Many real-world manufacturing problems are too complex to be modelled analytically. In these scenarios, simulation-based optimisation is a powerful tool to determine optimal system settings. While traditional optimisation methods have been unable to cope with the complexities of many real-world problems approached by simulation, evolutionary methods have proven to be highly useful. This paper p...

A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of singleinput multiple-output (SIMO) systems. The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop. An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify th...

Next-generation model-based advanced process control technologies should be centred on an architecture that allows the choice of models, solutions methods, control settings and optimisation strategies seamlessly. In this work a model-centric platform for advanced process control-and-optimisation of industrial manufacturing systems is presented, which aims at providing a framework for realising ...

In this study we present a technique for the parallel optimisation of join queries, that uses the offered coarse-grain parallelism of the underlying architecture in order to reduce the CPU-bound optimisation overhead. The optimisation technique performs an almost exhaustive search of the solution space for small join queries and gradually, as the number of joins increases, it diverges towards i...

1. Abstract This paper presents a boundary based structural optimisation in the finite element fixed grid environment. The boundary is represented by smooth B-spline curves, as they typically require a relatively small number of control points to describe a curve. Utilising B-spline control points reduces the number of design variables whilst achieving a smooth boundary representation. An appro...

In order to properly operate an autonomous vehicle navigation system, it is important that the vehicle and sensor models of an autonomous vehicle are defined by an accurate parameter set. This paper presents a technique for identifying parameters of an autonomous vehicle using multi-objective optimisation, which enables the identification process without introducing additional parameters. A mul...

Current optimisation methods are of limited use for decision-support in complex systems due to two main short-comings. Firstly, they require long computational times to identify optimal solutions for large-scale problems and the algorithms are not easily parallelised for use in large-scale distributed computing environments. Such environments become increasingly available with the advent of Gri...

Energy consumption is rapidly becoming a limiting factor in scientific computing. As a result, hardware manufacturers increasingly prioritise energy efficiency in their processor designs. Performance engineers are also beginning to explore software optimisation and hardware/software co-design as a means to reduce energy consumption. Energy efficiency metrics developed by the hardware community ...

Many-objective optimisation poses great challenges to evolutionary algorithms. To start with, the ineffectiveness of the Pareto dominance relation, which is the most important criterion in multi-objective optimisation, results in the underperformance of traditional Pareto-based algorithms. Also, the aggravation of the conflict between proximity and diversity, along with increasing time or space...

1. Abstract In order to account for current and future requirements regarding the further improvement of the dynamical behaviour of a chain CVT (Continuous Variable Transmission) optimisation methods are substantial. The primary focus lies on the chain, which is the central part of the gear. In this work a concept to deal with this challenging task is introduced, leading to an optimisation tool...