Software testing is a crucial part of software development. It enables quality assurance, such as correctness, completeness and high reliability of the software systems. Current state-of-the-art software testing techniques employ search-based optimisation methods, such as genetic algorithms to handle the difficult and laborious task of test data generation. Despite their general applicability, ...

Breeder Genetic Algorithms represent a class of random optimisation techniques gleaned from the science of population genetics, which have proved their ability to solve hard optimisation problems with continuous parameters. In this paper we test a parallel version of this technique against a sequential Breeder Genetic Algorithm on a typical inverse design problem in Aerodynamics, the problem of...

In this paper, the topic of constrained multiobjective in-core fuel management optimisation (MICFMO) using metaheuristics is considered. Several modern and stateof-the-art metaheuristics from different classes, including evolutionary algorithms, local search algorithms, swarm intelligence algorithms, a probabilistic model-based algorithm and a harmony search algorithm, are compared in order to ...

The global optimisation of non-convex quadratically constrained quadratic programs is a notoriously difficult problem, being not only NP-hard in the strong sense, but also very difficult in practice. We present a new heuristic approach to this problem, which enables one to obtain solutions of good quality in reasonable computing times. The heuristic consists of four phases: binarisation, convex...

In this contribution we present two interior-point path-following algorithms that solve the convex optimisation problem that arises in recentred barrier function model predictive control (MPC), which includes standard MPC as a limiting case. However the optimisation problem that arises in nonlinear MPC may not be convex. In this case we propose sequential convex programming (SCP) as an alternat...

This study proposes a decentralised reactive power optimisation of capacitors in distribution system with uncertain renewable energy sources (RES). The optimisation problem is modelled as minimising the active power loss and installed capacitors costs, subject to power flow constraints and other operation conditions. In view of the non-linear power flow equality constraints with uncertain power...

We consider the modelling of a particular layout optimisation problem with cellular automata, namely, the LEGO construction problem. We show that this problem can be modelled easily with cellular automata, provided that cells are considered as clusters which can merge or split during each time step of the evolution of the cellular automaton. The LEGO construction problem has previously been sol...

This contribution describes the methods used in a factory floor optimization process. The manufacturing process involves approximately 140 machines and is concentrated around a group of multipurpose CNC machines. We have analysed the current state of the manufacturing system by constructing a discrete event simulation (DES) model. The factory manufactures approximately 30,000 different products...

This paper addresses the discretisation problem for sparse linear systems. Classical methods usually destroy sparsity patterns of continuous-time systems. We develop an optimisation procedure that yields the best approximation to the discrete-time dynamical matrix with a prescribed sparsity pattern and subject to stability and other constraints. By formulating this problem in an adequate manner...