Evolutionary Computation for Dynamic Optimisation in Network Environments

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.

This project will provide significant economic and social benefits in the generation and analysis of evolutionary computation (EC) methods for dynamic network optimisation problems (DNOPs). The main beneficiaries, how they will benefit from this research, and relevant activities are summarized as follows.

1. The academic community: DNOPs have not been well understood in research. The DNOP models and advanced EC methods that we will develop in this project will be very useful for researchers in DNOPs and EC to pursue their research and hence benefit them directly. This project will also open new frontiers for computer scientists and mathematicians to analyse EC methods for DNOPs. We will publish in highly rated journals and conferences, put project materials online, and interact with the research communities (through conferences, seminars and visits) to disseminate our research results to the academic community.

2. Benefits to the UK: The potential economic benefits to the UK are significant, including more effective and efficient dynamic optimisation software and decision making processes for many industries in communication, transport, social, and financial networks, etc. For example, the DNOP models and EC methods developed will help to manage the traffic in UK railway networks more efficiently, which will lead to increased capacity and customer satisfaction and reduced costs and carbon emissions. Utilising the EC methods to be developed in this project will also enable UK researchers and practitioners to improve their processes to maximise the usage of resources (staff, computers, vehicles etc). More effective DNOP algorithms will increase the competitiveness of UK network industries internationally.

3. Industry partners: The close collaboration with industrial partners via PDRA secondments, industry-focussed workshops, regular project meetings and additional email/phone communications, ensures the development of effective and efficient algorithms for dealing with real-world DNOPs, and thus benefit the industry directly. The prototype software that we will develop for railway networks will enable Network Rail to test and evaluate our system themselves in a realistic environment. Even a small improvement in Network Rail's operations by using our system could mean millions of pounds of real savings. The success with one industrial partner, such Network Rail, can set a good example and inspire other companies to also exploit the knowledge and technologies developed from this project.

4. Education and skill training: PDRAs on this project will gain significant expertise in EC and its innovative applications to DNOPs. They will be armed with skills and knowledge in both theoretical analysis and applied research. If they move into industry after the project, they will have significant impact on industrial research and development because their theoretical insight gained from the project will help them to understand the real world problems better and hence provide more better solutions. If they move into academia, their experience in working with industrial partners and real world problems will help them in formulating new research problems that model the real world better and in focusing their research effort on the most pressing needs of the industry. Furthermore, the knowledge gained in this project can also be incorporated into relevant BSc and MSc modules at Brunel and Birmingham. Both universities have several modules that are relevant to EC and optimisation.

5. The general public: This project is likely to produce interest amongst the media and general public since railway transport touches the life of millions in the UK. Our carefully designed public engagement activities will use railway networks as an example to illustrate the importance of scientific research and the potential benefits of scientific research to our economy and society in general.