12986050
Improving the Robustness of ‘Online Agglomerative Clustering Method’ Based on Kernel-Induce Distance Measures
Recently, an online agglomerative clustering method called AddC (I. D. Guedalia et al. Neural Comput. {\bf 11} (1999), 521--540) was proposed for nonstationary data clustering. Although AddC possesses many good attributes, a vital problem of that method is its sensitivity to noises, which limits its use in real-word applications. In this paper, based on \hbox{kernel-induced} distance measures, a robust online clustering (ROC) algorithm is proposed to remedy the problem of AddC. Experimental results on artificial and benchmark data sets show that ROC has better clustering performances than AddC, while still inheriting advantages such as clustering data in a single pass and without knowing the exact number of clusters beforehand.
120730704
Robust image denoising using kernel-induced measures
We propose a class of novel nonlinear robust filters for image denoising by incorporating the kernel-induced measures into the classical linear mean filter. Particularly, we place more focus on the Gaussian kernel based filter (GK) due to its simplicity. The GK filter not only generalizes and makes the original linear mean filter highly resistant to outliers but also outperforms a typical and powerful mean-logCauchy filter recently developed by Hamza et al in the mixed noise removal in certain specific conditions in the normalized mean square error (NMSE) sense. The experimental results also illustrate that the kernel-based nonlinear filters are promising.
16227528
Fast image retrieval using color-spatial information
Abstract. In this paper, we present an image retrieval system that employs both the color and spatial information of images to facilitate the retrieval process. The basic unit used in our technique is a single-colored cluster, which bounds a homogeneous region of that color in an image. Two clusters from two images are similar if they are of the same color and overlap in the image space. The number of clusters that can be extracted from an image can be very large, and it affects the accuracy of retrieval. We study the effect of the number of clusters on retrieval effectiveness to determine an appropriate value for “optimal'' performance. To facilitate efficient retrieval, we also propose a multi-tier indexing mechanism called the Sequenced Multi-Attribute Tree (SMAT). We implemented a two-tier SMAT, where the first layer is used to prune away clusters that are of different colors, while the second layer discriminates clusters of different spatial locality. We conducted an experimental study on an image database consisting of 12,000 images. Our results show the effectiveness of the proposed color-spatial approach, and the efficiency of the proposed indexing mechanism.
26533495
A Tabu-Based Exploratory Evolutionary Algorithm for Multiobjective Optimization
This paper presents an exploratorymultiobjective evolutionary algorithm (EMOEA)that integrates the features of tabu search andevolutionary algorithm for multiobjective (MO)optimization. The method incorporates the taburestriction in individual examination andpreservation in order to maintain the searchdiversity in evolutionary MO optimization,which subsequently helps to prevent the searchfrom trapping in local optima as well as topromote the evolution towards the globaltrade-offs concurrently. In addition, a newlateral interference is presented in the paperto distribute nondominated individuals alongthe discovered Pareto-front uniformly. Unlikemany niching or sharing methods, the lateralinterference can be performed without the needof parameter settings and can be flexiblyapplied in either the parameter or objectivedomain. The features of the proposed algorithmare examined based upon three benchmarkproblems. Experimental results show that EMOEAperforms well in searching and distributingnondominated solutions along the trade-offsuniformly, and offers a competitive behavior toescape from local optima in a noisyenvironment.
207726294
Evolving better population distribution and exploration in evolutionary multi-objective optimization
The aim of multi-objective evolutionary optimization is to minimize the distance between the solution set and the true Pareto front, to distribute the solutions evenly and to maximize the spread of solution set. This paper addresses these issues by presenting two features that enhance the optimization ability of multi-objective evolutionary algorithms. The first feature is a variant of the mutation operator that adapts the mutation rate along the evolution process to maintain a balance between the introduction of diversity and local fine-tuning. In addition, this adaptive mutation operator adopts a new approach to strike a compromise between the preservation and disruption of genetic information. The second feature is an enhanced exploration strategy that encourages the exploration towards less populated areas and hence achieves better discovery of gaps in the generated front. The strategy also preserves non-dominated solutions in the evolving population to achieve a good convergence for the optimization. Comparative studies of some well-known diversity operators, mutation operators and multi-objective evolutionary algorithms are performed on different benchmark problems, which illustrate the effectiveness and efficiency of the proposed features.
27127935
Automatic Design of Multivariable QFT Control System via Evolutionary Computation
This paper proposes a multi-objective evolutionary automated design methodology for multivariable QFT control systems. Unlike existing manual or convex optimisation based QFT design approaches, the 'intelligent' evolutionary technique is capable of automatically evolving both the nominal controller and pre-filter simultaneously to meet all performance requirements in QFT, without going through the conservative and sequential design stages for each of the multivariable sub-systems. In addition, it avoids the need of manual QFT bound computation and trial-and-error loop-shaping design procedures, which is particularly useful for unstable or non-minimum phase plants for which stabilising controllers maybe difficult to be synthesised. Effectiveness of the proposed QFT design methodology is validated upon a benchmark multivariable system, which offers a set of low-order Pareto optimal controllers that satisfy all the required closed-loop performances under practical constraints.
39015345
FPGA-BASED AUTONOMOUS ROBOT NAVIGATION VIA INTRINSIC EVOLUTION
Artificial evolution has been applied in mobile robotics to achieve adaptive robotic behaviors in unstructured environments. Meanwhile, much attention has been paid to the evolvable hardware, which is a novel set of integrated circuits capable of reconfiguring its architectures using evolutionary computing techniques. This paper presents the design and implementation of an evolvable hardware-based autonomous robot navigation system using intrinsic evolution. Distinguished from traditional evolutionary approaches based on software simulation, an evolvable robot controller at the hardware gate-level that is capable of adapting to dynamic changes in the environments is implemented. In our approach, the concept of Boolean function is used to construct the evolvable controller implemented on an FPGA-based robot turret, and evolutionary computing is applied as a learning tool to guide the artificial evolution at the hardware level. The effectiveness of the proposed evolvable robotic system is confirmed with physical implementation of robot navigation behaviors on light source following and obstacle avoidance using a robot with traction fault.