时间窗的车辆路径规划问题,设计相应的编码机制、适应度函数构造。与基本的细菌觅食优化算法及两种改进的细菌觅食优化算法进行对比研究。实验结果表明,该算法更加适用于该类问题的求解,可以获得更快的收敛速度与求解精度。 Due to the lack of communication mechanism in the basic bacterial foraging optimization,BFO is liable to trap into local minima and has a slow convergence rate.To deal with those problems,this paper incorporates the comprehensive learning mechanism and adaptive updating mechanism into the basic BFO algorithm and thus a new variant of BFO is proposed,which is called Adaptive Comprehensive Learning Bacterial Foraging Optimization(ACLBFO).To demonstrate the efficiency of the proposed ACLBFO,it is applied to solving Vehicle Routing Problem with Time Windows(VRPTW).The corresponding encoding schema and fitness function strategy are also presented.Compared with traditional BFO and two other BFO algorithms,the proposed algorithm has the potential to solve Vehicle Routing Problem with Time Windows(VRPTW)with rapid convergence rate and solving accuracy.

关 键 词： 带时间窗车辆路径问题 细菌觅食算法 全面学习策略

领　　域： [理学] [理学] [交通运输工程]