Evolutionary Computation for Dynamic Optimisation in Network Environments

网络环境中优化问题的研究有着悠久的历史，但它通常无法捕捉真实世界的场景，因为它通常假设网络环境（如网络拓扑结构，节点处理能力，干扰等）和优化问题（如用户需求）是预先已知的，并在问题求解过程中保持不变。然而，大多数现实世界的网络优化问题（NOP）是高度动态的，其中网络拓扑、资源的可用性、干扰因素、用户要求等，是不可预测的、随时间变化的和/或先验未知的。这给决策者带来了许多困难，产生了重大的优化挑战。本研究的目的是研究动态NOPs（DNOPs）在各种网络环境中。将深入研究网络环境和问题中的动态。DNOP出现在广泛的应用领域，诸如通信网络、传输网络、社交网络和金融网络。本项目的理论研究将寻求适用于多个应用领域的基本见解，而应用研究将侧重于铁路网络和电信网络。进化计算（EC）涵盖多个研究领域，它将自然界（特别是生物学）的思想应用于解决优化和搜索问题。EC已经成功地应用于许多真实的世界场景，特别是对于困难和具有挑战性的问题以及那些难以精确定义的问题。该项目旨在研究解决DNOP的EC方法。我们的目标是通过实证和理论研究，深入了解并进一步了解如何将不同的EC方法应用于DNOP。重要的是在理论和经验两个层面上开展这项研究，因为一个可以反馈到另一个。我们将与工业伙伴合作（例如，铁路安全和标准委员会和网络铁路），他们将验证我们的研究并参与我们的项目。我们可以利用他们的技能和专业知识来生成基础理论模型，然后可以根据他们提供的真实数据进行验证。无论是从现实世界的角度还是从推进我们的理论理解的角度来看，这个项目都有很大的潜力从根本上改变DNOP的处理方式。为了测试和评估我们新开发的DNOP算法，我们将开发一套通用的DNOP模型，以捕捉现实世界的复杂性，并开发高级EC方法来解决这些DNOP模型。由于DNOP在从通信网络到传输网络到社交网络到金融网络的许多不同领域中无处不在，这将使更广泛的研究社区受益。该项目的研究成果也将对许多涉及DNOP的行业产生重大影响，并将从成本和环境角度节省大量资金。

The research on optimisation problems in network environments has a long history but it generally fails to capture real-world scenarios as it usually assumes that both the network environments (such as network topologies, node processing capabilities, interference, etc) and the optimisation problems (such as the user requirements) are known in advance and remain unchanged in the problem-solving procedure. However, most real-world network optimisation problems (NOPs) are highly dynamic, where the network topologies, availability of resources, interference factors, user requirements, etc., are unpredictable, change with time, and/or are unknown a priori. This poses many difficulties for decision makers, generating significant optimisation challenges. This research aims to investigate Dynamic NOPs (DNOPs) in various network environments. The dynamics in both network environments and problems will be studied in depth. DNOPs occur across a wide range of application areas, such as communication networks, transport network, social networks, and financial networks. Our theoretical study in this project will seek fundamental insight that is applicable to multiple application areas, while our applied research will focus on railway networks and telecommunications networks.Evolutionary Computation (EC) encompasses many research areas, which applies ideas from nature (especially from biology) to solve optimisation and search problems. EC has been successfully applied to many real world scenarios, especially for difficult and challenging problems and those problems that are difficult to define precisely. This project aims to investigate EC methods for solving DNOPs. We aim to gain insight and further our understanding of how different EC methods can be applied to DNOPs via empirical and theoretical studies. It is important to carry out this research at both theoretical and empirical levels, as one can feed into the other. We will work with industrial partners (e.g., Rail Safety and Standards Board, and Network Rail) who will validate our research and participate in our project. We can utilise their skills and expertise in producing the underlying theoretical models, which can then be validated on real-world data supplied by them. This project has great potentials to fundamentally change the way in which DNOPs are treated, both from a real-world point of view and from the point of view of advancing our theoretical understanding. We plan to develop a prototype system, in collaboration with our industrial partners, for our industrial partners.In order to test and evaluate our newly developed algorithms for DNOPs, we will develop a set of common DNOP models that capture the real-world complexities, and develop advanced EC methods to solve these DNOP models. This will benefit wider research communities due to the ubiquity of DNOPs in so many different fields from communication networks to transport networks to social networks to financial networks. The research results of this project will also be of significant benefit to many industries that involve DNOPs and will provide significant savings both from a cost point of view as well as from an environmental perspective.