A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems

Generic multidimensional economic environmental operation of power systems using equilibrium optimization algorithm

The economic emission load dispatch (EELD) problem is one of the main challenges to power system operators due to the complexity of the interconnected power systems and the non-linear characteristics of the objective functions (OFs). Therefore, the EELD problem has attracted significant attention in the electric power system because it has important objectives. Thus, this paper proposes the equilibrium optimization algorithm (EOA) to solve the EELD problem in electrical power systems by minimizing the total fuel cost and emissions, considering system and operational constraints. The OFs are optimized with and without considering valve point effects (VPE) and transmission system loss. The multi-OF, which aims to optimize these objectives simultaneously, is considered. In the proposed EOA, agents are particles and concentrations that express the solution and position, respectively. The proposed EOA is evaluated and tested on different-sized standard test systems having 10, 20, 40, and 80 generation units through several case studies. The numerical results obtained by the proposed EOA are compared with other optimization techniques such as grey wolf optimization, particle swarm optimization (PSO), differential evolution algorithm, and other optimization techniques in the literature. To show the reliability of the proposed algorithm for solving the considered OFs on a large-scale power system with and without considering different practical constraints such as VPE, ramp-rate limits (RRL), and prohibited operating zones (POZs) of generating units, the proposed EOA is evaluated and tested on the 140-unit test system. Also, the proposed multi-objective EOA (MOEOA) successfully acquires the Pareto optimal front to find the best compromise solution between the considered OFs. Also, the statistical analysis and the Wilcoxon signed rank test between the EOA and other optimization techniques for solving the EELD problem are performed. From numerical results, the total fuel cost obtained without considering VPE using the proposed EOA is reduced by 0.1414%, 0.1295%, 0.6864%, 5.8441% than the results of PSO, with maximum savings of 150 $/hr, 78 $/hr, 820 $/hr, and 14,730 $/hr for 10, 20, 40, and 80 units, respectively. The total fuel cost considering VPE is reduced by 0.0753%, 0.2536%, 2.8891%, and 3.6186% than the base case with maximum savings of 80 $/hr, 158 $/hr, 3610 $/hr, 9230 $/hr for 10, 20, 40, and 80 units, respectively. The total emission is reduced by 1.7483%, 12.8673%, and 7.5948% from the base case for 10, 40, and 80 units, respectively. For the 140-unit test system, the total fuel cost without and with considering VPE, RRL, and POZs is reduced by 6.4203% and 7.2394%, than the results of PSO with maximum savings of 107,200 $/hr and 126,400 $/hr. The total emission is reduced by 2.5688% from the base case. The comparative studies show the superiority of the EOA for the economic/environmental operation of the power system by solving the EELD problem with more accuracy and efficiency, especially as the system size increases.

Research on plasma arc flame length detection technology based on region of interest

With the rapid advancement of metal 3D printing technology, there is a growing demand for spherical metal powder as a primary material for 3D printing. The process technology that ensures the production of high-quality spherical metal powder has become a focal area of research for numerous enterprises and research institutions globally. In the conventional plasma rotating electrode method for powder production, the feed speed of the servo feeding mechanism is manually predetermined, leading to potential variations in the distance between the end face of the metal rod and the plasma gun that generates the plasma arc. Such inconsistency can compromise the quality of the metal powder produced and pose safety hazards if the gap between the metal rod and the plasma gun is too narrow. To address these issues, this study presents a novel plasma arc length detection system based on the concept of the region of interest. The proposed system leverages image processing technology for efficiently detecting the plasma arc length. By incorporating image detection within the region of interest alongside an arc length correction function, the system enhances real-time performance and detection precision. Additionally, real-time monitoring of the detection site is enabled through KingView. Experimental findings indicate that the image target area post plasma arc detection exhibits well-defined edges, clear brightness, and minimal noise, thereby meeting the prerequisites for subsequent image processing and monitoring tasks. The corrected plasma arc length averages around 40mm, with a detection error of less than 1mm when compared to the desired controlled plasma arc length. Moreover, the length variation remains relatively stable, thus fulfilling the measurement criteria. Over time, the detected plasma arc length exhibits negligible fluctuations, suggesting consistent proximity between the plasma gun and the end face of the metal rod during the melting process. The controller can dynamically control the feed speed of the servo feeding mechanism according to the detected plasma arc length, ensuring a constant distance between the plasma arc and the end face of the metal rod throughout the powder production process, thus aligning with practical industrial requirements.

A multi-level thresholding image segmentation algorithm based on equilibrium optimizer

Multi-level thresholding for image segmentation is one of the key techniques in image processing. Although numerous methods have been introduced, it remains challenging to achieve stable and satisfactory thresholds when segmenting images with various unknown properties. This paper proposes an equilibrium optimizer algorithm to find the optimal multi-level thresholds for grayscale images. The proposed algorithm AEO (advanced equilibrium optimizer) uses two sub-populations to balance exploration and exploitation during the multi-level threshold search process. Two mutation schemes are proposed for the sub-populations to prevent them from being trapped in local optima. AEO offers a repair function to avoid generating duplicate thresholds. The performance of AEO is evaluated on multiple benchmark images. Experimental results demonstrate that AEO has an outstanding ability for multi-level threshold image segmentation in terms of cross-entropy, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM).

Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation

Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels.

Integrated machine learning for modeling bearing capacity of shallow foundations

Analyzing the stability of footings is a significant step in civil/geotechnical engineering projects. In this work, two novel predictive tools are suggested based on an artificial neural network (ANN) to analyze the bearing capacity of a footing installed on a two-layered soil mass. To this end, backtracking search algorithm (BSA) and equilibrium optimizer (EO) are employed to train the ANN for approximating the stability value (SV) of the system. After executing a set of finite element analyses, the settlement values lower/higher than 5 cm are considered to indicate the stability/failure of the system. The results demonstrated the efficiency of these algorithms in fulfilling the assigned task. In detail, the training error of the ANN (in terms of root mean square error-RMSE)) dropped from 0.3585 to 0.3165 (11.72%) and 0.2959 (17.46%) by applying the BSA and EO, respectively. Moreover, the prediction accuracy of the ANN climbed from 93.7 to 94.3% and 94.1% (in terms of area under the receiving operating characteristics curve-AUROC). A comparison between the elite complexities of these algorithms showed that the EO enjoys a larger accuracy, while BSA is a more time-effective optimizer. Lastly, an explicit mathematical formula is derived from the EO-ANN model to be conveniently used in predicting the SV.

Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques

Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in the global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes the segmentation threshold combination by accelerating convergence and diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination of optimal thresholds for final segmentation. The efficacy of DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM), and compared with six contemporary swarm intelligence algorithms. The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm's practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.

CNN-IKOA: convolutional neural network with improved Kepler optimization algorithm for image segmentation: experimental validation and numerical exploration

Chest diseases, especially COVID-19, have quickly spread throughout the world and caused many deaths. Finding a rapid and accurate diagnostic tool was indispensable to combating these diseases. Therefore, scientists have thought of combining chest X-ray (CXR) images with deep learning techniques to rapidly detect people infected with COVID-19 or any other chest disease. Image segmentation as a preprocessing step has an essential role in improving the performance of these deep learning techniques, as it could separate the most relevant features to better train these techniques. Therefore, several approaches were proposed to tackle the image segmentation problem accurately. Among these methods, the multilevel thresholding-based image segmentation methods won significant interest due to their simplicity, accuracy, and relatively low storage requirements. However, with increasing threshold levels, the traditional methods have failed to achieve accurate segmented features in a reasonable amount of time. Therefore, researchers have recently used metaheuristic algorithms to tackle this problem, but the existing algorithms still suffer from slow convergence speed and stagnation into local minima as the number of threshold levels increases. Therefore, this study presents an alternative image segmentation technique based on an enhanced version of the Kepler optimization algorithm (KOA), namely IKOA, to better segment the CXR images at small, medium, and high threshold levels. Ten CXR images are used to assess the performance of IKOA at ten threshold levels (T-5, T-7, T-8, T-10, T-12, T-15, T-18, T-20, T-25, and T-30). To observe its effectiveness, it is compared to several metaheuristic algorithms in terms of several performance indicators. The experimental outcomes disclose the superiority of IKOA over all the compared algorithms. Furthermore, the IKOA-based segmented CXR images at eight different threshold levels are used to train a newly proposed CNN model called CNN-IKOA to find out the effectiveness of the segmentation step. Five performance indicators, namely overall accuracy, precision, recall, F1-score, and specificity, are used to disclose the CNN-IKOA’s effectiveness. CNN-IKOA, according to the experimental outcomes, could achieve outstanding outcomes for the images segmented at T-12, where it could reach 94.88% for overall accuracy, 96.57% for specificity, 95.40% for precision, and 95.40% for recall.

A multi-branched semantic segmentation network based on twisted information sharing pattern for medical images

BACKGROUND

Semantic segmentation plays an indispensable role in clinical diagnosis support, intelligent surgical assistance, personalized treatment planning, and drug development, making it a core area of research in smart healthcare. However, the main challenge in medical image semantic segmentation lies in the accuracy bottleneck, primarily due to the low interactivity of feature information and the lack of deep exploration of local features during feature fusion.

METHODS

To address this issue, a novel approach called Twisted Information-sharing Pattern for Multi-branched Network (TP-MNet) has been proposed. This architecture facilitates the mutual transfer of features among neighboring branches at the next level, breaking the barrier of semantic isolation and achieving the goal of semantic fusion. Additionally, performing a secondary feature mining during the transfer process effectively enhances the detection accuracy. Building upon the Twisted Pattern transmission in the encoding and decoding stages, enhanced and refined modules for feature fusion have been developed. These modules aim to capture key features of lesions by acquiring contextual semantic information in a broader context.

RESULTS

The experiments extensively and objectively validated the TP-MNet on 5 medical datasets and compared it with 21 other semantic segmentation models using 7 metrics. Through metric analysis, image comparisons, process examination, and ablation tests, the superiority of TP-MNet was convincingly demonstrated. Additionally, further investigations were conducted to explore the limitations of TP-MNet, thereby clarifying the practical utility of the Twisted Information-sharing Pattern.

CONCLUSIONS

TP-MNet adopts the Twisted Information-sharing Pattern, leading to a substantial improvement in the semantic fusion effect and directly contributing to enhanced segmentation performance on medical images. Additionally, this semantic broadcasting mode not only underscores the importance of semantic fusion but also highlights a pivotal direction for the advancement of multi-branched architectures.

Multilevel Threshold Segmentation of Skin Lesions in Color Images Using Coronavirus Optimization Algorithm

Skin Cancer (SC) is among the most hazardous due to its high mortality rate. Therefore, early detection of this disease would be very helpful in the treatment process. Multilevel Thresholding (MLT) is widely used for extracting regions of interest from medical images. Therefore, this paper utilizes the recent Coronavirus Disease Optimization Algorithm (COVIDOA) to address the MLT issue of SC images utilizing the hybridization of Otsu, Kapur, and Tsallis as fitness functions. Various SC images are utilized to validate the performance of the proposed algorithm. The proposed algorithm is compared to the following five meta-heuristic algorithms: Arithmetic Optimization Algorithm (AOA), Sine Cosine Algorithm (SCA), Reptile Search Algorithm (RSA), Flower Pollination Algorithm (FPA), Seagull Optimization Algorithm (SOA), and Artificial Gorilla Troops Optimizer (GTO) to prove its superiority. The performance of all algorithms is evaluated using a variety of measures, such as Mean Square Error (MSE), Peak Signal-To-Noise Ratio (PSNR), Feature Similarity Index Metric (FSIM), and Normalized Correlation Coefficient (NCC). The results of the experiments prove that the proposed algorithm surpasses several competing algorithms in terms of MSE, PSNR, FSIM, and NCC segmentation metrics and successfully solves the segmentation issue.

Research on tire crack detection using image deep learning method

Driving can understand the importance of tire tread depth and air pressure, but most people are unaware of the safety risks of tire oxidation. Drivers must maintain vehicle tire quality to ensure performance, efficiency, and safety. In this study, a deep learning tire defect detection method was designed. This paper improves the traditional ShuffleNet and proposes an improved ShuffleNet method for tire image detection. The research results are compared with the five methods of GoogLeNet, traditional ShuffleNet, VGGNet, ResNet and improved ShuffleNet through tire database verification. The experiment found that the detection rate of tire debris defects was 94.7%. Tire defects can be effectively detected, which proves the robustness and effectiveness of the improved ShuffleNet, enabling drivers and tire manufacturers to save labor costs and greatly reduce tire defect detection time.

Urban scene segmentation model based on multi-scale shuffle features

The monitoring of urban land categories is crucial for effective land resource management and urban planning. To address challenges such as uneven parcel distribution, difficulty in feature extraction and loss of image information in urban remote sensing images, this study proposes a multi-scale feature shuffle urban scene segmentation model. The model utilizes a deep convolutional encoder-decoder network with BlurPool instead of MaxPool to compensate for missing translation invariance. GSSConv and SE module are introduced to enhance information interaction and filter redundant information, minimizing category misclassification caused by similar feature distributions. To address unclear boundary information during feature extraction, the model applies multi-scale attention to aggregate context information for better integration of boundary and global information. Experiments conducted on the BDCI2017 public dataset show that the proposed model outperforms several established segmentation networks in OA, mIoU, mRecall, P and Dice with scores of 83.1%, 71.0%, 82.7%, 82.7% and 82.5%, respectively. By effectively improving the completeness and accuracy of urban scene segmentation, this study provides a better understanding of urban development and offers suggestions for future planning.

A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection

Feature selection (FS) is a popular data pre-processing technique in machine learning to extract the optimal features to maintain or increase the classification accuracy of the dataset, which is a combinatorial optimization problem, requiring a powerful optimizer to obtain the optimum subset. The equilibrium optimizer (EO) is a recent physical-based metaheuristic algorithm with good performance for various optimization problems, but it may encounter premature or the local convergence in feature selection. This work presents a self-adaptive quantum EO with artificial bee colony for feature selection, named SQEOABC. In the proposed algorithm, the quantum theory and the self-adaptive mechanism are employed into the updating rule of EO to enhance convergence, and the updating mechanism from the artificial bee colony is also incorporated into EO to achieve appropriate FS solutions. In the experiments, 25 benchmark datasets from the UCI repository are investigated to verify SQEOABC, which is compared with several state-of-the-art metaheuristic algorithms and the variants of EO. The statistical results of fitness values and accuracy demonstrate that SQEOABC has better performance than the compared algorithms and the variants of EO. Finally, a real-world FS problem from COVID-19 illustrates the effectiveness and superiority of SQEOABC.

Velocity pausing particle swarm optimization: a novel variant for global optimization

Particle swarm optimization (PSO) is one of the most well-regard metaheuristics with remarkable performance when solving diverse optimization problems. However, PSO faces two main problems that degrade its performance: slow convergence and local optima entrapment. In addition, the performance of this algorithm substantially degrades on high-dimensional problems. In the classical PSO, particles can move in each iteration with either slower or faster speed. This work proposes a novel idea called velocity pausing where particles in the proposed velocity pausing PSO (VPPSO) variant are supported by a third movement option that allows them to move with the same velocity as they did in the previous iteration. As a result, VPPSO has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, VPPSO modifies the first term of the PSO velocity equation. In addition, the population of VPPSO is divided into two swarms to maintain diversity. The performance of VPPSO is validated on forty three benchmark functions and four real-world engineering problems. According to the Wilcoxon rank-sum and Friedman tests, VPPSO can significantly outperform seven prominent algorithms on most of the tested functions on both low- and high-dimensional cases. Due to its superior performance in solving complex high-dimensional problems, VPPSO can be applied to solve diverse real-world optimization problems. Moreover, the velocity pausing concept can be easily integrated with new or existing metaheuristic algorithms to enhance their performances. The Matlab code of VPPSO is available at: https://uk.mathworks.com/matlabcentral/fileexchange/119633-vppso.

Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

The intricacy of the real-world numerical optimization tribulations has full-fledged and diversely amplified necessitating proficient yet ingenious optimization algorithms. In the domain wherein the classical approaches fall short, the predicament resolving nature-inspired optimization algorithms (NIOA) tend to hit upon an excellent solution to unbendable optimization problems consuming sensible computation time. Nevertheless, in the last few years approaches anchored in nonlinear physics have been anticipated, announced, and flourished. The process based on non-linear physics modeled in the form of optimization algorithms and as a subset of NIOA, in countless cases, has successfully surpassed the existing optimization methods with their effectual exploration knack thus formulating utterly fresh search practices. Archimedes Optimization Algorithm (AOA) is one of the recent and most promising physics optimization algorithms that use meta-heuristics phenomenon to solve real-world problems by either maximizing or minimizing a variety of measurable variables such as performance, profit, and quality. In this paper, Archimedes Optimization Algorithm (AOA) has been discussed in great detail, and also its performance was examined for Multi-Level Thresholding (MLT) based image segmentation domain by considering t-entropy and Tsallis entropy as objective functions. The experimental results showed that among recent Physics Inspired Optimization Algorithms (PIOA), the Archimedes Optimization Algorithm (AOA) produces very promising outcomes with Tsallis entropy rather than with t-entropy in both color standard images and medical pathology images.

Multilevel segmentation of 2D and volumetric medical images using hybrid Coronavirus Optimization Algorithm

Medical image segmentation is a crucial step in Computer-Aided Diagnosis systems, where accurate segmentation is vital for perfect disease diagnoses. This paper proposes a multilevel thresholding technique for 2D and 3D medical image segmentation using Otsu and Kapur's entropy methods as fitness functions to determine the optimum threshold values. The proposed algorithm applies the hybridization concept between the recent Coronavirus Optimization Algorithm (COVIDOA) and Harris Hawks Optimization Algorithm (HHOA) to benefit from both algorithms' strengths and overcome their limitations. The improved performance of the proposed algorithm over COVIDOA and HHOA algorithms is demonstrated by solving 5 test problems from IEEE CEC 2019 benchmark problems. Medical image segmentation is tested using two groups of images, including 2D medical images and volumetric (3D) medical images, to demonstrate its superior performance. The utilized test images are from different modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and X-ray images. The proposed algorithm is compared with seven well-known metaheuristic algorithms, where the performance is evaluated using four different metrics, including the best fitness values, Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Correlation Coefficient (NCC). The experimental results demonstrate the superior performance of the proposed algorithm in terms of convergence to the global optimum and making a good balance between exploration and exploitation properties. Moreover, the quality of the segmented images using the proposed algorithm at different threshold levels is better than the other methods according to PSNR, SSIM, and NCC values. Additionally, the Wilcoxon rank-sum test is conducted to prove the statistical significance of the proposed algorithm.

Graphical Image Region Extraction with K-Means Clustering and Watershed

With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances achieved in the last decades, image segmentation remains a challenging problem, particularly, the segmenting of color images due to the diverse inhomogeneities of color, textures and shapes present in the descriptive features of the images. In trademark graphic images segmentation, beyond these difficulties, we must also take into account the high noise and low resolution, which are often present. Trademark graphic images can also be very heterogeneous with regard to the elements that make them up, which can be overlapping and with varying lighting conditions. Due to the immense variation encountered in corporate logos and trademark graphic images, it is often difficult to select a single method for extracting relevant image regions in a way that produces satisfactory results. Many of the hybrid approaches that integrate the Watershed and K-Means algorithms involve processing very high quality and visually similar images, such as medical images, meaning that either approach can be tweaked to work on images that follow a certain pattern. Trademark images are totally different from each other and are usually fully colored. Our system solves this difficulty given it is a generalized implementation designed to work in most scenarios, through the use of customizable parameters and completely unbiased for an image type. In this paper, we propose a hybrid approach to Image Region Extraction that focuses on automated region proposal and segmentation techniques. In particular, we analyze popular techniques such as K-Means Clustering and Watershedding and their effectiveness when deployed in a hybrid environment to be applied to a highly variable dataset. The proposed system consists of a multi-stage algorithm that takes as input an RGB image and produces multiple outputs, corresponding to the extracted regions. After preprocessing steps, a K-Means function with random initial centroids and a user-defined value for k is executed over the RGB image, generating a gray-scale segmented image, to which a threshold method is applied to generate a binary mask, containing the necessary information to generate a distance map. Then, the Watershed function is performed over the distance map, using the markers defined by the Connected Component Analysis function that labels regions on 8-way pixel connectivity, ensuring that all regions are correctly found. Finally, individual objects are labelled for extraction through a contour method, based on border following. The achieved results show adequate region extraction capabilities when processing graphical images from different datasets, where the system correctly distinguishes the most relevant visual elements of images with minimal tweaking.

A new fusion of whale optimizer algorithm with Kapur's entropy for multi-threshold image segmentation: analysis and validations

The separation of an object from other objects or the background by selecting the optimal threshold values remains a challenge in the field of image segmentation. Threshold segmentation is one of the most popular image segmentation techniques. The traditional methods for finding the optimum threshold are computationally expensive, tedious, and may be inaccurate. Hence, this paper proposes an Improved Whale Optimization Algorithm (IWOA) based on Kapur’s entropy for solving multi-threshold segmentation of the gray level image. Also, IWOA supports its performance using linearly convergence increasing and local minima avoidance technique (LCMA), and ranking-based updating method (RUM). LCMA technique accelerates the convergence speed of the solutions toward the optimal solution and tries to avoid the local minima problem that may fall within the optimization process. To do that, it updates randomly the positions of the worst solutions to be near to the best solution and at the same time randomly within the search space according to a certain probability to avoid stuck into local minima. Because of the randomization process used in LCMA for updating the solutions toward the best solutions, a huge number of the solutions around the best are skipped. Therefore, the RUM is used to replace the unbeneficial solution with a novel updating scheme to cover this problem. We compare IWOA with another seven algorithms using a set of well-known test images. We use several performance measures, such as fitness values, Peak Signal to Noise Ratio, Structured Similarity Index Metric, Standard Deviation, and CPU time.

Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images

Clinical Decision Support Systems (CDSS) provide an efficient way to diagnose the presence of diseases such as breast cancer using ultrasound images (USIs). Globally, breast cancer is one of the major causes of increased mortality rates among women. Computer-Aided Diagnosis (CAD) models are widely employed in the detection and classification of tumors in USIs. The CAD systems are designed in such a way that they provide recommendations to help radiologists in diagnosing breast tumors and, furthermore, in disease prognosis. The accuracy of the classification process is decided by the quality of images and the radiologist's experience. The design of Deep Learning (DL) models is found to be effective in the classification of breast cancer. In the current study, an Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification (EDLCDS-BCDC) technique was developed using USIs. The proposed EDLCDS-BCDC technique was intended to identify the existence of breast cancer using USIs. In this technique, USIs initially undergo pre-processing through two stages, namely wiener filtering and contrast enhancement. Furthermore, Chaotic Krill Herd Algorithm (CKHA) is applied with Kapur's entropy (KE) for the image segmentation process. In addition, an ensemble of three deep learning models, VGG-16, VGG-19, and SqueezeNet, is used for feature extraction. Finally, Cat Swarm Optimization (CSO) with the Multilayer Perceptron (MLP) model is utilized to classify the images based on whether breast cancer exists or not. A wide range of simulations were carried out on benchmark databases and the extensive results highlight the better outcomes of the proposed EDLCDS-BCDC technique over recent methods.

Renewable Energy Micro-Grid Interfacing: Economic and Environmental Issues

This paper presents a study on the technical, economic, and environmental aspects of renewable energy resources-based distributed generation units (DGs). These units are connected to the medium-voltage network to create a new structure called a microgrid (MG). Renewable energies, especially wind and solar, are the most important generation units among DGs. The stochastic behavior of renewable resources increases the need to find the optimum operation of the MG. The optimal operation of a typical MG aims to simultaneously minimize the operational costs and the accompanied emission pollutants over a daily scheduling horizon. Several renewable DGs are investigated in the MG, consisting of biomass generators (BGs), wind turbines (WTs), and photovoltaics (PV). For the proposed operating strategy of the MG, a recent equilibrium optimization (EO) technique is developed and is inspired by the mass balance models for a control volume that are used to estimate their dynamic and equilibrium states. The uncertainties of wind speed and solar irradiation are considered via the Weibull and Beta-probability density functions (PDF) with different states of mean and standard deviation for each hour, respectively. Based on the developed EO, the hourly output powers of the PV, WT, and BGs are optimized, as are the associated power factors of the BGs. The proposed MG operating strategy based on the developed EO is tested on the IEEE 33-bus system and the practical large-scale 141-bus system of AES-Venezuela in the metropolitan area of Caracas. The simulation results demonstrate the significant benefits of the optimal operation of a typical MG using the developed EO by minimizing the operational costs and emissions while preserving the penetration level of the DGs by 60%. Additionally, the voltage profile of the MG operation for each hour is highly enhanced where the minimum voltage at each hour is corrected within the permissible limit of [0.95–1.05] Pu. Moreover, the active power losses per hour are greatly reduced.

Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution

In order to automatically recognize different kinds of objects from their backgrounds, a self-adaptive segmentation algorithm that can effectively extract the targets from various surroundings is of great importance. Image thresholding is widely adopted in this field because of its simplicity and high efficiency. The entropy-based and variance-based algorithms are two main kinds of image thresholding methods, and have been independently developed for different kinds of images over the years. In this paper, their advantages are combined and a new algorithm is proposed to deal with a more general scope of images, including the long-range correlations among the pixels that can be determined by a nonextensive parameter. In comparison with the other famous entropy-based and variance-based image thresholding algorithms, the new algorithm performs better in terms of correctness and robustness, as quantitatively demonstrated by four quality indices, ME, RAE, MHD, and PSNR. Furthermore, the whole process of the new algorithm has potential application in self-adaptive object recognition.

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019-2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation

Masi entropy is a popular criterion employed for identifying appropriate threshold values in image thresholding. However, with an increasing number of thresholds, the efficiency of Masi entropy-based multi-level thresholding algorithms becomes problematic. To overcome this, we propose a novel differential evolution (DE) algorithm as an effective population-based metaheuristic for Masi entropy-based multi-level image thresholding. Our ME-GDEAR algorithm benefits from a grouping strategy to enhance the efficacy of the algorithm for which a clustering algorithm is used to partition the current population. Then, an updating strategy is introduced to include the obtained clusters in the current population. We further improve the algorithm using attraction (towards the best individual) and repulsion (from random individuals) strategies. Extensive experiments on a set of benchmark images convincingly show ME-GDEAR to give excellent image thresholding performance, outperforming other metaheuristics in 37 out of 48 cases based on cost function evaluation, 26 of 48 cases based on feature similarity index, and 20 of 32 cases based on Dice similarity. The obtained results demonstrate that population-based metaheuristics can be successfully applied to entropy-based image thresholding and that strengthening both exploitation and exploration strategies, as performed in ME-GDEAR, is crucial for designing such an algorithm.

Differential evolution and particle swarm optimization against COVID-19

COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used.

A Novel Multi-Robot Task Allocation Model in Marine Plastics Cleaning Based on Replicator Dynamics

As marine plastic pollution threatens the marine ecosystem seriously, the government needs to find an effective way to clean marine plastics. Due to the advantages of easy operation and high efficiency, autonomous underwater vehicles (AUVs) have been applied to clean marine plastics. As for the large-scale marine environment, the marine plastic cleaning task needs to be accomplished through the collaborative work of multiple AUVs. Assigning the cleaning task to each AUV reasonably and effectively has an essential impact on improving cleaning efficiency. The coordination of AUVs is subjected to harsh communication conditions. Therefore, to release the dependence on the underwater communications among AUVs, proposing a reliable multi-robot task allocation (MRTA) model is necessary. Inspired by the evolutionary game theory, this paper proposes a novel multi-robot task allocation (MRTA) model based on replicator dynamics for marine plastic cleaning. This novel model not only satisfies the minimization of the cost function, but also reaches a relatively stable state of the task allocation. A novel optimization algorithm, equilibrium optimizer (EO), is adopted as the optimizer. The simulation results validate the correctness of the results achieved by EO and the applicability of the proposed model. At last, several valuable conclusions are obtained from the simulations on the three different assumed AUVs.

Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer

Recently, the use of diverse renewable energy resources has been intensively expanding due to their technical and environmental benefits. One of the important issues in the modeling and simulation of renewable energy resources is the extraction of the unknown parameters in photovoltaic models. In this regard, the parameters of three models of photovoltaic (PV) cells are extracted in this paper with a new optimization method called turbulent flow of water-based optimization (TFWO). The applications of the proposed TFWO algorithm for extracting the optimal values of the parameters for various PV models are implemented on the real data of a 55 mm diameter commercial R.T.C. France solar cell and experimental data of a KC200GT module. Further, an assessment study is employed to show the capability of the proposed TFWO algorithm compared with several recent optimization techniques such as the marine predators algorithm (MPA), equilibrium optimization (EO), and manta ray foraging optimization (MRFO). For a fair performance evaluation, the comparative study is carried out with the same dataset and the same computation burden for the different optimization algorithms. Statistical analysis is also used to analyze the performance of the proposed TFWO against the other optimization algorithms. The findings show a high closeness between the estimated power–voltage (P–V) and current–voltage (I–V) curves achieved by the proposed TFWO compared with the experimental data as well as the competitive optimization algorithms, thanks to the effectiveness of the developed TFWO solution mechanism.

Multi-Objective Energy Management of a Micro-Grid Considering Stochastic Nature of Load and Renewable Energy Resources

Optimal inclusion of a photovoltaic system and wind energy resources in electrical grids is a strenuous task due to the continuous variation of their output powers and stochastic nature. Thus, it is mandatory to consider the variations of the Renewable energy resources (RERs) for efficient energy management in the electric system. The aim of the paper is to solve the energy management of a micro-grid (MG) connected to the main power system considering the variations of load demand, photovoltaic (PV), and wind turbine (WT) under deterministic and probabilistic conditions. The energy management problem is solved using an efficient algorithm, namely equilibrium optimizer (EO), for a multi-objective function which includes cost minimization, voltage profile improvement, and voltage stability improvement. The simulation results reveal that the optimal installation of a grid-connected PV unit and WT can considerably reduce the total cost and enhance system performance. In addition to that, EO is superior to both whale optimization algorithm (WOA) and sine cosine algorithm (SCA) in terms of the reported objective function.

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO's initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu's method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu's method for all the used metrics.