基于人工鱼群算法的出租车智能调度

出租车以其方便、快捷、舒适等特点成为城市公共交通工具的重要补充,但是当前出租车资源普遍存在着分布不合理、空载率高、营运效率低下等问题.本文运用群智能的思想,提出基于人工鱼群算法的出租车智能调度方法,以实现对出租车资源的全局调度与合理分配.论文提出标准人工鱼群算法中的觅食函数、聚群函数和追尾函数的改进方案以及当前最优状态阈值限定的优化策略,使得改进算法具有全局寻找最优解的良好搜索能力.同时通过模拟实验分析视野、尝试次数、拥挤度因子、迭代次数和游动步长等参数对出租车调度算法寻优能力的影响.研究成果为城市出租车资源得到科学合理规划以及全局优化分配提供新思路和依据.

Intelligent taxi dispatching based on artificial fish swarm algorithm

Taxi becomes an important supplement to urban public transportation because of its convenient, fast and comfortable features. Focusing on the issues of unreasonable resources distribution, high load rate and inefficient assignment of taxies, this paper proposes an approach to intelligent taxi dispatching based on artificial fish swarm algorithm (AFSA). Basic functions of foraging, clustering, rearing in AFSA are improved in the paper and an optimization strategy of setting state threshold is presented. Also, some key parameters, i.e. vision, trynum, crowd, maxgen and step, are analyzed under simulation experiments, which are related to optimization capabilities of taxi dispatching algorithm. The results show it would be helpful to make reasoning planning and allocation of global optimization on taxi resources.