基于菌群自适应觅食的公交调度建模仿真与优化  

Adaptive Bacterial Foraging Optimization and Its Application for Bus Scheduling

作　　者：高政威[1] 庞哈利[1] 汪定伟[1] 

机构地区：[1]东北大学信息科学与工程学院,沈阳110004

出　　处：《系统仿真学报》2011年第6期1151-1155,1160,共6页Journal of System Simulation

基　　金：国家自然科学基金重点项目资助课题(70931001);国家自然科学基金创新群体项目(60821063)

摘　　要：提出了一种新型群体智能优化方法—菌群自适应搜索算法(Adaptive Bacterial Foraging Optimization,ABFO)。ABFO算法在细菌觅食算法(Bacterial Foraging Optimization,BFO)研究工作的基础上,将细菌的趋化行为,群体感应机制和自适应搜索策略相集成,体现细菌个体通过信息交流与合作在群体层面表现出更高智能行为的特性。此外,ABFO算法的自适应策略动态地控制人工细菌的趋化步长,从而能够在运行时有效地平衡算法的探索和开发能力。然后,基于ABFO进行公交调度模型的求解,以便快速地集散交通流,实现站点、客流、车流的优化配置。仿真试验结果表明,与其它智能算法相比ABFO算法具有收敛速度快,寻优能力强和较强的鲁棒性等优越性能。A variation on the original BFO algorithm,called the Adaptive Bacterial Foraging Optimization（ABFO） was proposed,which employed chemotactic behavior,quorum sensing,and adaptive foraging strategy to significantly improve the performance of the original algorithm.This improvement was achieved by enabling the bacterial foraging algorithm to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff.ABFO was then applied to schedule the bus departing interval by minimizing the average of passengers＇ waiting time and public transportation company＇s total running times in one day.Simulation results demonstrate that the ABFO algorithm is more feasible and efficient.

关 键 词：ABFO算法 细菌趋化 群体感应 自适应搜索策略 公交调度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]