Peephole optimisation is a simple and effective optimisation technique used in conventional compilers. In classical peephole optimisers, optimisation rules are commonly applied through string pattern matching by means of regular expressions. This string-based approach to matching has proven to be very effective, but it is just the syntax of the assembly code input that is processed and its mean...

This paper presents an investigation of two search techniques, tabu search (TS) and simulated annealing (SA), to assess their relative merits when applied to engineering design optimisation. Design optimisation problems are generally characterised as having multi-modal search spaces and discontinuities making global optimisation techniques beneficial. Both techniques claim to be capable of loca...

The SOCRATES (Self-Optimisation and self-ConfiguRATion in wirelEss networkS) project aims at the development of self-organisation methods for LTE radio networks. Self-organisation comprises self-optimisation, self-configuration and self-healing. Within this document a framework, or guidelines, are laid out that are to be the basis for the development of self-organisation functionalities within ...

BOLD imaging is a useful and promising examination tool for mapping human brain functions, using the local alteration of blood oxygenation in the brain caused by neural activity. However, due to the weak signal response, optimisation of the entire methodology is important in BOLD imaging. To enable optimisation of various examination parameters, a fixed BOLD signal is often desirable. The phant...

A new algorithm for manipulating the radiation pattern of Electronically Steerable Array Radiator Antennas is proposed. A continuous implementation of the Ant Colony Optimisation (ACO) technique calculates the optimal impedance values of reactances loading different parasitic radiators placed in a circle around a centre antenna. By proposing a method to obtain a suitable sampling frequency of t...

Modelica is well suited for modelling complex physical systems due to the acausal description it is using. The causalisation of the model is carried out prior to each simulation. A significant part of the causalisation process is the symbolic manipulation and optimisation of the model. Despite the growing interest in Modelica, the capabilities of symbolic manipulation and optimisation are not f...

This paper presents a Bayesian approach for optimizing the position of sensors aimed at impact identification in composite structures under operational conditions. The uncertainty in the sensor data has been represented by statistical distributions of the recorded signals. An optimisation strategy based on the genetic algorithm is proposed to find the best sensor combination aimed at locating i...

The public health system is plagued by inefficient use of resources. Frequently, the results are lengthy patient treatment waiting times. While many solutions for patient scheduling in health systems exist, few address the problem of coordination between independent autonomous departments. In this study, we describe the use of a distributed dynamic constraint optimisation algorithm (Support Bas...

The combination of local optimisation heuristics and genetic algorithms has been shown to be an effective approach for finding near-optimum solutions to the Travelling Salesman Problem (TSP). In problem domains where the problem can be represented geometrically, such as networks and chemical structures, the combination of local optimisation operators and phenotype genetic operators has also bee...

The advancement in power systems has led to the development of generation dispatch (GD) that is difficult to solve by classical optimisation method. The proposed paper work is to evolve simple and effective method for optimum generation dispatch to minimise the fuel cost, environmental cost and security requirement of power networks. The approach is based on the bi-criterion global optimisation...