Erkan Besdok

A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms

In this paper, the algorithmic concepts of the Cuckoo-search (CK), Particle swarm optimization (PSO), Differential evolution (DE) and Artificial bee colony (ABC) algorithms have been analyzed. The numerical optimization problem solving successes of the mentioned algorithms have also been compared statistically by testing over 50 different benchmark functions. Empirical results reveal that the problem solving success of the CK algorithm is very close to the DE algorithm. The run-time complexity and the required function-evaluation number for acquiring global minimizer by the DE algorithm is generally smaller than the comparison algorithms. The performances of the CK and PSO algorithms are statistically closer to the performance of the DE algorithm than the ABC algorithm. The CK and DE algorithms supply more robust and precise results than the PSO and ABC algorithms.

A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification.

This paper introduces a comparison of training algorithms of radial basis function (RBF) neural networks for classification purposes. RBF networks provide effective solutions in many science and engineering fields. They are especially popular in the pattern classification and signal processing areas. Several algorithms have been proposed for training RBF networks. The Artificial Bee Colony (ABC) algorithm is a new, very simple and robust population based optimization algorithm that is inspired by the intelligent behavior of honey bee swarms. The training performance of the ABC algorithm is compared with the Genetic algorithm, Kalman filtering algorithm and gradient descent algorithm. In the experiments, not only well known classification problems from the UCI repository such as the Iris, Wine and Glass datasets have been used, but also an experimental setup is designed and inertial sensor based terrain classification for autonomous ground vehicles was also achieved. Experimental results show that the use of the ABC algorithm results in better learning than those of others.

Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding

This paper introduces the comparison of evolutionary and swarm-based optimization algorithms for multilevel color image thresholding problem which is a process used for segmentation of an image into different regions. Thresholding has various applications such as video image compression, geovideo and document processing, particle counting, and object recognition. Evolutionary and swarm-based computation techniques are widely used to reduce the computational complexity of the multilevel thresholding problem. In this study, well-known evolutionary algorithms such as Evolution Strategy, Genetic Algorithm, Differential Evolution, Adaptive Differential Evolution and swarm-based algorithms such as Particle Swarm Optimization, Artificial Bee Colony, Cuckoo Search and Differential Search Algorithm have been used for solving multilevel thresholding problem. Kapurs entropy is used as the fitness function to be maximized. Experiments are conducted on 20 different test images to compare the algorithms in terms of quality, running CPU times and compression ratios. According to the statistical analysis of objective values, swarm based algorithms are more accurate and robust than evolutionary algorithms in general. However, experimental results exposed that evolutionary algorithms are faster than swarm based algorithms in terms of CPU running times.

Detail-preserving restoration of impulse noise corrupted images by a switching median filter guided by a simple neuro-fuzzy network

A new operator for the restoration of digital images corrupted by impulse noise is presented. The proposed operator is a simple recursive switching median filter guided by a neuro-fuzzy network functioning as an impulse detector. The internal parameters of the neuro-fuzzy impulse detector are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The most distinctive feature of the proposed operator over other operators is that it offers excellent detail- and texture-preservation performance, while effectively removing noise from the input image. Extensive experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.

Using an adaptive neuro-fuzzy inference system-based interpolant for impulsive noise suppression from highly distorted images

A new impulsive noise (IN) suppression filter, entitled Adaptive neuro-fuzzy inference system (ANFIS)-based impulsive noise suppression Filter, which shows a high performance at the restoration of images distorted by IN, is proposed in this paper. The extensive simulation results show that the proposed filter achieves a superior performance to the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise density is very high.

Impulsive Noise Suppression from Highly Corrupted Images by Using Resilient Neural Networks

A new impulsive noise elimination filter, entitled Resilient Neural Network based impulsive noise removing filter (RF), which shows a high performance at the restoration of images corrupted by impulsive noise, is proposed in this paper. The RF uses Chi-square goodness-of-fit test in order to find corrupted pixels more accurately. The corrupted pixels are replaced by new values which were estimated by using the proposed RF. Extensive simulation results show that the proposed filter achieves a superior performance to the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise density is very high.

Impulsive noise suppression from images with the noise exclusive filter

A novel impulsive noise elimination filter, entitled noise exclusive filter (NEF), which shows a high performance at the restoration of images distorted by impulsive noise, is proposed in this paper. NEF uses chi-square goodness-of-fit test in order to detect the corrupted pixels more accurately. Simulation results show that the proposed filter achieves a superior performance compared with the other filters mentioned in this paper in terms of noise suppression and detail preservation, particularly when the noise density is very high. The proposed method also achieves the robustness and detail preservation perfectly for a wide range of impulsive noise density. NEF provides efficient filtering performance with reduced computational complexity.

Comparative Analysis of the Cuckoo Search Algorithm

Cuckoo Search Algorithm (CS) is a population based, elitist evolutionary search algorithm proposed for the solution of numerical optimization problems. Despite its wide use, the algorithmic process of CS has been scarcely studied in detail. In this chapter, the algorithmic structure of CS and its effective problem solving success have been studied. Fifty benchmark problems were used in the numerical tests performed in order to study the algorithmic behavior of CS. The success of CS in solving benchmark problems was compared with three widely used optimization algorithms (i.e., PSO, DE, and ABC) by means of Kruskal–Wallis statistical test. The search strategy of CS, which utilizes the Levy distribution, enables it to analyze the search space in a very successful manner. The statistical results have verified that CS has the superior problem-solving ability as a search strategy.

A new method for impulsive noise suppression from highly distorted images by using Anfis

Abstract A new impulsive noise elimination filter, entitled Anfis-based impulsive noise removing filter (AIF), which shows a high performance at the restoration of images distorted by impulsive noise, is proposed in this paper. AIF uses Anderson-Darling test values in order to determine the pixels which exhibit impulsive behavior within the image. Extensive simulation results show that the proposed filter achieves a superior performance to the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise density is very high.

Impulsive noise suppression from images by using Anfis interpolant and lillietest

A new impulsive noise (IN) elimination filter, entitled adaptive neuro-fuzzy inference system-based IN removal filter (Anfis-F), which shows high performance at the restoration of images distorted by IN, is proposed in this paper. The Anfis-F comprises three main steps: finding the pixels that are suspected to be corrupted, the Delaunay triangulation, and finally, making estimation for intensity values of corrupted pixels within each of the Delaunay triangles. Extensive simulation results show that the proposed filter achieves better performance than other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise density is very high.