论文标题
网络连通性优化：对应用于复杂网络的启发式方法和运输案例研究的评估
Network connectivity optimization: An evaluation of heuristics applied to complex networks and a transportation case study
论文作者
论文摘要
网络优化通常集中在解决网络流问题上，但是最近已经进行了调查以优化网络特征。优化网络连接性以最大化给定距离与焦点节点内的节点数量，然后最小化附加连接的数量和长度，但尚未得到彻底探索，但在包括运输计划，电信网络和地理空间分析在内的多个领域中很重要。我们将几种启发式方法与使用随机网络的使用进行比较，以探索该网络连接优化问题，包括引入两个用于空间网络仿真研究有用的平面随机网络，以及城市规划和公共卫生的真实案例研究。我们观察到网络类型之间的节点特性和最佳连接之间存在显着差异。该结果以及寻找最佳解决方案的计算成本突出了寻找有效启发式方法的困难。提出了一种新型的遗传算法，我们发现这种优化的启发式优于现有技术，并描述了如何将其应用于其他组合和动态问题。
Network optimization has generally been focused on solving network flow problems, but recently there have been investigations into optimizing network characteristics. Optimizing network connectivity to maximize the number of nodes within a given distance to a focal node and then minimizing the number and length of additional connections has not been as thoroughly explored, yet is important in several domains including transportation planning, telecommunications networks, and geospatial analysis. We compare several heuristics to explore this network connectivity optimization problem with the use of random networks, including the introduction of two planar random networks that are useful for spatial network simulation research, and a real-world case study from urban planning and public health. We observe significant variation between nodal characteristics and optimal connections across network types. This result along with the computational costs of the search for optimal solutions highlights the difficulty of finding effective heuristics. A novel genetic algorithm is proposed and we find this optimization heuristic outperforms existing techniques and describe how it can be applied to other combinatorial and dynamic problems.
