In recent papers, [1, 2, 3], coevolutionary genetic algorithms have been used to solve so-called minimax problems from mechanical structure optimisation, constrained optimisation and scheduling domains. The applications have been quite successful, but the algorithms used require the minimax problems to have a certain property; the problem has to be symmetric in the two search-spaces. In the pre...

This thesis presents a set of rigorous methodologies for tuning the performance of algorithms that solve optimisation problems. Many optimisation problems are difficult and time-consuming to solve exactly. An alternative is to use an approximate algorithm that solves the problem to an acceptable level of quality and provides such a solution in a reasonable time. Using optimisation algorithms ty...

This paper presents two multilevel refinement algorithms for the capacitated clustering problem. Multilevel refinement is a collaborative technique capable of significantly aiding the solution process for optimisation problems. The central methodologies of the technique are filtering solutions from the search space and reducing the level of problem detail to be considered at each level of the s...

In this paper we show how a Problem Solving Environment (PSE) can be used to manage and steer numerical optimisation of a challenging problem from mechanical engineering running in parallel on a remote Grid resource. The industrial code used by Shell Global Solutions in their lubrication work is transformed from a serial code through an interactive PSE providing multivariate visualisations to a...

The Ant Colony Optimisation Algorithm (ACO) supports the development of a system for a multi-objective network optimisation problem. The ACO system bases itself on an agent’s population and, in this case, uses a multi-level pheromone trail associated to a cost vector, which will be optimised.

Bi-level optimisation problems have gained increasing interest in the field of combinatorial optimisation in recent years. In this paper, we analyse the runtime of some evolutionary algorithms for bi-level optimisation problems. We examine two NP-hard problems, the generalised minimum spanning tree problem and the generalised travelling salesperson problem in the context of parameterised comple...

While stochastic optimal control, together with associate formulations like Reinforcement Learning, provides a formal approach to, amongst other, motor control, it remains computationally challenging for most practical problems. This thesis is concerned with the study of relations between stochastic optimal control and probabilistic inference. Such dualities – exemplified by the classical Kalma...

In this paper we present a heuristic algorithm based on the formulation space search method to solve the circle packing problem. The circle packing problem is the problem of finding the maximum radius of a specified number of identical circles that can be fitted, without overlaps, into a two-dimensional container of fixed size. In this paper we consider a variety of containers: the unit circle,...

During incremental odometry estimation in robotics and vision applications, the accumulation of estimation error produces a drift in the trajectory. This drift becomes observable when returning to previously visited areas, where it is possible to correct it by applying loop closing techniques. Ultimately a loop closing process leads to an optimisation problem where new constraints between poses...

In this paper, we apply particle swarm optimisation to the construction of optimal risky portfolios for financial investments. Constructing an optimal risky portfolio is a high-dimensional constrained optimisation problem where financial investors look for an optimal combination of their investments among different financial assets with the aim of achieving a maximum reward-to-variability ratio...