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Calcification is a process of accumulation of calcium in tissues and deposition of calcium salts by the crystallization of PO43− and ionized calcium (Ca2+). It is a crucial process in the development of bones and teeth. However, pathological calcification can occur in almost any soft tissue of the organism. The better studied is vascular calcification, where calcium salts can accumulate in the intima or medial layer or in aortic valves, and it is associated with higher mortality and cardiovascular events, including myocardial infarction, stroke, aortic and peripheral artery disease (PAD), and diabetes or chronic kidney disease (CKD), among others. The process involves an intricate interplay of different cellular components, endothelial cells (ECs), vascular smooth muscle cells (VSMCs), fibroblasts, and pericytes, concurrent with the activation of several signaling pathways, calcium, Wnt, BMP/Smad, and Notch, and the regulation by different molecular mediators, growth factors (GFs), osteogenic factors and matrix vesicles (MVs). In the present review, we aim to explore the cellular players, molecular pathways, biomarkers, and clinical treatment strategies associated with vascular calcification to provide a current and comprehensive overview of the topic. [ABSTRACT FROM AUTHOR]

Image segmentation, the process of partitioning an image into meaningful regions, is a fundamental step in image processing, crucial for applications like computer vision, medical imaging, and object recognition. Image segmentation is an essential step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is prevalent. Two well-known approaches to histogram-based thresholding are Otsu's and Kapur's methods in gray images that maximize the between-class variance and the entropy measure, respectively. Both techniques were introduced for bi-level thresholding. However, these techniques can be expanded to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. To this end, various optimization techniques have been used to overcome this drawback. Recently, a new optimization algorithm called Battle Royal Optimizer (BRO) has been published, which is shown to solve various optimization tasks effectively. In this study, BRO has been applied to yield optimum threshold values in multilevel image thresholding. Here is also demonstrated the effectiveness of BRO for image segmentation on various images from the standard publicly accessible Berkeley segmentation dataset. We compare the performance of BRO to other state-of-the-art optimization-based methods and show that it outperforms them in terms of fitness value, Peak Signal-to-Noise Ratio, Structural Similarity Index Method, Feature Similarity Index Method (FSIM), Color FSIM (FSIMc), and Standard Deviation. These results underscore the potential of BRO as a promising solution for image segmentation tasks, particularly through its effective implementation of multilevel thresholding. [ABSTRACT FROM AUTHOR]

Minimum cross-entropy is widely used to find the best threshold values for image segmentation; this technique is known as MCET. However, when the number of thresholds increases, it becomes computationally expensive. Under such circumstances, employing a metaheuristic algorithm (MA) is a good choice. The Runge Kutta (RUN) optimization algorithm is MA recently proposed for solving global optimization problems. Like other MA, the RUN tends to fall into sub-optimal solutions presenting a premature convergence, especially in high-dimensional problems. This article aims to improve the RUN by merging it with opposition-based learning (OBL), creating a hybrid algorithm called RUN-OBL. By doing this hybridization, the RUN can search in two directions, and its performance is considerably improved, solving its drawbacks. One of the most significant improvements in this paper is that the RUN-OBL can handle high-dimensional search spaces escaping from local optimal solutions. The performance of the RUN-OBL is tested over different experiment series. Firstly, the RUN-OBL is tested using the CEC'17 set of benchmark functions for numerical optimization. Here, comparisons with other MA and the Friedman test were conducted. Secondly, the proposed algorithm segments digital images using the MCET. A set of benchmark images with different degrees of complexity are used. The RUN-OBL for image thresholding is tested in the third experiment series over a set of medical images (chest x-ray). A comparative study of the segmentation results is conducted to verify the efficiency of the proposal. Here different MA and other image segmentation methodologies were used. The comparisons were performed in terms of the fitness functions, peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), and structural similarity index (SSIM). Experimental results demonstrate that the proposed RUB-OBL approach is better regarding quality and consistency for segmentation purposes and the optimization of complex problems. [ABSTRACT FROM AUTHOR]

Simple Summary: Allograft inflammatory factor 1 (AIF1) is a calcium-binding protein that participates in intracellular processes such as phagocytosis, membrane ruffling and F-actin remodeling. Due to its multiple functions, AIF1 is linked with the activation of macrophages and several diseases such as kidney disease, rheumatoid arthritis, cancer, cardiovascular diseases, metabolic diseases, neurological disorders and transplants. In this review, we present a comprehensive review of the known structure, functions and role of AIF1 in inflammatory diseases. Macrophages are a type of immune cell distributed throughout all tissues of an organism. Allograft inflammatory factor 1 (AIF1) is a calcium-binding protein linked to the activation of macrophages. AIF1 is a key intracellular signaling molecule that participates in phagocytosis, membrane ruffling and F-actin polymerization. Moreover, it has several cell type-specific functions. AIF1 plays important roles in the development of several diseases: kidney disease, rheumatoid arthritis, cancer, cardiovascular diseases, metabolic diseases and neurological disorders, and in transplants. In this review, we present a comprehensive review of the known structure, functions and role of AIF1 in inflammatory diseases. [ABSTRACT FROM AUTHOR]

Histone acetylation plays a vital role in organizing chromatin, regulating gene expression and controlling the cell cycle. The first histone acetyltransferase to be identified was histone acetyltransferase 1 (HAT1), but it remains one of the least understood acetyltransferases. HAT1 catalyzes the acetylation of newly synthesized H4 and, to a lesser extent, H2A in the cytoplasm. However, 20 min after assembly, histones lose acetylation marks. Moreover, new noncanonical functions have been described for HAT1, revealing its complexity and complicating the understanding of its functions. Recently discovered roles include facilitating the translocation of the H3H4 dimer into the nucleus, increasing the stability of the DNA replication fork, replication-coupled chromatin assembly, coordination of histone production, DNA damage repair, telomeric silencing, epigenetic regulation of nuclear lamina-associated heterochromatin, regulation of the NF-κB response, succinyl transferase activity and mitochondrial protein acetylation. In addition, the functions and expression levels of HAT1 have been linked to many diseases, such as many types of cancer, viral infections (hepatitis B virus, human immunodeficiency virus and viperin synthesis) and inflammatory diseases (chronic obstructive pulmonary disease, atherosclerosis and ischemic stroke). The collective data reveal that HAT1 is a promising therapeutic target, and novel therapeutic approaches, such as RNA interference and the use of aptamers, bisubstrate inhibitors and small-molecule inhibitors, are being evaluated at the preclinical level. [ABSTRACT FROM AUTHOR]

Optimization has increased its use in different domains for accurately solving challenging problems. Complex optimization problems require the use of methods that possess the capabilities to properly explore the search spaces. The traditional algorithms commonly tend to fail in suboptimal values during the optimization process; this fact affects the quality of the solutions. This situation occurs for different reasons, but the lack of diversity due to the use of exploitation operators is the most common. Brainstorm optimization is an alternative method based on the social strategy to generate new innovative ideas in work groups. In brainstorm optimization, each solution representing an idea and brainstorm process is performed using clustering algorithms. However, brainstorm optimization is not able to thoroughly explore the search space, and its diversity is reduced. It does not possess any mechanism to escape from suboptimal solutions. Besides, the computational effort is also increased in the iterative process. This paper presents a modified version of brainstorm optimization that improves its performance. In the proposed algorithm, chaotic maps and opposition-based learning are applied to initialize the solutions for a given problem. Moreover, in the optimization process, the positions of the initial population are updated using the disruptor operator. After updating the population, opposition-based learning is used again to analyze the opposite solutions. The combination of chaotic maps, opposition-based learning and disruption operator improve the exploration ability of brainstorm optimization by increasing the diversity of the population. The proposed method has been evaluated using a set of benchmark functions, and it has been also used for feature selection in data mining. The results show the high efficacy of the proposed method to determine the optimal solutions of the tested functions. [ABSTRACT FROM AUTHOR]

This paper proposes a general multi-objective thresholding segmentation methodology for color images and a quality metric designed to prevent and quantify the overlapping effect of segmented images. Multi-level thresholding (MTH) has been used to segment color images in recent years; this process considers each channel as a single grayscale image and applies the MTH independently. Although this method provides competitive results, the inherent relationship among color channels is disregarded. Such approaches generate spurious classes on overlapping regions, where new colors are generated, especially on the borders of the objects. The proposed multi-objective color thresholding (MOCTH) approach performs image segmentation while preserving the relationship between image channels. MOCTH is aimed to reduce the overlapping effect on segmented color images without performing additional post-processing. To measure the overlapping classes on a thresholded color image, the overlapping index is proposed to quantify the pixels affected. The presented approach is analyzed on two color spaces (RGB and CIE L*a*b*) using three multi-objective algorithms; they are NSGA-III, SPEA-2, and MOPSO. Results provide evidence pointing out to a better segmentation from MOCTH over the traditional single-objective approaches while reducing overlapped areas on the image. [ABSTRACT FROM AUTHOR]

Highlights • We propose a multi-objective version of the Multiverse Optimizer (MOMVO). • The problem of image thresholding is solved by a multi-objective algorithm. • The Otsu and Kapur's methods are combined in a multi-objective problem. • The MOMVO is tested over different images for thresholding. • Comparisons support the performance of the proposed MOMVO. Graphical abstract Abstract Image segmentation is among the most important techniques in image processing, and many methods have been developed to perform this task. This paper presents a new multi-objective metaheuristic based on a multi-verse optimization algorithm to segment grayscale images via multi-level thresholding. The proposed approach involves finding an approximate Pareto-optimal set by maximizing the Kapur and Otsu objective functions. Both Kapur's and Otsu's methods are highly used for image segmentation performed by means of bi-level and multi-level thresholding. However, each of them has certain characteristics and limitations. Several metaheuristic approaches have been proposed in the literature to separately optimize these objective functions in terms of accuracy, whereas only a few multi-objective approaches have explored the benefits of the joint use of Kapur and Otsu's methods. However, the computational cost of Kapur and Otsu is high and their accuracy needs to be improved. The proposed method, called Multi-objective Multi-verse Optimization, avoids these limitations. It was tested using 11 natural grayscale images and its performance was compared against three of well-known multi-objective algorithms. The results were analyzed based on two sets of measures, one to assess the performance of the proposed method as a multi-objective algorithm, and the other to evaluate the accuracy of the segmented images. The results showed that the proposed method provides a better approximation to the optimal Pareto Front than the other algorithms in terms of hypervolume and spacing. Moreover, the quality of its segmented image is better than those of the other methods in terms of uniformity measures. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-objective optimization approach for the optimal placement of a weather radar network. Assuming a finite geographical region and a limited number of weather radars, a network is produced by considering the minimization of the total partial beam blocking percentage of the network and the minimization of network installation and maintenance costs. Several constraints on the solutions are considered such as terrain topography, radar beam elevation, distance between radars and distance from the power grid and roads. In order to reduce the number of possible combinations of radar networks, the solution space is discretized into a gridded system. The multi-objective optimization problem is solved by four different evolutionary algorithms, and the obtained results are used in a land clutter simulation of the whole network. The presented approach can serve as an analysis tool for a decision support system by providing meteorologist a set of Pareto optimal solutions to facilitate the selection of future prime sites for the installation of weather radars. [ABSTRACT FROM AUTHOR]

The segmentation of digital images is one of the most important steps in an image processing or computer vision system. It helps to classify the pixels in different regions according to their intensity level. Several segmentation techniques have been proposed, and some of them use complex operators. The techniques based on thresholding are the easiest to implement; the problem is to select correctly the best threshold that divides the pixels. An interesting method to choose the best thresholds is the minimum cross entropy (MCET), which provides excellent results for bi-level thresholding. Nevertheless, the extension of the segmentation problem into multiple thresholds increases significantly the computational effort required to find optimal threshold values. Each new threshold adds complexity to the formulation of the problem. Classic methods for image thresholding perform extensive searches, while new approaches take advantage of heuristics to reduce the search. Evolutionary algorithms use heuristics to optimize criteria over a finite number of iterations. The correct selection of an evolutionary algorithm to minimize the MCET directly impacts the performance of the method. Current approaches take a large number of iterations to converge and a high rate of MCET function evaluations. The electromagnetism-like optimization (EMO) algorithm is an evolutionary technique which emulates the attraction-repulsion mechanism among charges for evolving the individuals of a population. Such technique requires only a small number of evaluations to find the optimum. This paper proposes the use of EMO to search for optimal threshold values by minimizing the cross entropy function while reducing the amount of iterations and function evaluations. The approach is tested on a set of benchmark images to demonstrate that is able to improve the convergence and velocity; additionally, it is compared with similar state-of-the-art optimization approaches. [ABSTRACT FROM AUTHOR]

Solar cells are considered as a clean source of energy, and their application includes industrial and domestic users. Most of the algorithms used to design solar cells are tested (and used) only for domestic implementations. However, it is necessary to have accurate mechanisms for solar cell design that can be used in both industrial and domestic energy systems. To achieve this goal, this article introduces an improved version of the whale optimization Algorithm that uses the opposition-based learning to enhance the exploration of the search space. This algorithm is applied to estimate the parameters of solar cells using three different diode models. Such models are the single diode model, the double diode model and the three diode model, each of them has different internal parameters that must be accurately estimated in order to have a good performance of the solar cells. The inclusion of the three diode model is due it represents a more accurate representation of the solar cells behavior in industrial applications. For experiments and comparisons, there are used similar approaches and datasets from solar cells and photovoltaic modules. Moreover, the proposed method has also been tested over different benchmark optimization functions to verify its exploration capabilities. The experiments and comparisons support the performance of the proposed approach in complex optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents two multi-criteria optimization techniques: the Multi-Objective Crow Search Algorithm (MOCSA) and an improved chaotic version called Multi-Objective Chaotic Crow Search Algorithm (MOCCSA). Both methods MOCSA and MOCCSA are based on an enhanced version of the recently published Crow Search Algorithm. Crows are intelligent animals with interesting strategies for protecting their food hatches. This compelling behavior is extended into a Multi-Objective approach. MOCCSA uses chaotic-based criteria on the optimization process to improve the diversity of solutions. To determinate if the performance of the algorithm is significantly enhanced, the incorporation of a chaotic operator is further validated by a statistical comparison between the proposed MOCCSA and its chaotic-free counterpart (MOCSA) indicating that the results of the two algorithms are significantly different from each other. The performance of MOCCSA is evaluated by a set of standard benchmark functions, and the results are contrasted with two well-known algorithms: Multi-Objective Dragonfly Algorithm and Multi-Objective Particle Swarm Optimization. Both quantitative and qualitative results show competitive results for the proposed approach. [ABSTRACT FROM AUTHOR]

Real life optimization problems require techniques that properly explore the search spaces to obtain the best solutions. In this sense, it is common that traditional optimization algorithms fail in local optimal values. The Sine Cosine Algorithms (SCA) has been recently proposed; it is a global optimization approach based on two trigonometric functions. SCA uses the sine and cosine functions to modify a set of candidate solutions; such operators create a balance between exploration and exploitation of the search space. However, like other similar approaches, SCA tends to be stuck into sub-optimal regions that it is reflected in the computational effort required to find the best values. This situation occurs due that the operators used for exploration do not work well to analyze the search space. This paper presents an improved version of SCA that considers the opposition based learning (OBL) as a mechanism for a better exploration of the search space generating more accurate solutions. OBL is a machine learning strategy commonly used to increase the performance of metaheuristic algorithms. OBL considers the opposite position of a solution in the search space. Based on the objective function value, the OBL selects the best element between the original solution and its opposite position; this task increases the accuracy of the optimization process. The hybridization of concepts from different fields is crucial in intelligent and expert systems; it helps to combine the advantages of algorithms to generate more efficient approaches. The proposed method is an example of this combination; it has been tested over several benchmark functions and engineering problems. Such results support the efficacy of the proposed approach to find the optimal solutions in complex search spaces. [ABSTRACT FROM AUTHOR]

The using of solar energy has been increased since it is a clean source of energy. In this way, the design of photovoltaic cells has attracted the attention of researchers over the world. There are two main problems in this field: having a useful model to characterize the solar cells and the absence of data about photovoltaic cells. This situation even affects the performance of the photovoltaic modules (panels). The characteristics of the current vs. voltage are used to describe the behavior of solar cells. Considering such values, the design problem involves the solution of the complex non-linear and multi-modal objective functions. Different algorithms have been proposed to identify the parameters of the photovoltaic cells and panels. Most of them commonly fail in finding the optimal solutions. This paper proposes the Chaotic Whale Optimization Algorithm (CWOA) for the parameters estimation of solar cells. The main advantage of the proposed approach is using the chaotic maps to compute and automatically adapt the internal parameters of the optimization algorithm. This situation is beneficial in complex problems, because along the iterative process, the proposed algorithm improves their capabilities to search for the best solution. The modified method is able to optimize complex and multimodal objective functions. For example, the function for the estimation of parameters of solar cells. To illustrate the capabilities of the proposed algorithm in the solar cell design, it is compared with other optimization methods over different datasets. Moreover, the experimental results support the improved performance of the proposed approach regarding accuracy and robustness. [ABSTRACT FROM AUTHOR]

Segmentation is considered the central part of an image processing system due to its high influence on the posterior image analysis. In recent years, the segmentation of magnetic resonance (MR) images has attracted the attention of the scientific community with the objective of assisting the diagnosis in different brain diseases. From several techniques, thresholding represents one of the most popular methods for image segmentation. Currently, an extensive amount of contributions has been proposed in the literature, where thresholding values are obtained by optimizing relevant criteria such as the cross entropy. However, most of such approaches are computationally expensive, since they conduct an exhaustive search strategy for obtaining the optimal thresholding values. This paper presents a general method for image segmentation. To estimate the thresholding values, the proposed approach uses the recently published evolutionary method called the Crow Search Algorithm (CSA) which is based on the behavior in flocks of crows. Different to other optimization techniques used for segmentation proposes, CSA presents a better performance, avoiding critical flaws such as the premature convergence to sub-optimal solutions and the limited exploration-exploitation balance in the search strategy. Although the proposed method can be used as a generic segmentation algorithm, its characteristics allow obtaining excellent results in the automatic segmentation of complex MR images. Under such circumstances, our approach has been evaluated using two sets of benchmark images; the first set is composed of general images commonly used in the image processing literature, while the second set corresponds to MR brain images. Experimental results, statistically validated, demonstrate that the proposed technique obtains better results in terms of quality and consistency. [ABSTRACT FROM AUTHOR]

• Novel shape descriptor deploying alphabetical shaped geometry. • Computationally simple feature characterization scheme. • Exploits the congruence nature of formulated patterns. • Renders superior performance on benchmarked dataset in comparison with its peers. • Highly robust towards affine transformations. Feature representation patterns serving shape retrieval have gained considerable attention over recent years. Accordingly, a geometry-based characterization arrangement based on the l -shape pattern is adopted in this paper for shape description. The presented l -shape pattern descriptor bounds shape edges for providing highly localized distinct features supporting characterization. Then a novel feature representation scheme fabricates these shapes into histograms subsequently, employed for matching and retrieval. The metric Bull's Eye Retrieval (BER) rate is deployed for retrieval analysis on the Kimia-99, MPEG-7 and Tari-1000 datasets that reveal a uniform and remarkable performance higher than 90% over its predecessors. The congruence nature of the l -shaped geometrical arrangement ensures its robustness towards diverse affine transformations and warrants increased performance. The complexity associated with descriptor realization along space and time reveals its lightweight simplicity and efficiency. [ABSTRACT FROM AUTHOR]

Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and enhancing the classification accuracy in general, accompanied by great significance in different fields like data mining applications, pattern classification, and data analysis. Using powerful optimization algorithms is crucial to obtaining the best subsets of information in FS. Different metaheuristics, such as the Sooty Tern Optimization Algorithm (STOA), help to optimize the FS problem. However, such kind of techniques tends to converge in sub-optimal solutions. To overcome this problem in the STOA, an improved version called mSTOA is introduced. It employs the balancing exploration/exploitation strategy, self-adaptive of the control parameters strategy, and population reduction strategy. The proposed approach is proposed for solving the FS problem, but also it has been validated over benchmark optimization problems from the CEC 2020. To assess the performance of the mSTOA, it has also been tested with different algorithms. The experiments in terms of FS provide qualitative and quantitative evidence of the capabilities of the mSTOA for extracting the optimal subset of features. Besides, statistical analyses and no-parametric tests were also conducted to validate the result obtained by the mSTOA in optimization. • An enhanced algorithm called the mSTOA that employs three strategies is proposed. • mSTOA efficiency and performance are verified on several benchmarks. • CEC'20 test suite are used for algorithm validation. • mSTOA is proposed as an alternate feature selection approach. [ABSTRACT FROM AUTHOR]

Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and enhancing the classification accuracy in general, accompanied by great significance in different fields like data mining applications, pattern classification, and data analysis. Using powerful optimization algorithms is crucial to obtaining the best subsets of information in FS. Different metaheuristics, such as the Sooty Tern Optimization Algorithm (STOA), help to optimize the FS problem. However, such kind of techniques tends to converge in sub-optimal solutions. To overcome this problem in the STOA, an improved version called mSTOA is introduced. It employs the balancing exploration/exploitation strategy, self-adaptive of the control parameters strategy, and population reduction strategy. The proposed approach is proposed for solving the FS problem, but also it has been validated over benchmark optimization problems from the CEC 2020. To assess the performance of the mSTOA, it has also been tested with different algorithms. The experiments in terms of FS provide qualitative and quantitative evidence of the capabilities of the mSTOA for extracting the optimal subset of features. Besides, statistical analyses and no-parametric tests were also conducted to validate the result obtained by the mSTOA in optimization. • An enhanced algorithm called the mSTOA that employs three strategies is proposed. • mSTOA efficiency and performance are verified on several benchmarks. • CEC'20 test suite are used for algorithm validation. • mSTOA is proposed as an alternate feature selection approach. [ABSTRACT FROM AUTHOR]

The search for new energy resources is a crucial task nowadays. Research on the use of solar energy is growing every year. The aim is the design of devices that can produce a considerable amount of energy using the Sun's radiation. The modeling of solar cells (SCs) is based on the estimation of the intrinsic parameters of electrical circuits that simulate their behavior based on the current vs. voltage characteristics. The problem of SC design is defined by highly nonlinear and multimodal objective functions. Most of the algorithms proposed to find the best solutions become trapped into local solutions. This paper introduces the Chaotic Improved Artificial Bee Colony (CIABC) algorithm for the estimation of SC parameters. It combines the use of chaotic maps instead random variables with the search capabilities of the Artificial Bee Colony approach. CIABC has also been modified to avoid the generation of new random solutions, preserving the information of previous iterations. In comparison with similar optimization methods, CIABC is able to find the global solution of complex and multimodal objective functions. Experimental results and comparisons prove that the proposed technique can design SCs, even with the presence of noise. [ABSTRACT FROM AUTHOR]

This work addresses the optimal design of a flexible heat exchanger network using model-based optimization, applied to hydrogen production by means of an ethanol steam reforming process. High efficiencies are obtained at different hydrogen production levels ranging from 25 to 100% of a nominal output. System structure, heat exchanger sizing, and operation conditions are simultaneously settled, ensuring both operational feasibility and optimality. The system involves a reforming reactor, vaporization and reheating equipment, combustors, and a heat exchanger network system. A multi-period nonlinear optimization problem (NLP) was formulated to account for the production level distribution. Equipment sizing constraints and structural constraints link the different scenarios. The trade-off between area and efficiency is analyzed using a multi-objective epsilon-constraint approach. Models were developed in the GAMS environment. The resulting solutions, for the maximum area case, maintain alcohol combustion at low levels showing efficiencies around 63% in each operational level. Pareto Optimal diagram shows that a 1% reduction of efficiency allows a 50% decrease in total required heat exchanger area by 50%. [ABSTRACT FROM AUTHOR]

Alternative energies will be the engine that moves the world in the future; therefore, researchers focus their attention on the problems related to designing and properly exploiting this type of energy. Artificial intelligence methods are explored as tools to improve different branches of alternative energies. In this context, metaheuristic algorithms (MA) are optimization techniques that have been explored in the field of solar cell design. This article analyses the performance of different MAs for modeling solar cells by estimating the parameters of photovoltaic (PV) diode models. The MA used are part of a recent generation of techniques that include innovative processes that permits a proper selection of the optimization operators in the iterative search; such algorithms are the Artificial Hummingbird Algorithm (AHA), Aquila Optimizer (AO), African Vulture Optimization Algorithm (AVOA), Equilibrium Optimizer (EO), Golden Eagle Optimizer (GEO), Side Blotched Lizard Algorithm (SBLA), Social Network Search (SNS), and Reptile Search Algorithm (RSA). We implemented them to optimize the parameters of different models of solar cells with arrays of one, two, and three diodes. The experimental analysis performed on an R.T.C. France solar cell demonstrates which methods are the most suitable for the identification of unknown parameters in three PV models named single diode solar cell model (SDSCM), double diode solar cell model (DDSCM), and triple diode solar cell model (TDSCM). The results were statistically analyzed with Voltage–Current and Voltage–Power tests, comparisons of the best-optimized parameters, convergence plots, four error types such as Root Mean Square Error (RMSE), Absolute Error (AE), Mean Absolute Error (MAE), and Normalized Root Mean Square Error (NRMSE), a run-time analysis and non-parametric tests. The study converges in comparing and defining which of the implemented MA's is the best alternative for the design of solar cells, concluding that some tested methods, such as AHA, can improve the efficiency of this class of devices; with RMSE values of 0.000986 for SDSCM, 0.00098353 for DDSCM, and 0.0009855 for TDSCM and the fourth-best average execution time according to the overall ranking. • Comparison of performance of metaheuristics to estimate parameters of solar cells. • Diode models are used, and eight metaheuristics estimate their parameters. • Different metrics are employed to validate the performance of the algorithms. • Comparison, statistic and non-parametric test help to validate the analysis. • Probe the stability of the AHA algorithm by the consistency of the results. [ABSTRACT FROM AUTHOR]

Image segmentation plays an important role in image processing and computer vision. It is often used to classify an image into separate regions, which ideally correspond to different real-world objects. Several segmentation methods have been proposed in the literature, being thresholding techniques the most popular. In such techniques, it is selected a set of proper threshold values that optimize a determined functional criterion, so that each pixel is assigned to a determined class according to its corresponding threshold points. One interesting functional criterion is the Tsallis entropy, which gives excellent results in bi-level thresholding. However, when it is applied to multilevel thresholding, its evaluation becomes computationally expensive, since each threshold point adds restrictions, multimodality and complexity to its functional formulation. Therefore, in the process of finding the appropriate threshold values, it is desired to limit the number of evaluations of the objective function (Tsallis entropy). Under such circumstances, most of the optimization algorithms do not seem to be suited to face such problems as they usually require many evaluations before delivering an acceptable result. On the other hand, the Electromagnetism-Like algorithm is an evolutionary optimization approach which emulates the attraction–repulsion mechanism among charges for evolving the individuals of a population. This technique exhibits interesting search capabilities whereas maintains a low number of function evaluations. In this paper, a new algorithm for multilevel segmentation based on the Electromagnetism-Like algorithm is proposed. In the approach, the optimization algorithm based on the electromagnetism theory is used to find the optimal threshold values by maximizing the Tsallis entropy. Experimental results over several images demonstrate that the proposed approach is able to improve the convergence velocity, compared with similar methods such as Cuckoo search, and Particle Swarm Optimization. [ABSTRACT FROM AUTHOR]

Image segmentation is an indispensable part of computer vision applications, and image thresholding is a popular one due to its simplicity and robustness. Generalised Masi entropy (GME) is an image thresholding method that exploits the additive/non-extensive information using entropic measure (r). r shows the measure of degree of extensibility and non-extensibility available in an image. From the literature, all research considered it as a fixed coefficient, while finding a proper value for r can enhance the efficacy of thresholding. This paper proposes a simple yet effective approach for adaptively finding a proper value for r without any background knowledge regarding the distribution of histogram. To this end, a new representation is proposed so that it can be used with any type of population-based metaheuristic (PBMH) algorithms. For the optimisation process, we use differential evolution (DE), as a representative. In addition, to further improve efficacy, we improve DE algorithm based on one-step k -means clustering, random-based sampling, Gaussian-based sampling, and opposition-based learning. Our extensive experiments compared to the most recent approaches on a set of benchmark images and in terms of several criteria clearly show that the proposed approach not only can find the proper value for r automatically but also it can improve the efficacy of GME-based image thresholding methods. [ABSTRACT FROM AUTHOR]

Abstract: Segmentation is one of the most important tasks in image processing. It consists of classifying the pixels into two or more groups depending on their intensity levels and a threshold value. The quality of the segmentation depends on the method applied to select the threshold. The use of the classical implementations for Multilevel Thresholding is computationally expensive since they exhaustively search the best values to optimize the objective function. Under such conditions, the use of optimization evolutionary approaches has been extended. The Electromagnetism-like Algorithm (EMO) is an evolutionary method which mimics the attraction–repulsion mechanism among charges to evolve the members of a population. Different to other algorithms, EMO exhibits interesting search capabilities whereas maintains a low computational overhead. In this paper, a Multilevel Thresholding (MT) algorithm based on the EMO is introduced. The approach combines the good search capabilities of EMO algorithm with objective functions proposed by the popular MT methods of Otsu and Kapur. The algorithm takes random samples from a feasible search space inside the image histogram. Such samples build each particle in the EMO context whereas its quality is evaluated considering the objective that is function employed by the Otsu׳s or Kapur׳s method. Guided by these objective values the set of candidate solutions are evolved through the EMO operators until an optimal solution is found. The approach generates a multilevel segmentation algorithm which can effectively identify the threshold values of a digital image in a reduced number of iterations. Experimental results show performance evidence of the implementation of EMO for digital image segmentation. [Copyright &y& Elsevier]

In order to improve the performance of solar energy systems, accurate modeling of current vs. voltage ( I - V ) characteristics of solar cells has attracted the attention of various researches. The main drawback in accurate modeling is the lack of information about the precise parameter values which indeed characterize the solar cell. Since such parameters cannot be extracted from the datasheet specifications, an optimization technique is necessary to adjust experimental data to the solar cell model. Considering the I - V characteristics of solar cells, the optimization task involves the solution of complex non-linear and multi-modal objective functions. Several optimization approaches have been proposed to identify the parameters of solar cells. However, most of them obtain sub-optimal solutions due to their premature convergence and their difficulty to overcome local minima in multi-modal problems. This paper proposes the use of the ABC (artificial bee colony) algorithm to accurately identify the solar cells' parameters. The ABC algorithm is an evolutionary method inspired by the intelligent foraging behavior of honey bees. In comparison with other evolutionary algorithms, ABC exhibits a better search capacity to face multi-modal objective functions. In order to illustrate the proficiency of the proposed approach, it is compared to other well-known optimization methods. Experimental results demonstrate the high performance of the proposed method in terms of robustness and accuracy. [ABSTRACT FROM AUTHOR]

Highlights: [•] We aid decision-makers in problems with a large number of environmental objectives. [•] Our method is based on a novel mixed-integer linear program. [•] We compare our approach against other well-known clustering methods. [Copyright &y& Elsevier]

Abstract: This paper is aimed at presenting a methodology for the simultaneous synthesis of solid oxide fuel cell (SOFC) based systems and their associated heat exchangers network (HEN). The optimization model is formulated as a mixed integer nonlinear mathematical programming (MINLP) problem. The optimization goal is to maximize the overall net efficiency of the integrated system. Ethanol and glycerin are studied as fuels fed to the SOFC system as they constitute two renewable and sustainable sources of energy. As main results, net global efficiency values of 69.35% and 66.97% were computed for ethanol and glycerin, respectively. For both cases, the computed optimal operation pressure, the SOFC operation temperature and the fed water/fuel molar ratio values were 2 atm, 1073 K and 3, respectively. [Copyright &y& Elsevier]

In this paper, a multilevel thresholding (MT) algorithm based on the harmony search algorithm (HSA) is introduced. HSA is an evolutionary method which is inspired in musicians improvising new harmonies while playing. Different to other evolutionary algorithms, HSA exhibits interesting search capabilities still keeping a low computational overhead. The proposed algorithm encodes random samples from a feasible search space inside the image histogram as candidate solutions, whereas their quality is evaluated considering the objective functions that are employed by the Otsu's or Kapur's methods. Guided by these objective values, the set of candidate solutions are evolved through the HSA operators until an optimal solution is found. Experimental results demonstrate the high performance of the proposed method for the segmentation of digital images. [ABSTRACT FROM AUTHOR]

Medical imaging is a relevant field of application of image processing algorithms. In particular, the analysis of white blood cell (WBC) images has engaged researchers from fields of medicine and computer vision alike. Since WBCs can be approximated by a quasicircular form, a circular detector algorithm may be successfully applied. This paper presents an algorithm for the automatic detection of white blood cells embedded into complicated and cluttered smear images that considers the complete process as a circle detection problem. The approach is based on a nature-inspired technique called the electromagnetism-like optimization (EMO) algorithm which is a heuristic method that follows electromagnetism principles for solving complex optimization problems. The proposed approach uses an objective function which measures the resemblance of a candidate circle to an actual WBC. Guided by the values of such objective function, the set of encoded candidate circles are evolved by using EMO, so that they can fit into the actual blood cells contained in the edge map of the image. Experimental results from blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique regarding detection, robustness, and stability. [ABSTRACT FROM AUTHOR]

Abstract: The development of biofuels has gained much attention in recent years. Thermodynamic analyses to obtain energy from biofuels using fuel cells were addressed in previous works for a variety of processes. In those processes, the determination of the best conditions to achieve high efficiency values in the conversion of chemical energy into electrical power is a critical issue from the net global energy efficiency point of view. In this regard, a main aspect is to address the energy integration of the whole process. In a previous paper, the authors dealt with energy integration studies for glycerin- and ethanol-based processors coupled to PEM fuel cells resorting on the “multi-stream heat exchanger” feature provided by the simulation tool HYSYS. In that work, the aim was to maximize the energy recovery from the process streams that renders the maximum achievable net global efficiency. In this paper, the aim is to synthesize and design the optimal heat exchangers network (i.e. determination of the process configuration and units sizes) while maintaining the net global efficiency of the whole system at its achievable value. Three modifications to the original SYNHEAT model developed in 1990 by Yee and Grossmann for synthesizing heat exchanger networks are proposed in this work aiming at a better problem description, and consequently searching for best problem solutions. First, a modification in computing the minimum approach temperature difference is proposed. Second, the called “operation line method” is coupled to the SYNHEAT model to built-up the network superstructure to be optimized. Finally, the SYNHEAT model’s hypothesis of constant cp value for modeling heat exchange between process streams is improved by considering enthalpy variable instead of temperature variable, which is convenient when latent heat is transferred. The model variables number involved in the heat exchanger network synthesis problems solved has been reduced to less than a half by applying the operation line method. The proposed methodology and modifications made are of general application and not just for the specific cases addressed in this work. [Copyright &y& Elsevier]

Abstract: The aim of this work is to analyze energetically the use of glycerin as the primary hydrogen source to operate a proton exchange membrane fuel cell. A glycerin processor system based on its steam reforming is described departing from a previous process model developed for ethanol processing. Since about 10% w/w of glycerin is produced as a byproduct when vegetable oils are converted into biodiesel, and due to the later is increasing its production abruptly, a large glycerin excess is expected to oversaturate the market. The reformed stream contains mainly H2 but also CO, CO2, H2O and CH4. As CO is a poison for PEM fuel cell type, a stream purification step is previously required. The purification subsystem consists of two water gas shift reactors and a CO preferential oxidation reactor to reduce the CO levels below 10ppm. The reforming process is governed by endothermic reactions, requiring thus energy to proceed. Depending on the system operation point, the energy requirements can be fulfilled by burning an extra glycerin amount (to be determined), which is the minimal that meets the energy requirements. In addition a self-sufficient operation region can be distinguished. In this context, the water/glycerin molar ratio, the glycerin steam reformer temperature, the system pressure, and the extra glycerin amount to be burned (if necessary) are the main decision variables subject to analysis. Process variables are calculated simultaneously, updating the composite curves at each iteration to obtain the best possible energy integration of the process. The highest net system efficiency value computed is 38.56% based on the lower heating value, and 34.71% based on the higher heating value. These efficiency values correspond to a pressure of 2atm, a water/glycerin molar ratio of 5, a glycerin steam reformer temperature of 953K, and an extra glycerin amount burned of 0.27molh−1. Based on the main process variables, suitable system operation zones are identified. As in practice, most PEM fuel cells operate at 3atm, optimal variable values obtained at this condition are also reported. Finally, some results and aspects on the system performance of both glycerin and ethanol processors operated at 3atm are compared and discussed. [Copyright &y& Elsevier]

A worldwide challenge is to provide medical resources required for COVID-19 detection. They must be effective tools for fast detection and diagnose of the virus using a large number of tests; besides, they should be low-cost developments. While a chest X-ray scan is a powerful candidate tool, if several tests are carried out, the images produced by the devices must be interpreted accurately and rapidly. COVID-19 induces longitudinal pulmonary parenchymal ground-glass and consolidates pulmonary opacity, in some cases with rounded morphology and peripheral lung distribution, which is very difficult to predict in an early stage. In this paper, we aim to develop a robust model to extract high-level features of COVID-19 from chest X-ray (CXR) images to help in rapid diagnosis. In specific, this paper proposes an optimization model for COVID-19 diagnosis based on adaptive Fuzzy C-means (AFCM) and improved Slime Mould Algorithm (SMA) based on Lévy distribution, namely AFCM-LSMA. The SMA optimizer is proposed to adapt weights in oscillation mode and to mimic the process of generating positive and negative feedback from the propagation wave to shape the optimum path for food connectivity. Lévy motion is used as a permutation to perform a local search and to adapt SMA optimizer (LSMA) by generating several solutions that are apart from current candidates. Furthermore, it permits the optimizer to escape from local minima, examine large search areas and reach optimal solutions in fewer iterations with high convergence speed. The FCM algorithm is used to segment pulmonary regions from CXR images and is adapted to reduce time and amount of computations using histogram of the image intensities during the clustering process. The performance of the proposed AFCM-LSMA has been validated on CXR images and compared with different conventional machine learning and deep learning techniques, meta-heuristics methods, and different chaotic maps. The accuracies achieved by the proposed model are around (ACC = 0.96, RMSE = 0.23, Prec. = 0.98, F1_score = 0.98, MCC = 0.79, and Kappa = 0.79). The experimental findings indicate that the proposed new method outperforms all other methods, which will be beneficial to the clinical practitioner for the early identification of infected COVID-19 patients. [ABSTRACT FROM AUTHOR]

Abstract: This work analyzes the CO-PrOx reactor design as a component of the CO clean-up system of the ethanol processor for H2 production applied to PEM fuel cells. The operating conditions of the processor require compact and lightweight pieces of equipment and efficient operation at different conditions. An egg-shell catalyst type of Pt/Al2O3 is considered. One-dimensional heterogeneous catalytic reactor model accounting for interfacial gradients is used to optimize the PrOx reactor. Different reactor components are added gradually to illustrate how the system dimensions and configuration change after optimization. The optimization problem determines the optimal reactor length, reactor diameter, catalyst particle diameter, inlet reactants temperature and insulating material thickness that minimize the total system volume. On these model-based results, the final reactor design is mainly governed by the presence of hemispherical heads (distributor and collector). Different inlet CO compositions and power generation targets are analyzed. According to the inlet CO level, more than one catalytic stage is required to meet design goals and fulfill process constraints. The model-based reactor optimization of the pseudo-adiabatic operation allows obtaining both designs for reducing volumes and optimal operating conditions that allows conventional reactor technology. Afterwards, simulation runs based on a rigorous one-dimensional heterogeneous catalytic reactor model accounting for intra-particle gradients are performed using data obtained from model-based optimization results. The aim of these simulations is to verify feasibility of the optimal design obtained from the proposed one-dimensional heterogeneous catalytic reactor model without intra-particle gradients, which is intended to approximate an egg-shell catalyst behavior. The present work reflects clearly the advantages of applying mathematical programming techniques to optimize both design and operation conditions of the PrOx reactor. [Copyright &y& Elsevier]

The PD-1/PD-L1 axis is a complex signaling pathway that has an important role in the immune system cells. Programmed cell death protein 1 (PD-1) acts as an immune checkpoint on the T lymphocytes, B lymphocytes, natural killer (NK), macrophages, dendritic cells (DCs), monocytes, and myeloid cells. Its ligand, the programmed cell death 1 ligand (PD-L1), is expressed in the surface of the antigen-presenting cells (APCs). The binding of both promotes the downregulation of the T cell response to ensure the activation to prevent the onset of chronic immune inflammation. This axis in the tumor microenvironment (TME) performs a crucial role in the tumor progression and the escape of the tumor by neutralizing the immune system, the engagement of PD-L1 with PD-1 in the T cell causes dysfunctions, neutralization, and exhaustion, providing the tumor mass production. This review will provide a comprehensive overview of the functions of the PD-1/PD-L1 system in immune function, cancer, and the potential therapeutic implications of the PD-1/PD-L1 pathway for cancer management. [ABSTRACT FROM AUTHOR]

Chronic venous disease (CVD) comprises a spectrum of morphofunctional disorders affecting the venous system, affecting approximately 1 in 3 women during gestation. Emerging evidence highlights diverse maternofetal implications stemming from CVD, particularly impacting the placenta. While systemic inflammation has been associated with pregnancy-related CVD, preliminary findings suggest a potential link between this condition and exacerbated inflammation in the placental tissue. Inflammasomes are major orchestrators of immune responses and inflammation in different organs and systems. Notwithstanding the relevance of inflammasomes, specifically the NLRP3 (nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3)- which has been demonstrated in the placentas of women with different obstetric complications, the precise involvement of this component in the placentas of women with CVD remains to be explored. This study employs immunohistochemistry and real-time PCR (RT-qPCR) to examine the gene and protein expression of key components in both canonical and non-canonical pathways of the NLRP3 inflammasome (NLRP3, ASC—apoptosis-associated speck-like protein containing a C-terminal caspase recruitment domain—caspase 1, caspase 5, caspase 8, and interleukin 1β) within the placental tissue of women affected by CVD. Our findings reveal a substantial upregulation of these components in CVD-affected placentas, indicating a potential pathophysiological role of the NLRP3 inflammasome in the development of this condition. Subsequent investigations should focus on assessing translational interventions addressing this dysregulation in affected patient populations. [ABSTRACT FROM AUTHOR]

Single-solution-based optimization algorithms are computationally cheap yet powerful methods that can be used on various optimization tasks at minimal processing expenses. However, there is a considerable shortage of research in this domain, resulting in only a handful of proposed algorithms over the last four decades. This study proposes the Prism Refraction Search (PRS), a novel, simple yet efficient, single-solution-based metaheuristic algorithm for single-objective real-parameter optimization. PRS is a physics-inspired algorithm modeled on a well-known optimization paradigm in ray optics arising from the refraction of light through a triangular prism. The key novelty lies in its scientifically sound background that is supported by the well-established laws of physical optics. The proposed algorithm is evaluated on several numerical objectives, including 23 classical benchmark functions, the CEC-2017 test suite, and five standard real-world engineering design problems. Further, the results are analyzed using standard statistical tests to prove their significance. Extensive experiments and comparisons with state-of-the-art metaheuristic algorithms in the literature justify the robustness and competitive performance of the PRS algorithm as a lightweight and efficient optimization strategy. [ABSTRACT FROM AUTHOR]

Emperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently developed and illustrates the emperor penguin's huddling behaviour. However, the original version of the EPO will fix issues that are continuing in fact but not discrete. The eight separate EPO variants have been provided in this article. Four transfer features, s-shaped and v-shaped, that are used in order to map the search space into a separate research space are considered in the proposed algorithm. The output of the proposed algorithm is validated using 25 standard benchmark functions. It also analyses the statistical sense of the proposed algorithm. Experimental findings and comparisons suggest that the proposed algorithm performs better than other algorithms. The solution also applies to the issue of feature selection. The findings reveal the supremacy of the binary emperor penguin optimization algorithm. [ABSTRACT FROM AUTHOR]

Simheuristics combine metaheuristics with simulation in order to solve the optimization problems with stochastic elements. This paper introduces the concept of fuzzy simheuristics, which extends the simheuristics approach by making use of fuzzy techniques, thus allowing us to tackle optimization problems under a more general scenario, which includes uncertainty elements of both stochastic and non-stochastic nature. After reviewing the related work, the paper discusses, in detail, how the optimization, simulation, and fuzzy components can be efficiently integrated. In order to illustrate the potential of fuzzy simheuristics, we consider the team orienteering problem (TOP) under an uncertainty scenario, and perform a series of computational experiments. The obtained results show that our proposed approach is not only able to generate competitive solutions for the deterministic version of the TOP, but, more importantly, it can effectively solve more realistic TOP versions, including stochastic and other uncertainty elements. [ABSTRACT FROM AUTHOR]

Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to provide good classification results. FS aims to reduce the dimensionality and improve the classification accuracy that is generally utilized with great importance in different fields such as pattern classification, data analysis, and data mining applications. The main problem is to find the best subset that contains the representative information of all the data. In order to overcome this problem, two binary variants of the whale optimization algorithm (WOA) are proposed, called bWOA-S and bWOA-V. They are used to decrease the complexity and increase the performance of a system by selecting significant features for classification purposes. The first bWOA-S version uses the Sigmoid transfer function to convert WOA values to binary ones, whereas the second bWOA-V version uses a hyperbolic tangent transfer function. Furthermore, the two binary variants introduced here were compared with three famous and well-known optimization algorithms in this domain, such as Particle Swarm Optimizer (PSO), three variants of binary ant lion (bALO1, bALO2, and bALO3), binary Dragonfly Algorithm (bDA) as well as the original WOA, over 24 benchmark datasets from the UCI repository. Eventually, a non-parametric test called Wilcoxon's rank-sum was carried out at 5% significance to prove the powerfulness and effectiveness of the two proposed algorithms when compared with other algorithms statistically. The qualitative and quantitative results showed that the two introduced variants in the FS domain are able to minimize the selected feature number as well as maximize the accuracy of the classification within an appropriate time. [ABSTRACT FROM AUTHOR]

The techniques of halftoning are widely used in marketing because they reduce the cost of impression and maintain the quality of graphics. Halftoning converts a digital image into a binary image conformed by dots. The output of the halftoning contains less visual information; a possible benefit of this task is the reduction of ink when graphics are printed. The human eye is not able to detect the absence of information, but the printed image stills have good quality. The most used method for halftoning is called Floyd-Steinberger, and it defines a specific matrix for the halftoning conversion. However, most of the proposed techniques in halftoning use predefined kernels that do not permit adaptation to different images. This article introduces the use of the harmony search algorithm (HSA) for halftoning. The HSA is a popular evolutionary algorithm inspired by the musical improvisation. The different operators of the HSA permit an efficient exploration of the search space. The HSA is applied to find the best configuration of the kernel in halftoning; meanwhile, as an objective function, the use of the structural similarity index (SSIM) is proposed. A set of rules are also introduced to reduce the regular patterns that could be created by non-appropriate kernels. The SSIM is used due to the fact that it is a perception model used as a metric that permits comparing images to interpret the differences between them numerically. The aim of combining the HSA with the SSIM for halftoning is to generate an adaptive method that permits estimating the best kernel for each image based on its intrinsic attributes. The graphical quality of the proposed algorithm has been compared with classical halftoning methodologies. Experimental results and comparisons provide evidence regarding the quality of the images obtained by the proposed optimization-based approach. In this context, classical algorithms have a lower graphical quality in comparison with our proposal. The results have been validated by a statistical analysis based on independent experiments over the set of benchmark images by using the mean and standard deviation. [ABSTRACT FROM AUTHOR]

Identifying the defects in apples is commonly done with visual examination techniques. However, it is a slow and laborious process. Image processing techniques have begun to be used to help and make the diagnosis of fruit diseases more efficient. In image processing systems, the segmentation of regions in the scenes is a crucial step. Specifically for images from apples, disease segmentation is a complicated task due to the different elements that affect the acquisition of the images. In addition, apple diseases also have features that need to be segmented. In this work, an efficient approach that uses the Gaining-sharing Knowledge-based (GSK) algorithm is proposed to optimize the minimum cross-entropy thresholding (MCET) for the segmentation of apple images highlighting the diseases defects. The proposed MCET-GSK has been tested for experimental purposes over different images and compared with various metaheuristics. The experiments were conducted to provide evidence of the GSK's optimization capabilities by performing the Wilcoxon test and applying a set of metrics to verify the quality of the segmented images. The experimental results validate the performance of the MCET-GSK in the segmentation of apple images by adequately separating the regions with damage produced by a disease. The quality of the segmentation is superior compared with other similar approaches. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming due to the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASA R L) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The implementation of the HH improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition. • A new methodology for solving high-dimensional electric vehicle routing problems. • A HH based on hybridization of self-adaptive SA and RL treating it as a MAB problem. • State-of-the-art results for the CEVRP benchmark proposed for the IEEE WCCI2020. [ABSTRACT FROM AUTHOR]

Several optimization-based population search methods have been proposed; they use various operators that permit exploring the search space. These methods typically suffer from local search (LS) problems and are unbalanced between exploration and exploitation. Consequently, recent researchers sought to modify the algorithms to avoid search problems using local search techniques to intensify the exploitation when is necessary. This paper proposes a novel single-based local search optimization algorithm called the non-monopolize search (NO). The NO is a single-solution metaphor-free algorithm, and its operators are designed based to explore and exploit along the iterative process. The NO works only with a candidate solution, and the operators modify the dimension to move the current solution along the search space. The NO is an effective LS method that combines the benefits of exploration with exploitation. Different from other LS, the NO can escape from suboptimal solutions thanks to the randomness incorporated into its operators. This is the main advantage of the NO. Experiments are conducted on standard benchmark functions to validate the performance of the proposed non-monopolize search optimization technique. The results are compared with other well-known methods, and the proposed NO got better results. Moreover, the proposed NO can be considered a powerful alternative to improve the optimization algorithms' performance and help avoid local search problems. Source codes of NO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/156154-the-non-monopolize-search-no. [ABSTRACT FROM AUTHOR]

This study assesses the optimal levelized cost of hydrogen (LCOH) production from wind farms. The study investigates 84 locations in the south of Argentina with nodes spaced 100 km apart, as well as one location each in Chile, the USA, Spain, Japan, and Australia. A model is presented to optimize the design of the wind farm, electrolyzer plant, compressor station, and storage capacity (gaseous 160 bar), with different hydrogen demands (hydrogen dispatch constraints) and minimizing the LCOH. Hourly data for wind resources from NASA is used to define a characteristic year. The model involves energy and mass balances in hourly basis for the whole year. Sequential solution procedures with heuristic rules are implemented. The optimal costs and their relation to the main variables, as well as production and storage, are analyzed. By imposing dispatch constraints, increase in levelized cost ranges from 25 % to 40 %, and decrease in annual hydrogen production ranges from 5 % to 30 %. Best levelized cost without considering electric transport between wind farm and electrolyzer plant is 2700 USD/t. • Attain the optimal hydrogen cost from wind energy. • Analize 84 sites in a 100 km × 100 km grid covering the Argentina's south region. • Identify the representative year (2011–2020) for wind resources. • Determine the site-specific optimal wind farm-to-electrolyzer power ratio. • Investigate the influence of dispatch constraints on the optimal LCOH. [ABSTRACT FROM AUTHOR]

An Improved binary Non‐Linear Convergent Bi‐phase Mutated Grey Wolf Optimizer (IbGWO) is proposed for solving feature selection problems with two main goals reducing irrelevant features and maximizing accuracy. We used stratified k‐fold cross‐validation that performs stratified sampling on the data to avoid overfitting problems. The fitness function used in the proposed algorithm allows choosing the solution with the minimum number of features if more than one feature has the same highest accuracy. When stratified cross‐validation is performed, the split datasets contain the same share of the feature of interest as the actual dataset. During stratified sampling, the cross‐validation result minimizes the generalization error to a considerable extent, with a smaller variance. Feature selection could be seen as an optimization problem that efficiently removes irrelevant data from high‐dimensional data to reduce computation time and improve learning accuracy. This paper proposes an improved Non‐Linear Convergent Bi‐Phase Mutated Binary Grey Wolf Optimizer (IbGWO) algorithm for feature selection. The bi‐phase mutation enhances the rate of exploitation of GWO, where the first mutation phase minimizes the number of features and the second phase adds more informative features for accurate feature selection. A non‐linear tangent trigonometric function is used for convergence to generalize better while handling heterogeneous data. To accelerate the global convergence speed, an inertia weight is added to control the position updating of the grey wolves. Feature‐weighted K‐Nearest Neighbor is used to enhance classification accuracy, where only relevant features are used for feature selection. Experimental results confirm that IbGWO outperforms other algorithms in terms of average accuracy of 0.8716, average number of chosen features of 6.13, average fitness of 0.1717, and average standard deviation of 0.0072 tested on different datasets and in terms of statistical analysis. IbGWO is also benchmarked using unimodal, multimodal, and IEEE CEC 2019 functions, where it outperforms other algorithms in most cases. Three classical engineering design problems are also solved using IbGWO, which significantly outperforms other algorithms. Moreover, the overtaking percentage of the proposed algorithm is 69.56%. [ABSTRACT FROM AUTHOR]

In the original publication, Algorithm 1 and Algorithm 2 are incorrectly published with the same content. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced sitting–sizing scheme for shunt capacitors (4SCs) in a radial distribution system (RDS) based on an improved atom search optimization (IASO) algorithm is proposed. IASO emulates the model of atomic motion in nature based on interaction forces among atoms. The main goal of the 4SCs problem is to reduce the line losses and minimize the capacitor installation cost by searching for the optimal location and sizing of the capacitors. This leads to improvements in the voltage profile and reliability of the system. The IASO algorithm is introduced to achieve the optimal sitting and sizing of capacitors for RDSs. The proposed IASO algorithm is benchmarked and validated on different radial systems, including the IEEE 33-bus, IEEE 34-bus, IEEE 65-bus, IEEE 85-bus and Marsa Matrouh networks, to demonstrate its performance in real-world applications. The results obtained by the proposed IASO algorithm are compared with the standard ASO, PSO, SCA, GWO and SSA algorithms. Furthermore, the significance of the obtained results is confirmed by performing a nonparametric statistical test, i.e., the Wilcoxon's rank-sum at the 5% significance level. The comprehensive results demonstrate that the results obtained by the proposed IASO algorithm denominate the results obtained by the other algorithms and that IASO minimizes the operating costs while achieving better voltage profiles. [ABSTRACT FROM AUTHOR]

The natural aggregation algorithm (NAA) is a new efficient population-based optimizer. The NAA has a competent performance when compared to other well-established optimizers. However, a problem of concern is NAA lack of exploitation in its local search. In this article, we propose an improved version of NAA. The modifications made are: hypercubes with displacement and shrink mechanism applied in each shelter, we designed a new movement operator to search inside the hypercubes, an improved readjustment of the algorithm's parameters and "leave shelter" formula of NAA, to better mimic the aggregation behavior. To prove the effectiveness of the modified hypercube natural aggregation algorithm (HYNAA), we compared with classics optimizers, such as PSO, DE and ABC, state of the art, such as CMA-ES, MSA and NAA himself with a benchmark of 28 functions. The said functions consist of five unimodal, 19 multimodal and four hybrids, and we compared them on 30, 50 and 100 dimensions. We also made extra comparisons against NAA in 500 and 1000 dimensions to contrast the ability of the hypercubes to reduce the dimensional complexity. Finally, we tested two trajectory optimization problems. Experimental results and statistical tests demonstrate that the performance of HYNAA is significantly better than that of other optimizers. [ABSTRACT FROM AUTHOR]