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  • Elsevier (CrossRef)  (24)
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  • 1
    Language: English
    In: Applied Soft Computing Journal, 2011, Vol.11(2), pp.2334-2347
    Description: The need for solving multi-modal optimization problems in high dimensions is pervasive in many practical applications. Particle swarm optimization (PSO) is attracting an ever-growing attention and more than ever it has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best ( ) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose two efficient solutions to remedy this problem using a stochastic approximation (SA) technique. In the first approach, is updated (moved) with respect to a global estimation of the gradient of the underlying (error) surface or function and hence can avoid getting trapped into a local optimum. The second approach is based on the formation of an alternative or artificial global best particle, the so-called , which can replace the native particle for a better guidance, the decision of which is held by a fair competition between the two. For this purpose we use simultaneous perturbation stochastic approximation (SPSA) for its low cost. Since SPSA is applied only to the (not to the entire swarm), both approaches result thus in a negligible overhead cost for the entire PSO process. Both approaches are shown to significantly improve the performance of PSO over a wide range of non-linear functions, especially if SPSA parameters are well selected to fit the problem at hand. A major finding of the paper is that even if the SPSA parameters are not tuned well, results of SA-driven (SAD) PSO are still better than the best of PSO and SPSA. Since the problem of poor update persists in the recently proposed extension of PSO, called multi-dimensional PSO (MD-PSO), both approaches are also integrated into MD-PSO and tested over a set of unsupervised data clustering applications. As in the basic PSO application, experimental results show that the proposed approaches significantly improved the quality of the MD-PSO clustering as measured by a validity index function. Furthermore, the proposed approaches are generic as they can be used with other PSO variants and applicable to a wide range of problems.
    Keywords: Particle Swarm Optimization ; Stochastic Approximation ; Multi-Dimensional Search ; Gradient Descent ; Computer Science
    ISSN: 1568-4946
    E-ISSN: 18729681
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  • 2
    Language: English
    In: Expert Systems With Applications, 2011, Vol.38(3), pp.2212-2223
    Description: ► Multi-dimensional particle swarm optimization. ► Fractional global-best formation. ► Optimization in dynamic environments. ► Global optimum tracking. Particle swarm optimization (PSO) was proposed as an optimization technique for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change in time. In this paper, we adapt recent techniques, which successfully address several major problems of PSO and exhibit a significant performance over multi-modal and non-stationary environments. In order to address the pre-mature convergence problem and improve the rate of PSO’s convergence to the global optimum, Fractional Global Best Formation (FGBF) technique is used. FGBF basically collects all the best dimensional components and fractionally creates an artificial Global Best particle ( ) that has the potential to be a better “guide” than the PSO’s native gbest particle. To establish follow-up of local optima, we then introduce a novel multi-swarm algorithm, which enables each swarm to converge to a different optimum and use FGBF technique distinctively. Finally for the multi-dimensional dynamic environments where the optimum dimension also changes in time, we utilize a recent PSO technique, the multi-dimensional (MD) PSO, which re-forms the native structure of the swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multi-dimensional search space where the optimum dimension is unknown, swarm particles can seek for both positional and dimensional optima. This eventually pushes the frontier of the optimization problems in dynamic environments towards a global search in a multi-dimensional space, where there exists a multi-modal problem possibly in each dimension. We investigated both standalone and mutual applications of the proposed methods over the moving peaks benchmark (MPB), which originally simulates a dynamic environment in a unique (fixed) dimension. MPB is appropriately extended to accomplish the simulation of a multi-dimensional dynamic system, which contains dynamic environments active in several dimensions. An extensive set of experiments show that in traditional MPB application domain, FGBF technique applied with multi-swarms exhibits an impressive speed gain and tracks the global peak with the minimum error so far achieved with respect to the other competitive PSO-based methods. When applied over the extended MPB, MD PSO with FGBF can find optimum dimension and provide the (near-) optimal solution in this dimension.
    Keywords: Particle Swarm Optimization ; Multi-Dimensional Search ; Fractional Global Best Formation ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
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  • 3
    Language: English
    In: Signal Processing: Image Communication, March 2014, Vol.29(3), pp.410-423
    Description: In this paper, we propose a novel and robust modus operandi for fast and accurate shot boundary detection where the whole design philosophy is based on human perceptual rules and the well-known “Information Seeking Mantra”. By adopting a top–down approach, redundant video processing is avoided and furthermore elegant shot boundary detection accuracy is obtained under significantly low computational costs. Objects within shots are detected via local image features and used for revealing visual discontinuities among shots. The proposed method can be used for detecting all types of gradual transitions as well as abrupt changes. Another important feature is that the proposed method is fully generic, which can be applied to any video content without requiring any training or tuning in advance. Furthermore, it allows a user interaction to direct the SBD process to the user's “Region of Interest” or to stop it once satisfactory results are obtained. Experimental results demonstrate that the proposed algorithm achieves superior computational times compared to the state-of-art methods without sacrificing performance.
    Keywords: Video Shot Boundary Detection ; Human Perception ; Local Image Features ; Video Content Analysis ; Engineering ; Applied Sciences ; Computer Science
    ISSN: 0923-5965
    E-ISSN: 1879-2677
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  • 4
    Language: English
    In: Neurocomputing, 08 February 2017, Vol.224, pp.142-154
    Description: There are well-known limitations and drawbacks on the performance and robustness of the feed-forward, fully-connected Artificial Neural Networks (ANNs), or the so-called Multi-Layer Perceptrons (MLPs). In this study we shall address them by Generalized Operational Perceptrons (GOPs) that consist of neurons with distinct (non-)linear operators to achieve a generalized model of the biological neurons and ultimately a superior diversity. We modified the conventional back-propagation (BP) to train GOPs and furthermore, proposed Progressive Operational Perceptrons (POPs) to achieve self-organized and depth-adaptive GOPs according to the learning problem. The most crucial property of the POPs is their ability to simultaneously search for the optimal operator set and train each layer individually. The final POP is, therefore, formed layer by layer and in this paper we shall show that this ability enables POPs with minimal network depth to attack the most challenging learning problems that cannot be learned by conventional ANNs even with a deeper and significantly complex configuration. Experimental results show that POPs can scale up very well with the problem size and can have the potential to achieve a superior generalization performance on real benchmark problems with a significant gain.
    Keywords: Artificial Neural Networks ; Multi-Layer Perceptrons ; Progressive Operational Perceptrons ; Diversity ; Scalability ; Computer Science
    ISSN: 0925-2312
    E-ISSN: 1872-8286
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  • 5
    Language: English
    In: ISPRS Journal of Photogrammetry and Remote Sensing, April 2014, Vol.90, pp.10-22
    Description: Fully and partially polarimetric SAR data in combination with textural features have been used extensively for terrain classification. However, there is another type of visual feature that has so far been neglected from polarimetric SAR classification: Color. It is a common practice to visualize polarimetric SAR data by color coding methods and thus it is possible to extract powerful color features from such pseudo color images so as to gather additional crucial information for an improved terrain classification. In this paper, we investigate the application of several individual visual features over different pseudo color generated images along with the traditional SAR and texture features for a novel supervised classification application of dual- and single-polarized SAR data. We then draw the focus on evaluating the effects of the applied pseudo coloring methods on the classification performance. An extensive set of experiments show that individual visual features or their combination with traditional SAR features introduce a new level of discrimination and provide noteworthy improvement of classification accuracies within the application of land use and land cover classification for dual- and single-pol image data.
    Keywords: Synthetic Aperture Radar ; Classification ; Image Analysis ; Visual Features ; Color ; Texture ; Engineering ; Geography
    ISSN: 0924-2716
    E-ISSN: 1872-8235
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  • 6
    Language: English
    In: Expert Systems With Applications, 2012, Vol.39(5), pp.4710-4717
    Description: In this paper, a robust radial basis function (RBF) network based classifier is proposed for polarimetric synthetic aperture radar (SAR) images. The proposed feature extraction process utilizes the covariance matrix elements, the H/α/A decomposition based features combined with the backscattering power (span), and the gray level co-occurrence matrix (GLCM) based texture features, which are projected onto a lower dimensional feature space using principal components analysis. For the classifier training, both conventional backpropagation (BP) and multidimensional particle swarm optimization (MD-PSO) based dynamic clustering are explored. By combining complete polarimetric covariance matrix and eigenvalue decomposition based pixel values with textural information (contrast, correlation, energy, and homogeneity) in the feature set, and employing automated evolutionary RBF classifier for the pattern recognition unit, the overall classification performance is shown to be significantly improved. An experimental study is performed using the fully polarimetric San Francisco Bay and Flevoland data sets acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band to evaluate the performance of the proposed classifier. Classification results (in terms of confusion matrix, overall accuracy and classification map) compared with the major state of the art algorithms demonstrate the effectiveness of the proposed RBF network classifier.
    Keywords: Polarimetric Synthetic Aperture Radar ; Radial Basis Function Network ; Particle Swarm Optimization ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
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  • 7
    Language: English
    In: Image and Vision Computing, 2010, Vol.28(8), pp.1309-1326
    Description: Color features are the key-elements widely used in content-analysis and retrieval. However, most of them show severe limitations and drawbacks due to their inefficiency of modeling the human visual system with respect to color perception. Moreover, they cannot characterize all the properties of the color composition in a visual scenery. In this paper we present a perceptual color feature, which describes all major properties of prominent colors both in spatial and color domains. In accordance with the well-known law, we adopt a global, top-down approach in order to model (see) the whole color composition before its parts and in this way we can avoid the problems of pixel-based approaches. In color domain the dominant colors are extracted along with their global properties and quad-tree decomposition partitions the image so as to characterize the spatial color distribution (SCD). We propose two efficient SCD descriptors; the proximity histograms, which distill the histogram of inter-color distances and the proximity grids, which cumulate the spatial co-occurrence of colors in a 2D grid. Both approaches are configurable and provide means of modeling SCD in a scalar and directional way. Combination of the extracted global and spatial properties forms the final descriptor, which is unbiased and robust to non-perceivable color elements in both spatial and color domains. Finally a penalty-trio model fuses all color properties in a similarity distance computation during retrieval. Experimental results approve the superiority of the proposed technique against powerful global and spatial color descriptors.
    Keywords: Perceptual Color Descriptor ; Human Visual System ; Content-Based Image Indexing and Retrieval ; Spatial Color Distribution ; Engineering ; Applied Sciences
    ISSN: 0262-8856
    E-ISSN: 1872-8138
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  • 8
    Language: English
    In: Expert Systems With Applications, 15 November 2015, Vol.42(20), pp.7175-7185
    Description: Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic EEG records. We aim to achieve this with the minimum supervision from the neurologist. To accomplish this objective, first a novel feature extraction method is proposed based on the mapping of EEG signals into two dimensional space, resulting into a texture image. The texture image is constructed by mapping and scaling EEG signals and their associated frequency sub-bands into the gray-level image domain. Image texture analysis using gray level co-occurrence matrix (GLCM) is then applied in order to extract multivariate features which are able to differentiate between seizure and seizure-free events. To evaluate the discriminative power of the proposed feature extraction method, a comparative study is performed, against other dedicated feature extraction methods. The comparative performance evaluations show that the proposed feature extraction method can outperform other state-of-art feature extraction methods with a low computational cost. With a training rate of 25%, the overall sensitivity of 70.19% and specificity of 97.74% are achieved in the classification of over 163 h of EEG records using support vector machine (SVM) classifiers with linear kernels and trained by the stochastic gradient descent (SGD) algorithm.
    Keywords: Electroencephalography ; Epileptic Seizure Classification ; Haralick ; Textural Features ; Stochastic Gradient Descent ; Chb-MIT Dataset ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
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  • 9
    Language: English
    In: Swarm and Evolutionary Computation, June 2017, Vol.34, pp.103-118
    Description: In this paper, we propose a new way to carry out fitness evaluation in dynamic Particle Swarm Clustering (PSC) with centroid-based encoding. Generally, the PSC fitness function is selected among the clustering validity indices and most of them directly depend on the cluster centroids. In the traditional fitness evaluation approach, the cluster centroids are replaced by the centroids proposed by a particle position. We propose to first compute the centroids of the corresponding clusters and then use these computational centroids in fitness evaluation. The proposed way is called Fitness Evaluation with Computational Centroids (FECC). We conducted an extensive set of comparative evaluations and the results show that FECC leads to a clear improvement in clustering results compared to the traditional fitness evaluation approach with most of the fitness functions considered in this study. The proposed approach was found especially beneficial when underclustering is a problem. Furthermore, we evaluated 31 fitness functions based on 17 clustering validity indices using two PSC methods over a large number of synthetic and real data sets with varying properties. We used three different performance criteria to evaluate the clustering quality and found out that the top three fitness functions are Xu index, WB index, and Dunn variant applied using FECC. These fitness functions consistently performed well for both PSC methods, for all data distributions, and according to all performance criteria. In all test cases, they were clearly among the better half of the fitness functions and, in the majority of the cases, they were among the top 4 functions. Further guidance for improved fitness function selection in different situations is provided in the paper.
    Keywords: Particle Swarm Optimization ; Pattern Clustering ; Validity Index ; Swarm Intelligence ; Computer Science
    ISSN: 2210-6502
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  • 10
    Language: English
    In: Mechanical Systems and Signal Processing, 01 February 2017, Vol.84, pp.625-641
    Description: Vibration suppression remains a crucial issue in the design of structures and machines. Recent studies have shown that with the use of metamaterial inspired structures (or metastructures), considerable vibration attenuation can be achieved. Optimization of the internal geometry of metastructures maximizes the suppression performance. Zigzag inserts have been reported to be efficient for vibration attenuation. It has also been reported that the geometric parameters of the inserts affect the vibration suppression performance in a complex manner. In an attempt to find out the most efficient parameters, an optimization study has been conducted on the linear zigzag inserts and is presented here. The research reported in this paper aims at developing an automated method for determining the geometry of zigzag inserts through optimization. This genetic algorithm based optimization process searches for optimal zigzag designs which are properly tuned to suppress vibrations when inserted in a specific host structure (cantilever beam). The inserts adopted in this study consist of a cantilever zigzag structure with a mass attached to its unsupported tip. Numerical simulations are carried out to demonstrate the efficiency of the proposed zigzag optimization approach.
    Keywords: Metastructures ; Zigzag Insert Metastructures ; Vibration Attenuation ; Genetic Algorithms ; Engineering
    ISSN: 0888-3270
    E-ISSN: 1096-1216
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