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In the oceanic context, the aim of Target Motion Analysis (TMA) is to estimate the state, i.e. location, bearing and velocity, of a sound-emitting object. These estimates are based on a series of passive measures of both the angle and the distance between an observer and the source of sound, which is called the target. These measurements are corrupted by noise and false readings, which are perceived as outliers. Usually, sequences of measurements are taken and statistical methods, like the Least Squares method or the Annealing M-Estimator, are applied to estimate the target's state by minimising the residual in range and bearing for a series of measurements. In this research, an ACO-Estimator, a novel hybrid optimisation algorithm based on Ant Colony Optimisation, has been developed and applied to the TMA problem and its effectiveness was compared with standard estimators. It was shown that the new algorithm outperforms conventional estimators by successfully removing outliers from the measurements. [ABSTRACT FROM AUTHOR]

Colour quantisation is a common image processing application where full colour images are to be displayed using a limited palette. The choice of a good palette is therefore crucial as it directly determines the quality of the resulting image. While standard quantisation algorithms typically employ domain specific knowledge, we apply a novel variant of simulated annealing as a standard black-box optimisation algorithm to the colour quantisation problem. The main advantage of black-box optimisation algorithms is that they do not require any domain specific knowledge yet are able to provide a near optimal solution. The effectiveness of our approach is evaluated by comparing its performance with several specialised colour quantisation algorithms. The results obtained show that even without any domain specific knowledge our SA based algorithm is able to outperform standard quantisation algorithms. To further improve the performance of the algorithm we combine the SA technique with a standard k-means clustering technique. This hybrid quantisation algorithm is shown to outperform all other algorithms and hence to provide images with superior image quality. [ABSTRACT FROM AUTHOR]

The quality of steel strip produced in a wide strip rolling mill depends heavily on the careful selection of initial ground work roll profiles for each of the mill stands in the finishing train. In the past, these profiles were determined by human experts, based on their knowledge and experience. In previous work, the profiles were successfully optimised using a self-organising migration algorithm (SOMA). In this research, SASS, a novel heuristic optimisation algorithm that has only one control parameter, has been used to find the optimum profiles for a simulated rolling mill. The resulting strip quality produced using the profiles found by SASS is compared with results from previous work and the quality produced using the original profile specifications. The best set of profiles found by SASS clearly outperformed the original set and performed equally well as SOMA without the need of finding a suitable set of control parameters. [ABSTRACT FROM AUTHOR]

In order to optimise the energy production in a fleet of power plants, it is necessary to solve a mixed integer optimisation problem. Traditionally, the continuous parts of the problem are linearized and a Simplex scheme is applied. Alternatively, heuristic ˵bionic″ optimisation methods can be used without having to linearize the problem. Weare going to demonstrate this approach by modelling power plant blocks with fast Neural Networks and optimising the operation of multi-block power plants over one day with Simulated Annealing. [ABSTRACT FROM AUTHOR]

We apply a variant of Simulated Annealing (SA) as a standard black-box optimisation algorithm to the colour quantisation problem. The main advantage of black-box optimisation algorithms is that they do not require any domain specific knowledge yet are able to provide a near optimal solution. To further improve the performance of the algorithm we combine the SA technique with a standard k-means clustering technique. We evaluate the effectiveness of our approach by comparing its performance with several specialised colour quantisation algorithms. The results obtained show that our hybrid SA algorithm clearly outperforms standard quantisation algorithms and provides images with superior image quality. [ABSTRACT FROM AUTHOR]

We apply a variant of Simulated Annealing (SA) as a standard black-box optimisation algorithm to the colour quantisation problem. The main advantage of black-box optimisation algorithms is that they do not require any domain specific knowledge yet are able to provide a near optimal solution. We evaluate the effectiveness of our approach by comparing its performance with several specialised colour quantisation algorithms. The results obtained show that even without any domain specific knowledge our SA based algorithm is able to outperform standard quantisation algorithms and hence to provide images with superior image quality. [ABSTRACT FROM AUTHOR]

The finishing train of a hot strip mill has been modelled by using a constant volume element model. The accuracy of the model has been increased by using an Artificial Neural Network (ANN). A non-linear Rank Based Genetic Algorithm has been developed for the optimization of the work roll profiles in the finishing stands of the simulated hot strip mill. It has been compared with eight other experimental optimization algorithms: Random Walk, Hill Climbing, Simulated Annealing (SA) and five different Genetic Algorithms (GA). Finally, the work roll profiles have been optimized by the non-linear Rank Based Genetic Algorithm. The quality of the strip from the simulated mill was significantly improved. [ABSTRACT FROM AUTHOR]