Distribution-based adaptive bounding genetic algorithm for continuous optimisation problems

A new search-space-updating technique for genetic algorithms is proposed for continuous optimisation problems. Other than gradually reducing the search space during the evolution process with a fixed reduction rate set ‘a priori’, the upper and the lower boundaries for each variable in the objective function are dynamically adjusted based on its distribution statistics. To test the effectiveness, the technique is applied to a number of benchmark optimisation problems in comparison with three other techniques, namely the genetic algorithms with parameter space size adjustment (GAPSSA) technique [A.B. Djurišic, Elite genetic algorithms with adaptive mutations for solving continuous optimization problems – application to modeling of the optical constants of solids, Optics Communications 151 (1998) 147–159], successive zooming genetic algorithm (SZGA) [Y. Kwon, S. Kwon, S. Jin, J. Kim, Convergence enhanced genetic algorithm with successive zooming method for solving continuous optimization problems, Computers and Structures 81 (2003) 1715–1725] and a simple GA. The tests show that for well-posed problems, existing search space updating techniques perform well in terms of convergence speed and solution precision however, for some ill-posed problems these techniques are statistically inferior to a simple GA. All the tests show that the proposed new search space update technique is statistically superior to its counterparts.

Optimisation of radiotherapy for carcinoma of the parotid gland: a comparison of conventional, three-dimensional conformal, and intensity-modulated techniques

Background and purpose: To compare external beam radiotherapy techniques for parotid gland tumours using conventional radiotherapy (RT), three-dimensional conformal radiotherapy (3DCRT), and intensity-modulated radiotherapy (IMRT). To optimise the IMRT techniques, and to produce an IMRT class solution.Materials and methods: The planning target volume (PTV), contra-lateral parotid gland, oral cavity, brain-stem, brain and cochlea were outlined on CT planning scans of six patients with parotid gland tumours. Optimised conventional RT and 3DCRT plans were created and compared with inverse-planned IMRT dose distributions using dose-volume histograms. The aim was to reduce the radiation dose to organs at risk and improve the PTV dose distribution. A beam-direction optimisation algorithm was used to improve the dose distribution of the IMRT plans, and a class solution for parotid gland IMRT was investigated.Results: 3DCRT plans produced an equivalent PTV irradiation and reduced the dose to the cochlea, oral cavity, brain, and other normal tissues compared with conventional RT. IMRT further reduced the radiation dose to the cochlea and oral cavity compared with 3DCRT. For nine- and seven-field IMRT techniques, there was an increase in low-dose radiation to non-target tissue and the contra-lateral parotid gland. IMRT plans produced using three to five optimised intensity-modulated beam directions maintained the advantages of the more complex IMRT plans, and reduced the contra-lateral parotid gland dose to acceptable levels. Three- and four-field non-coplanar beam arrangements increased the volume of brain irradiated, and increased PTV dose inhomogeneity. A four-field class solution consisting of paired ipsilateral coplanar anterior and posterior oblique beams (15, 45, 145 and 170o from the anterior plane) was developed which maintained the benefits without the complexity of individual patient optimisation.Conclusions: For patients with parotid gland tumours, reduction in the radiation dose to critical normal tissues was demonstrated with 3DCRT compared with conventional RT. IMRT produced a further reduction in the dose to the cochlea and oral cavity. With nine and seven fields, the dose to the contra-lateral parotid gland was increased, but this was avoided by optimisation of the beam directions. The benefits of IMRT were maintained with three or four fields when the beam angles were optimised, but were also achieved using a four-field class solution. Clinical trials are required to confirm the clinical benefits of these improved dose distributions.

Neural modelling, control and optimisation of an industrial grinding process

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed. (c) 2004 Elsevier Ltd. All rights reserved.

Optimisation of trench isolated bipolar transistors on SOI substrates by 3D electro-thermal simulations

This paper provides a comprehensive analysis of thermal resistance of trench isolated bipolar transistors on SOI substrates based on 3D electro-thermal simulations calibrated to experimental data. The impact of emitter length, width, spacing and number of emitter fingers on thermal resistance is analysed in detail. The results are used to design and optimise transistors with minimum thermal resistance and minimum transistor area. (c) 2007 Elsevier Ltd. All rights reserved.

Non-stationary variogram models for geostatistical sampling optimisation: An empirical investigation using elevation data

A problem with use of the geostatistical Kriging error for optimal sampling design is that the design does not adapt locally to the character of spatial variation. This is because a stationary variogram or covariance function is a parameter of the geostatistical model. The objective of this paper was to investigate the utility of non-stationary geostatistics for optimal sampling design. First, a contour data set of Wiltshire was split into 25 equal sub-regions and a local variogram was predicted for each. These variograms were fitted with models and the coefficients used in Kriging to select optimal sample spacings for each sub-region. Large differences existed between the designs for the whole region (based on the global variogram) and for the sub-regions (based on the local variograms). Second, a segmentation approach was used to divide a digital terrain model into separate segments. Segment-based variograms were predicted and fitted with models. Optimal sample spacings were then determined for the whole region and for the sub-regions. It was demonstrated that the global design was inadequate, grossly over-sampling some segments while under-sampling others.