Evolutionary Computation for Dynamic Optimisation in Network Environments

网络环境下优化问题的研究历史悠久，但由于通常假设网络环境（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）都是事先已知的，并且在解决问题的过程中保持不变，因此通常无法捕捉到现实场景。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，用户需求等是不可预测的，随着时间的推移而变化，和/或先验未知。这给决策者带来了许多困难，产生了重大的优化挑战。本研究旨在探讨各种网路环境下的动态nop （Dynamic nop）。我们将深入研究网络环境和问题中的动力学。dnop出现在广泛的应用领域，如通信网络、传输网络、社交网络和金融网络。我们的理论研究将寻求适用于多个应用领域的基础见解，而我们的应用研究将侧重于铁路网和电信网。进化计算（EC）包含许多研究领域，它应用自然界（特别是生物学）的思想来解决优化和搜索问题。电子商务已经成功地应用于许多现实世界的场景，特别是那些困难和具有挑战性的问题，以及那些难以精确定义的问题。本项目旨在研究解决dnop的EC方法。我们的目标是通过实证和理论研究，深入了解不同的EC方法如何应用于dnop。重要的是在理论和实证两个层面上开展这项研究，因为两者可以相互促进。我们将与工业合作伙伴（例如，铁路安全和标准委员会和网络铁路）合作，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建立基本的理论模型，然后在他们提供的现实世界数据上进行验证。无论是从现实世界的角度来看，还是从推进我们的理论认识的角度来看，这个项目都有很大的潜力从根本上改变对待DNOPs的方式。我们计划与我们的工业伙伴合作，为我们的工业伙伴开发一个原型系统。为了测试和评估我们新开发的DNOP算法，我们将开发一套常见的DNOP模型来捕捉现实世界的复杂性，并开发先进的EC方法来解决这些DNOP模型。这将使更广泛的研究团体受益，因为从通信网络到运输网络，从社交网络到金融网络，dnop在许多不同领域都无处不在。该项目的研究结果也将为许多涉及dnop的行业带来重大利益，并从成本和环境的角度来看，都将大大节省成本。

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.