In video-on-demand (VoD) services, large volumes of digital data are kept at hubs which are spatially distributed over large geographic areas and users are connected to these hubs based on their demands. In this article, we consider a large database of video files, that are pre-partitioned to multiple segments based on the demand patterns of users. These segments are restricted to be located only in hubs. Here, users are allowed to be allocated to multiple hubs and all hubs are assumed to be connected with each other. We jointly decide the location of hubs, the placement of segments to these hubs and then the assignment of users to these hubs as per their demand patterns and finally, we find the optimal paths to route the demands of users for different segments having the objective of minimizing the total routing cost. In this article, a differential evolution (DE) based method is proposed to solve the problem. The proposed DE-based method utilizes an efficient function to evaluate the objective value of a candidate solution to the proposed problem. It also incorporates two problem-specific solution refinement techniques for faster convergence. Instances of the problem are generated from the real world movie database and the proposed method is applied to these instances and the performance is evaluated against the benchmark results obtained from CPLEX.

在视频点播（VoD）服务中，大量数字数据保存在集线器中，这些集线器在空间上分布在较大的地理区域，并且根据用户的需求将用户连接到这些集线器。在本文中，我们考虑了一个大型的视频文件数据库，该数据库根据用户的需求模式预先划分为多个分段。这些网段仅限于位于集线器中。在此，允许将用户分配给多个集线器，并且假定所有集线器都相互连接。我们共同决定集线器的位置，这些集线器的细分位置，然后根据其需求模式将用户分配给这些集线器，最后，我们找到了最佳途径来满足不同细分市场的用户需求，目标是最小化总路由成本。在这篇文章中，提出了一种基于差分进化（DE）的方法来解决该问题。所提出的基于DE的方法利用有效函数来评估所提出问题的候选解决方案的目标值。它还结合了两种针对特定问题的解决方案优化技术，以实现更快的收敛。从真实世界的电影数据库中生成问题的实例，然后将建议的方法应用于这些实例，并根据从CPLEX获得的基准结果评估性能。