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Differential evolutionary (DE) algorithm is one of the most frequently used evolutionary computation method for the solution of non-differentiable, complex and discontinuous real value numerical problems. The analytical structure of the mutation and crossover operators used by DE and the initial values of the parameters of the relevant operators affect the problem-solving ability of DE. Unfortunately, there is no analytical method for selecting and initializing the best artificial genetic operators that DE can use to solve a problem. Therefore, there is a need to develop new evolutionary search methods that are parameter-free and insensitive to the artificial genetic operators they use. In this paper, the Bernstein–Levy differential evolution (BDE) algorithm, which has a unique elitist-mutation operator and a Bernstein polynomials-based stochastic parameter-free crossover operator, is introduced. The numerical problem-solving success of BDE is statistically evaluated by using 30 benchmark problems of CEC2014 in the numerical experiments presented. BDE's success in solving the related benchmark problems is statistically compared with six state-of-the-art comparison algorithms. In this paper, three real-world optimization problems are also solved by using the proposed algorithm, BDE. According to statistics generated from the experimental results, BDE is statistically better than comparison methods in solving the related real-world problems. [ABSTRACT FROM AUTHOR]

Pansharpening refers to the synthesis of super-resolution multispectral images (i.e., pansharpened images, PIs) designed to overcome the technical constraints of Earth Observation Satellites. A PI is synthesized by fusing the high-resolution chromatic information carried by the multispectral image with the high-resolution spatial information carried by the panchromatic image. This process generates precise and comprehensive data suitable for diverse applications, such as land cover mapping, urban planning, and natural resource management. Various histogram transformation techniques are utilized in pansharpening methods to improve the chromatic fidelity of PIs. However, many existing histogram transformation techniques are susceptible to chromatic distortions. In this paper, a novel pansharpening method named "Pansharpening Using Dominant Pixels" (PDP) is introduced. PDP employs a new method for histogram transformation that preserves chromatic information. Furthermore, PDP is structurally simple, easy to implement, fast, and capable of producing high-quality pansharpened images. Six Remote Sensing images and seventeen pansharpening methods were used in experiments to examine PDP's success in generating pansharpened images. The experimental results demonstrate that PDP statistically outperforms the comparison methods, synthesizing PIs with high spectral and spatial fidelity. [ABSTRACT FROM AUTHOR]

Camera calibration is an avoidable process for computationalvision applications, such as 3D reverse engineering, industrial robot calibration, optic-pattern recognition, simultaneous localization and mapping, autonomous visual-driving and photogrammetric vision. The camera calibration problem is too complex, nonlinear and multimodal. Traditional camera calibration methods using gradient-based optimization often trap to one of the many local solutions available. Accurate computation ability of traditional camera calibration methods is limited since they use gradient-based optimization methods. Since evolutionary computing algorithms can avoid local solutions of numerical problems, they have the potential to accurately compute the required camera calibration parameters for high-precision computationalvision applications. In this paper, the camera calibration parameters are computed by using 11 evolutionary computing algorithms, i.e., WDE, ABC, PSO, COBIDE, DE, CS, GWO, TLBO, MVMO, FOA and LSHADE. In order to make unbiased evaluation of the camera calibration results provided by the related evolutionary computing algorithms, two gradient-based traditional camera calibration methods, i.e., Zhang and Bouguet, have been used in the conducted experiments in this paper. The camera calibration results of the related methods were used to model a 3D physical test scene by using Structure from Motion photogrammetry method. The reference data set of the related 3D physical scene has been captured by using a 3D terrestrial laser scanner. Statistical comparison of the camera calibration results exposed that WDE supplies statistically better results than other comparison algorithms. [ABSTRACT FROM AUTHOR]

In this paper, weighted differential evolution algorithm (WDE) has been proposed for solving real-valued numerical optimization problems. When all parameters of WDE are determined randomly, in practice, WDE has no control parameter but the pattern size. WDE can solve unimodal, multimodal, separable, scalable, and hybrid problems. WDE has a very fast and quite simple structure, in addition, it can be parallelized due to its non-recursive nature. WDE has a strong exploration and exploitation capability. In this paper, WDE's success in solving CEC' 2013 problems was compared to 4 different EAs (i.e., CS, ABC, JADE, and BSA) statistically. One 3D geometric optimization problem (i.e., GPS network adjustment problem) and 4 constrained engineering design problems were used to examine the WDE's ability to solve real-world problems. Results obtained from the performed tests showed that, in general, problem-solving success of WDE is statistically better than the comparison algorithms that have been used in this paper. [ABSTRACT FROM AUTHOR]

The recently proposed artificial cooperative search (ACS) algorithm is a population-based iterative evolutionary algorithm (EA) for solving real-valued numerical optimization problems. It uses a rotation-invariant line recombination-based mutation strategy and rule-based crossover operator. However, it performs poorly for problems that include closely-related variables because, in these cases, generating uncorrelated feasible trial solution vectors using stochastic crossover methods is extremely difficult, and its mutation and crossover operators are also less effective. This paper adds a new QR-decomposition-based rotation-invariant search strategy to the ACS algorithm to improve its ability to solve such problems. This new, advanced ACS algorithm, called A+, has only one control parameter, α , and experimental results have shown that its performance does not strongly depend on the initial value of α . This paper also examines A+’s performance for noisy point cloud filtering, which is a complex real-world problem. The results of numerical experiments demonstrate that A+’s performance when solving numerical and real-world problems with closely-related variables is better than those of the comparison algorithms. [ABSTRACT FROM AUTHOR]

• Contrast Stretching improves the pansharpening process of CSP. • Linear Contrast Sketching (LCS) has been used by CSP. • Weighted Differential Evolution Algorithm has been used as optimizer. • CSP is an iterative PI method and it supplies high spatial/spectral fidelity. • Analitic multimodal-image-fusion model of the CSP is easy to implement. Pansharpening techniques were developed to generate a super-resolution multispectral pansharpened image, PI, with the combination of a multispectral image carrying high-resolution spectral information with a panchromatic image carrying high resolution spatial information. For using energy resources and communication bandwidth efficiently, Earth Observation Satellites acquire multispectral images with lower spatial resolution when compared to panchromatic images. Contrast Stretching alters the range and statistical distribution of pixel values of an image to facilitate perception of image features and can be used to match histograms of distinct images. The Contrast Stretching Based Pansharpening method, CSP, has been introduced in this paper. CSP considers the pansharpening as a rescaling-based pixel-level image fusion problem in spatial domain. CSP uses the contrast stretching to generate two modified-multispectral images and one modified-panchromatic image, which are used to compute pansharpened image. The Weighted Differential Evolution Algorithm has been used to optimize the numerical values of internal parameters of CSP. The successes of CSP and 17 different pansharpening methods have been statistically compared by using three Test Image sets with different characteristics. Ten different image quality measures have been used in accuracy assessment analysis of the PIs generated by the related methods used in the Experiments. Statistical analysis exposed that CSP generates more pleasing PIs both quantitatively and qualitatively compared to the comparison methods employed in this paper. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Differential Evolution Algorithm (DE) is a commonly used stochastic search method for solving real-valued numerical optimization problems. Unfortunately, DE's problem solving success is very sensitive to the internal parameters of the artificial numerical genetic operators (i.e., mutation and crossover operators) used. Although several mutation and crossover methods have been developed for DE, there is not still an analytical method that can be used to select the most efficient mutation and crossover method while solving a problem with DE. Therefore, selection and parameter tuning processes of artificial numerical genetic operators used by DE are based on a trial-and-error process which is time consuming. The development of modern DE versions has been focused on developing fast, structurally simple and efficient genetic operators that are not sensitive to the initial values of their internal parameters. Problem solving successes of the Universal Differential Algorithms (uDE) are not sensitive to the structure and internal parameters of the related artificial numerical genetic operators used, unlike DE. In this paper a new uDE, Bezier Search Differential Evolution Algorithm , BeSD, has been proposed. BeSD's mutation and crossover operators are structurally simple, fast, unique and produce highly efficient trial patterns. BeSD utilizes a partially elitist unique mutation operator and a unique crossover operator. In this paper, the experiments were performed by using the 30 benchmark problems of CEC2014 with Dim =30, and one 3D viewshed problem as a real world application. The problem solving success of BeSD was statistically compared with five top-methods of CEC2014, i.e., CRMLSP, MVO, WA, SHADE and LSHADE by using Wilcoxon Signed Rank test. Statistical results exposed that BeSD's problem solving success is better than those of the comparison methods in general. • A new parameter-free crossover operator. • A new multi-component mutation operator. • A new Universal Differential Evolution Algorithm. • Real-valued evolutionary search for numerical function optimization. • Evolutionary 3D viewshed analysis. [ABSTRACT FROM AUTHOR]

• Bernstein polynomials based parameter-free crossover. • A new universal/parameter-free Differential Evolution. • Real-valued numerical function optimization. • Evolutionary Image Vectorization. • Evolutionary Digital Terrain Model Simplification. The standard Differential Evolution Algorithm (sDE) is a stochactic search method commonly used in evolutionary computing. The problem solving success of sDE is highly sensitive to the genetic operators used and the initial values of the parameters of these operators. Since a universal Differential Evolution Algorithm (uDE) is not sensitive to the structure and parameter values of the genetic operators used, it is parameter-free in practice and easier to control than sDE. uDE does not need a trial-and-error process when selecting the genetic operators and initial values of intrinsic parameter of related genetic operators to solve the problem, unlike the sDE. Therefore, the use and adaptation of a uDE to solve different types of numerical engineering problems is easy and time-consuming compared to sDE. In this paper, a new uDE, Bernstain-Search Differential Evolution Algorithm (BSD), is introduced. BSD is new and easily controllable, simple structured, non-recursive, highly efficient, fast and practically parameter-free uDE. BSD have a too feasible random crossover and mutation process and does not have a control-parameter setting process, contrary to sDE and its improved variants. In this paper, 30 benchmark problems of CEC'2014, 60 classic benchmark problems, image evolution problems for 12 test images and one Triangulated Irregular Network (TIN) refinement problem were used in the experiments performed to investigate the problem solving success of BSD, statistically. Four tested methods (i.e. , ABC, JADE, CUCKOO, WDE) were used in the conducted experiments. Problem solving successes of BSD and tested methods were statistically compared by using Wilcoxon Signed Rank Test piecewisely. Results obtained from the performed tests showed that in general, problem solving success of BSD is statistically better than the tested methods that have been used in this paper. [ABSTRACT FROM AUTHOR]