论文标题
贝叶斯分层多目标优化用于车辆停车路线发现
Bayesian Hierarchical Multi-Objective Optimization for Vehicle Parking Route Discovery
论文作者
论文摘要
对于任何在一天的高峰时段和拥挤的地方进一步加剧的驾驶员来说，发现通往最可行的停车场的最佳途径一直是一个问题。本文提出了一种贝叶斯分层技术，以获取通往停车场的最佳路线。路由选择基于相互冲突的目标，因此该问题属于多目标优化的领域。概率数据驱动方法已被用来克服流行的加权总和技术中固有的体重选择问题。这些相互矛盾的目标的权重已经使用了基于多项式和迪里奇的贝叶斯分层模型来完善。遗传算法已用于获得最佳溶液。模拟数据已用于获取与现实生活中有密切一致的路线。
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.
