基于贝叶斯分层多目标优化的车辆停放路径发现
Bayesian Hierarchical Multi-Objective Optimization for Vehicle Parking Route Discovery
摘要：
发现到最可行的停车场的最佳路线一直是任何驾驶员关注的问题，其在一天的高峰时间和拥挤的地方进一步加速，导致相当大的时间和燃料浪费。本文提出了一种贝叶斯分层技术，以获得最佳路径到停车场。路径选择是基于相互冲突的目标，因此该问题属于多目标优化领域。概率数据驱动的方法已被用来克服固有的问题的权重选择在流行的加权和技术。这些相互冲突的目标的权重已被细化使用贝叶斯分层模型的基础上多项式和狄利克雷先验。遗传算法已被用于获得最优解。模拟数据已被用来获得与现实生活中的情况非常吻合的路线。
英文摘要：
Discovering an optimal route to the most feasible parking lot has been a matter of concern for any driver which aggravates further during peak hours of the day and at congested places leading to considerable wastage of time and fuel. This paper proposes a Bayesian hierarchical technique for obtaining the most optimal route to a parking lot. The route selection is based on conflicting objectives and hence the problem belongs to the domain of multi-objective optimization. A probabilistic data driven method has been used to overcome the inherent problem of weight selection in the popular weighted sum technique. The weights of these conflicting objectives have been refined using a Bayesian hierarchical model based on Multinomial and Dirichlet prior. Genetic algorithm has been used to obtain optimal solutions. Simulated data has been used to obtain routes which are in close agreement with real life situations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
