Pinar Civicioglu

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.

Backtracking Search Optimization Algorithm for numerical optimization problems

This paper introduces the Backtracking Search Optimization Algorithm (BSA), a new evolutionary algorithm (EA) for solving real-valued numerical optimization problems. EAs are popular stochastic search algorithms that are widely used to solve non-linear, non-differentiable and complex numerical optimization problems. Current research aims at mitigating the effects of problems that are frequently encountered in EAs, such as excessive sensitivity to control parameters, premature convergence and slow computation. In this vein, development of BSA was motivated by studies that attempt to develop simpler and more effective search algorithms. Unlike many search algorithms, BSA has a single control parameter. Moreover, BSAs problem-solving performance is not over sensitive to the initial value of this parameter. BSA has a simple structure that is effective, fast and capable of solving multimodal problems and that enables it to easily adapt to different numerical optimization problems. BSAs strategy for generating a trial population includes two new crossover and mutation operators. BSAs strategies for generating trial populations and controlling the amplitude of the search-direction matrix and search-space boundaries give it very powerful exploration and exploitation capabilities. In particular, BSA possesses a memory in which it stores a population from a randomly chosen previous generation for use in generating the search-direction matrix. Thus, BSAs memory allows it to take advantage of experiences gained from previous generations when it generates a trial preparation. This paper uses the Wilcoxon Signed-Rank Test to statistically compare BSAs effectiveness in solving numerical optimization problems with the performances of six widely used EA algorithms: PSO, CMAES, ABC, JDE, CLPSO and SADE. The comparison, which uses 75 boundary-constrained benchmark problems and three constrained real-world benchmark problems, shows that in general, BSA can solve the benchmark problems more successfully than the comparison algorithms.

Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm

In order to solve numerous practical navigational, geodetic and astro-geodetic problems, it is necessary to transform geocentric cartesian coordinates into geodetic coordinates or vice versa. It is very easy to solve the problem of transforming geodetic coordinates into geocentric cartesian coordinates. On the other hand, it is rather difficult to solve the problem of transforming geocentric cartesian coordinates into geodetic coordinates as it is very hard to define a mathematical relationship between the geodetic latitude (@f) and the geocentric cartesian coordinates (X, Y, Z). In this paper, a new algorithm, the Differential Search Algorithm (DS), is presented to solve the problem of transforming the geocentric cartesian coordinates into geodetic coordinates and its performance is compared with the performances of the classical methods (i.e., Borkowski, 1989; Bowring, 1976; Fukushima, 2006; Heikkinen, 1982; Jones, 2002; Zhang, 2005; Borkowski, 1987; Shu, 2010 and Lin, 1995) and Computational-Intelligence algorithms (i.e., ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011, and CMA-ES). The statistical tests realized for the comparison of performances indicate that the problem-solving success of DS algorithm in transforming the geocentric cartesian coordinates into geodetic coordinates is higher than those of all classical methods and Computational-Intelligence algorithms used in this paper.

Using Uncorrupted Neighborhoods of the Pixels for Impulsive Noise Suppression With ANFIS

In this paper, a novel adaptive network-based fuzzy inference system (ANFIS)-based filter, ABF, is presented for the restoration of images corrupted by impulsive noise (IN). The ABF is performed in two steps. In the first step, impulse detection is realized by using statistical tools. In the second step, a nonlinear filtering scheme based on ANFIS is performed for only the corrupted pixels detected in the first step. To demonstrate the effectivity of ABF at the removal of high-level IN, extensive simulations were realized for ABF and nine different comparison filters. Empirical results indicate that the proposed filter achieves a better performance than the comparison filters in terms of noise suppression and detail preservation, even when the images are highly corrupted by IN

Artificial cooperative search algorithm for numerical optimization problems

In this paper, a new two-population based global search algorithm, the Artificial Cooperative Search Algorithm (ACS), is introduced. ACS algorithm has been developed to be used in solving real-valued numerical optimization problems. For purposes of examining the success of ACS algorithm in solving numerical optimization problems, 91 benchmark problems that have different specifications were used in the detailed tests. The success of ACS algorithm in solving the related benchmark problems was compared to the successes obtained by PSO, SADE, CLPSO, BBO, CMA-ES, CK and DSA algorithms in solving the related benchmark problems by using Wilcoxon Signed-Rank Statistical Test with Bonferroni-Holm correction. The results obtained in the statistical analysis demonstrate that the success achieved by ACS algorithm in solving numerical optimization problems is better in comparison to the other computational intelligence algorithms used in this paper.

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.

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.

Removal of random-valued impulsive noise from corrupted images

This paper presents a novel method for the suppression of random-valued impulsive noise from corrupted images. The proposed method is composed of an efficient noise detector and a pixel-restoration operator. The noise detector has been used to discriminate the uncorrupted pixels from the corrupted pixels. The noise-free intensity values of the corrupted pixels have been computed by using triangle-based linear interpolation and the values of tuning parameters of the proposed method have been optimized with differential evolution algorithm. Extensive simulation experiments indicate that the proposed method significantly outperforms all of the comparison methods mentioned in this paper. The success of the proposed method over comparison methods is due to its excellent detail preservation performance independent from the level of noise density.

CIRCULAR ANTENNA ARRAY DESIGN BY USING EVOLUTIONARY SEARCH ALGORITHMS

Evolutionary Search Algorithms (EA) have been inten- sively used in solving numerical optimization problems. Since design of antenna arrays is a numerical optimization problem, EAs have been intensively used in solving antenna arrays design problems. Although EAs are widely used in antenna array design problems, a performance comparison study of the intensively used EAs for circular antenna ar- ray design problem has been scarcely studied. In this paper, 3 difierent circular antenna array design problems have been solved by using 15 difierent evolutionary search algorithms (i.e., ABC, ACS, BSA, CK, CLPSO, CMAES, DE, E2-DSA, EPSDE, GSA, JADE, JDE, PSO, SADE, S-DSA). The objective function designed for solution of the relevant circular antenna array design problems ensures minimization of side lobe levels, acquisition of maximum directivity, and null control of the non-uniform, planar circular antenna array. Obtained statistical analysis results show that S-DSA solves the relevant circular antenna array design problems statistically better than the other evolutionary algorithms used in this paper.

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.