Computational Optimisation and Applications

Research of the Computational Optimisation and Applications (COPA) group focuses on the development and application of heuristic search and optimisation methods. These methods include genetic algorithms, simulated annealing, ant colony optimisation and tabu search.

Many scientific and engineering problems can be viewed as search or optimisation problems, where an optimum input vector for a given system has to be found in order to optimise the system response to that input vector. Often, auxiliary information about the system, such as its transfer function and derivatives, is not known, also various measures might be incomplete and distorted by noise. This makes such problems difficult to solve by traditional methods. In such cases, approaches based on computational optimisation techniques have been shown to be advantageous compared to classical approaches. Problems amenable to solution by heuristic search and computational optimisation techniques occur in all areas of science and engineering where an optimum design of a component or product, or optimum system input or response, is required.

Recent work carried out by the group includes automated tuning of plasma probes in real-time, optimum design of structures, roll profiles in steel mills, and optimum scheduling of power plant bocks.

The group is actively involved in collaborative research with several industrial partners and universities across Europe. The group welcomes new contacts with researchers and industrial personnel.