An Investigation into the Merger of Stochastic Diffusion Search and Particle Swarm Optimisation

This study reports early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) to the Particle Swarm Optimiser (PSO) metaheuristic, effectively merging the two swarm intelligence algorithms. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between particles, has the potential to improve the optimisation capability of conventional PSOs

A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts

Copyright © 2005 Springer Verlag. The final publication is available at link.springer.com3rd International Conference, EMO 2005, Guanajuato, Mexico, March 9-11, 2005. ProceedingsBook title: Evolutionary Multi-Criterion OptimizationIn extending the Particle Swarm Optimisation methodology to multi-objective problems it is unclear how global guides for particles should be selected. Previous work has relied on metric information in objective space, although this is at variance with the notion of dominance which is used to assess the quality of solutions. Here we propose methods based exclusively on dominance for selecting guides from a non-dominated archive. The methods are evaluated on standard test problems and we find that probabilistic selection favouring archival particles that dominate few particles provides good convergence towards and coverage of the Pareto front. We demonstrate that the scheme is robust to changes in objective scaling. We propose and evaluate methods for confining particles to the feasible region, and find that allowing particles to explore regions close to the constraint boundaries is important to ensure convergence to the Pareto front

A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation

In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class islabeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. Several methods have been proposed in order to solve threshold selectionproblems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the 1D histogram of a gray level image and whose parameters are calculated using three nature inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each Gaussian function approximates thehistogram, representing a pixel class and therefore a threshold point. Experimental results are shown, comparing in quantitative and qualitative fashion as well as the main advantages and drawbacks of each algorithm, applied to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to the Journa

The fabrication of micro- and nano- scale deterministic and stochastic pillar arrays for planar separations

Planar chromatography, unlike high performance liquid chromatography (HPLC), has not experienced a significant evolution in stationary phase media since the development of the technique. This has lead HPLC to become a much more popular and robust analytical method. Main factors that contribute to improved performance of chromatographic systems include a reduction in particle size, homogeneity of the stationary phase, and an increase in velocity of the mobile phase. In general, a reduction in particle size should lead to an improvement in the performance of all chromatography systems. However, the main obstacle of improving the performance of planar chromatography systems is that a reduction in particle size leads to a reduction in the capillary flow that governs solvent velocity. This decrease in solvent velocity leads to band broadening resulting in poor efficiency and resolution which are critical performance parameters for chromatographic systems. The research presented herein investigates the scaling down of dimensions to the micro- and nano-scale for pillar arrays in order to investigate the effect on plate height and chromatographic efficiency of these capillary action driven micro- and nano-fluidic systems. Sample application is a critical parameter that effects band broadening in UTLC systems. By taking advantage of the superhydrophobic nature of these arrays the development of a spotting method that demonstrates the ability to create reproducible sample spots that are less than 200 microns (micro- scale arrays) and 400nm (nano- scale arrays) within these arrays are highlighted in this dissertation. We have demonstrated the fabrication of deterministic micro-scale arrays that exhibit plate heights as low as 2µm as well as deterministic and stochastic nanothin-layer chromatographic platforms. Most significantly these systems resulted in bands that were highly efficient, with plate heights in the nm range. This resulted in significant separations of analytical laser test dyes, environmentally significant NBD-derivatized amines, and, biologically relevant chemotherapy drugs (Adriamycin and Daunorubicin)