About Us

Introduction

Nature-inspired algorithms are a family of optimisation techniques including genetic algorithms, particle swarm optimisers and ant colony optimisers. Our specialisms include the development of multi-objective and many-objective algorithms, visualising solution sets and performance/processes, and applying our techniques to real-world problems.

Find out more about our team and our publications.

Methodological Research

Our methodological research includes

  • Using visualisation to explain the processes used to generate solutions to optimisation problems and present the solutions to domain experts.
  • Development of novel algorithms and incorporating machine learning into the evolutionary process.
  • Human-in-the-loop optimisation – including a human decision maker in the optimisation process.

Some projects

Applications

We are working in a range of application areas, including:
  •  The water sector, optimising the design of water network infrastructure
  • Cryptography, the breaking of proposed cryptographic key exchanges on groups via evolutionary algorithms and machine learning
  • The offshore renewables sector, optimising the design of floating offshore windfarms and developing models of their performance and environmental impact

Some projects

Popular posts from this blog

New PhD Students: Pawel Manikowski and Rahul Soni

We're very pleased to welcome two new PhD students to the group this week. Pawel Manikowski will be investigating the optimisation of floating offshore wind farms, and is supported by an EPSRC DTP fellowship. His work will be looking at modelling the movement of these floating structures, as well as optimising their placement of individual turbines within a floating wind farm. Floating wind farms are a nascent technology that holds the promise of greatly expanding renewable energy generation. Rahul Soni will be working on a project investigating the use of hyper-heuristics, combining machine learning with optimisation, to solve multi-objective portfolio optimisation problems and exploring novel algorithms in this area. We are looking forward to sharing the results of their research with you soon!
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New PhD Student: Mathew Walter

This week we welcomed our latest PhD student to the group. Mathew Walter is going to be working on visualising the processes that are used to generate solutions in evolutionary algorithms with the aim of making optimisation processes more explainable. Mathew joins us having graduated from Plymouth with a degree in Mathematics, and is supervised by David Walker and Matthew Craven. We look forward to sharing the outputs from his research with you soon.
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