运用协同分布估计算法优化交通调度问题的研究

项目名称： 运用协同分布估计算法优化交通调度问题的研究

项目编号： No.61502542

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 自动化技术、计算机技术

项目作者： 龚月姣

作者单位： 华南理工大学

项目金额： 22万元

中文摘要： 传统上集中式的智能优化算法在求解大规模高维交通调度问题时的求解效率和寻优效果还有待改进。针对这一问题，本项目开展基于协同分布估计算法的交通调度方案研究，结合分布式计算资源对复杂的调度问题分而治之以提高算法求解效率，并进一步通过协同进化策略加强算法的全局寻优能力。提出基于密度聚类的问题空间解耦策略，提高分治法的有效性和对不同实例的适用性；设计多层树状处理器拓扑结构，实现对交通调度问题空间的逐层剖分优化，有助于同时加强分布估计算法的全局探索和局部寻优；研究异步反馈型通信协议，提高处理器间信息交互的有效性，提高算法分布式处理的效率；最终将所提出的算法运用于城市交通调度优化，充分考虑实际应用中的各项需求，在实践中检验算法性能，并提高城市交通调度的智慧化水平。

中文关键词： 分布估计算法；协同进化；分布式进化计算；交通调度；高维优化

英文摘要： When dealing with modern transportation scheduling problems with large-scale and high-dimensional problem spaces, traditional centralized optimization algorithms always endure low efficiency and weak global optimization ability. To solve this problem, this project conducts research into cooperatively coevolving estimation of distribution algorithm (EDA). Distributed computational resources are utilized by EDA to improve the computational efficiency in a way of divide-and-conquer, while the cooperative coevolution strategies are adopted to enhance the global exploration ability. First, we apply a density-based clustering approach to decouple the problem space, so as to improve the effectiveness and flexibility of dividing-and-conquering the problem. Then, a hierarchical topology is designed, by which different subcomponents of the problem are optimized from tier to tier, and from global to local. Meanwhile, an asynchronous back-propagation communication protocol is developed to improve the availability of message passing between processors, resulting in high distributed efficiency of the algorithm. Finally, the proposed algorithm will be applied to optimize the urban transportation scheduling problem by fully considering various requirements and conditions of this real-world application. The project is expected to achieve a new and powerful EDA, as well as to improve the intelligence level of urban transportation scheduling.

英文关键词： Estimation of Distribution Algorithm;Cooperate Coevolution;Distributed Evolutionary Computation;Transportation Scheduling ;High-Dimensional Optimization