Search

In today's dynamic business landscape, organizations heavily rely on cloud computing to leverage the power of virtualization and resource sharing. Service composition plays a vital role in cloud computing, combining multiple cloud services to fulfill complex user requests. Service composition in cloud computing presents several challenges. These include service heterogeneity, dynamic service availability, QoS (Quality of Service) constraints, and scalability issues. Traditional approaches often struggle to handle these challenges efficiently, leading to suboptimal resource utilization and poor service performance. This work presents a fuzzy-based strategy for composing cloud services to overcome these obstacles. The fact that service composition is NP-hard has prompted the use of a range of metaheuristic algorithms in numerous papers. Therefore, Particle Swarm Optimization (PSO) has been applied in this paper to solve the problem. Implementing a fuzzy-based PSO for service composition requires defining the fuzzy membership functions and rules based on the specific service domain. Once the fuzzy logic components are established, they can be integrated into the PSO algorithm. The simulation results have shown the high efficiency of the proposed method in decreasing the latency, cost, and response time. [ABSTRACT FROM AUTHOR]

Physical design is major key processes in integrating the circuit into the chip area in VLSI circuit design. A netlist is accepted as input by a floorplanning stage, provided by the physical design phase of splitting the circuits. The floor planner creates the best positions for the circuit module. The dimensions, size, and connectivity details of the modules are contained in the netlist. It is crucial to create an optimum floorplan by creating metaheuristic optimization algorithms to increase the circuit performance by reducing chip size, wire length, and peak temperature. This scheme always has a complexity to allocate the weights and might consequence in adverse bias with a specific intention. Here, the non-slicing floor plan is studied through the sequence pair (SP) method. SP is a method for packing the blocks using a pair of modules termed sequences. Consequently, optimal parameter (area and wire length) tuning is performed by a new hybrid Integrated Bald Eagle and SMO (IBE-SMO) scheme that combined the approaches such as BES and SMO for optimal FP by means of tuning the wire length and area, taking into consideration as multi-objective. This validates the optimization of wire length and area utilizing IBE-SMO to reduce the length, width and height of the floor plan area. The standard MCNC benchmark circuits were used to test the suggested technique, and the findings show that it decreases the area by 0.7 × 104, and wire length by 0.84 × 105, compared to the existing methods. [ABSTRACT FROM AUTHOR]

Many new metaheuristic algorithms prioritize their search strategy phase, often neglecting equally critical stages like initialization. Latin hypercube sampling (LHS) is one technique that stands out in this context. LHS selects representative samples through permutations in a multidimensional space, effectively preventing the clustering of points in specific areas. However, its limitations become apparent in high-dimensional problems, as it fails to provide crucial search space information and struggles to determine samples across all dimensions. Addressing these challenges, this paper introduces an innovative population initialization approach, combining the strengths of LHS with evolutionary behaviors. This technique is divided into two main sections: spatial and quality. The spatial section divides the search space into equal intervals across each dimension to establish initial solutions. Meanwhile, the quality section employs evolutionary strategies like mutation and crossover. These strategies serve a dual purpose: they explore the search space thoroughly and refine solutions, bringing them closer to the objective function. To validate the effectiveness of this method, these principles have been integrated it into the classic Differential Evolution algorithm. We conducted extensive tests using 30 representative benchmark functions to assess its performance. The experimental results are encouraging; our methodology not only speeds up convergence but also enhances solution quality, outperforming other similar techniques. [ABSTRACT FROM AUTHOR]

The distributed no-idle permutation flowshop scheduling problem has gained significant attention as a prominent area of research in recent years, particularly in industries where setup operations are so expensive that reactivating the machines is not cost-effective. This study addresses an extension of the distributed permutation flowshop scheduling problem with no-idle and due window constraints. The aim is to determine the job assignments to the factories and their sequences in each factory that provide the minimum total weighted earliness and tardiness (TWET) penalties considering due windows. This study is the first to formulate this problem, offering four different mathematical models, and presents a benchmark to examine different problem cases that may arise in practical applications. Furthermore, to effectively solve the diverse problem instances, two hybrid metaheuristic algorithms based on the Iterated Greedy are proposed. These metaheuristics exhibit promising capabilities, enabling the solution of problem instances involving up to 500 jobs. To assess the effectiveness of the proposed models and algorithms, extensive numerical experiments are conducted, facilitating a thorough evaluation and comparison of their performances. [ABSTRACT FROM AUTHOR]

Community search (CS) is a vital research area in network science that focuses on discovering personalized communities for query vertices from graphs. However, existing CS methods mainly concentrate on homogeneous or simple attributed graphs, often disregarding complex semantic information and rich contents carried by entities in heterogeneous graphs (HGs). In this paper, we propose a novel problem, namely the "Semantic Network Oriented Community Search with Meta-Structures in Heterogeneous Graphs (SNCS)," which aims to find dense communities that contain the query vertex, with vertices of the same type sharing similar topics. In response to this new problem, we present a novel approach, also named SNCS, representing the first solution employing meta-structures and topic constraints to tackle community search, leveraging both topological and latent features. To overcome the high-time complexity challenge posed by searching through meta-structures, we introduce a unique graph reconstruction technique. Our proposed method's superiority is validated through extensive evaluations on real-world datasets. The results demonstrate a significant improvement in the quality of the obtained communities, with increases of 3.5–4.4% in clustering coefficient and 5–11% in density while requiring only 4–46% of the running time when compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Medical image segmentation is crucial in using digital images for disease diagnosis, particularly in post-processing tasks such as analysis and disease identification. Segmentation of magnetic resonance imaging (MRI) and computed tomography images pose distinctive challenges attributed to factors such as inadequate illumination during the image acquisition process. Multilevel thresholding is a widely adopted method for image segmentation due to its effectiveness and ease of implementation. However, the primary challenge lies in selecting the optimal set of thresholds to achieve accurate segmentation. While Otsu's between-class variance and Kapur's entropy assist in identifying optimal thresholds, their application to cases requiring more than two thresholds can be computationally intensive. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. In this paper, we introduce an improved chameleon swarm algorithm (ICSA) to address these limitations. ICSA is designed for image segmentation and global optimization tasks, aiming to improve the precision and efficiency of threshold selection in medical image segmentation. ICSA introduces the concept of the "best random mutation strategy" to enhance the search capabilities of the standard chameleon swarm algorithm (CSA). This strategy leverages three distribution functions—Levy, Gaussian, and Cauchy—for mutating search individuals. These diverse distributions contribute to improved solution quality and help prevent premature convergence. We conduct comprehensive experiments using the IEEE CEC'20 complex optimization benchmark test suite to evaluate ICSA's performance. Additionally, we employ ICSA in image segmentation, utilizing Otsu's approach and Kapur's entropy as fitness functions to determine optimal threshold values for a set of MRI images. Comparative analysis reveals that ICSA outperforms well-known metaheuristic algorithms when applied to the CEC'20 test suite and significantly improves image segmentation performance, proving its ability to avoid local optima and overcome the original algorithm's drawbacks. Medical image segmentation is essential for employing digital images for disease diagnosis, particularly for post-processing activities such as analysis and disease identification. Due to poor illumination and other acquisition-related difficulties, radiologists are especially concerned about the optimal segmentation of brain magnetic resonance imaging (MRI). Multilevel thresholding is the most widely used image segmentation method due to its efficacy and simplicity of implementation. The issue, however, is selecting the optimum set of criteria to effectively segment each image. Although methods like Otsu's between-class variance and Kapur's entropy help locate the optimal thresholds, using them for more than two thresholds requires a significant amount of processing resources. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. Due to the aforementioned, we present an improved chameleon swarm algorithm (ICSA) in this paper for image segmentation and global optimization tasks to be able to address these weaknesses. In the ICSA method, the best random mutation strategy has been introduced to improve the searchability of the standard CSA. The best random strategy utilizes three different types of distribution: Levy, Gaussian, and Cauchy to mutate the search individuals. These distributions have different functions, which help enhance the quality of the solutions and avoid premature convergence. Using the IEEE CEC'20 test suite as a recent complex optimization benchmark, a comprehensive set of experiments is carried out in order to evaluate the ICSA method and demonstrate the impact of combining the best random mutation strategy with the original CSA in improving both the performance of the solutions and the rate at which they converge. Furthermore, utilizing the Otsu approach and Kapur's entropy as a fitness function, ICSA is used as an image segmentation method to select the ideal threshold values for segmenting a set of MRI images. Within the experiments, the ICSA findings are compared with well-known metaheuristic algorithms. The comparative findings showed that ICSA performs better than other competitors in solving the CEC'20 test suite and has a significant performance boost in image segmentation. [ABSTRACT FROM AUTHOR]

Due to environmental concerns associated with conventional energy production, the use of renewable energy sources (RES) has rapidly increased in power systems worldwide, with photovoltaic (PV) and wind turbine (WT) technologies being the most frequently integrated. This study proposes a modified Bald Eagle Search Optimization Algorithm (LBES) to enhance the performance of the conventional BES optimizer and optimize the size and location of RES-based Distribution Generation (DG) and Battery Energy Storage Systems (BESS) in distribution systems (DS) to minimize power and energy losses. The modified BES algorithm enhances the exploration phase by utilizing both crossover and mutation techniques with the top three leaders. Moreover, a loss sensitivity factor (LSF) is applied to expedite the solution process by identifying appropriate candidate buses. The variability of solar irradiation and wind speed is modeled using Weibull and Beta probability distribution functions (PDF). To address issues related to high penetration of renewables and demand fluctuations, BESS is used to improve power supply continuity and mitigate fluctuations. The suggested approach is tested on typical 33- and 118-bus systems and compared to alternative methods. The results show significant reduction in energy losses (49.32%, 67.82%, and 64.89% for the 33-bus system and 41.9157%, 60.3766%, and 54.8317% for the 118-bus system) when integrating PV, WT-based DG, and PV + BESS units into the DS. [ABSTRACT FROM AUTHOR]

Reinforced concrete (RC) shear walls play a pivotal role in resisting seismic and lateral loads within structural frameworks. A thorough examination of the existing literature was undertaken, covering a range of experimental and theoretical studies related to the design of RC shear walls. It was emphasized that comprehending shear failure behavior and precisely predicting the shear strength of RC walls holds considerable significance. To address this, the study proposes two models that integrate the support vector regression method with meta-heuristic optimization algorithms (Bat and GOA), utilizing 228 sets of experimental data. In identifying the parameters influencing the shear strength of RC shear walls, the study focused on eight influential factors. The comparison of the two proposed models in the current research with existing models and experimental data demonstrated their commendable accuracy, surpassing the performance of suggested empirical formulations. The prediction errors associated with the proposed models, when compared to experimental data, were notably low. An innovative approach was introduced in the research, presenting a novel method for predicting shear strength using the support vector regression method and the Bat optimization algorithm. A notable advantage of this formulation lies in its capacity to predict the shear strength across various configurations, including squat, cylindrical, and thin RC shear walls. Unlike some existing equations for predicting shear strength, this formulation exhibits no limitations. Through a comparative analysis with established equations, the computational framework's results suggest its successful applicability in building codes and construction practices. The proposed method contributes to the accurate prediction of shear strength in diverse RC shear wall configurations, offering a valuable tool for structural engineering applications. [ABSTRACT FROM AUTHOR]

One of the major challenges in the civil engineering sector is the durability of reinforced concrete structures against carbonation during the physico-chemical process of interaction of hydrated cementitious composites with carbon dioxide. This aggressive process causes carbon penetration into the reinforcement part, which affects the behavior of the structure during its lifetime due to corrosion risk. A countermeasure is using alternative cementitious materials to improve concrete texture and resist increased carbonation depth (CD). Considering that the CD test requires a long time and a skilled technician, this study strives to provide an alternative approach by moving from traditional laboratory-based methods towards artificial intelligence (AI) techniques for modeling the CD of sustainable concrete containing fly ash (CCFA). Despite the development of single AI models so far, it is undeniable that utilizing metaheuristic optimization techniques in the form of hybrid models can improve their performance. To this end, a new hybrid model from the integration of biogeography-based optimization (BBO) technique with artificial neural network (ANN) is developed for the first time to estimate the CD of CCFA. The error distribution results revealed that 59% of the ANN predictions had errors within the range of (− 1 mm, 1 mm], while the corresponding percentage for the ANN-BBO predictions was 70%, indicating an 11% reduction in the prediction errors by the proposed hybrid model. Furthermore, A10-index highlighted a performance improvement of 78% for the hybrid model, which met the closeness of the predicted values to the observed ones, so that the value of this index for models of ANN and ANN-BBO was 0.5019 and 0.8947, respectively. Analyzing the cross-validation confirmed the reliability and generalizability of the developed model. Also, the three most influential variables in estimating the CD were exposure time (27%), carbon dioxide concentration (22%), and water/binder (18%), respectively. Finally, the superiority of the ANN-BBO model was verified by comparing it with previous studies' models. [ABSTRACT FROM AUTHOR]

Digital twin network (DTN) as an emerging network paradigm, have garnered growing attention. For large-scale networks, a crucial problem is how to effectively map physical networks onto the infrastructure platform of DTN. To address this issue, we propose a heuristic method of the adaptive boundary whale optimization algorithm (ABWOA) to solve the digital twin network construction problem, improving the efficiency and reducing operational costs of DTN. Extensive comparison experiments are conducted between ABWOA and various algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony, differential evolution algorithm, moth search algorithm and original whale optimization algorithm. The experimental results show that ABWOA is superior to other algorithms in terms of solution quality, convergence speed, and time cost. It can solve the digital twin network construction problem more effectively. [ABSTRACT FROM AUTHOR]

The control system of the Inverted Pendulum System (IPS) has turned to be a common engineering issue for investigators because of its importance in the implementation in real life. The controller model is usually employed to stabilize the position and the angle of the IPS. In the present study, a cart IPS is designed using Artificial Neural Network (ANN) based Adaptive Neuro-Fuzzy Inference system (ANFIS) controller. To enhance the stability of the closed loop system, a hybrid meta-heuristic optimization algorithm is used to tune the hyper-parameters. The best position of the cart is determined using a hybrid approach (i.e. Ant-colony optimization and Particle Swarm Optimization Algorithms). The model is designed with 10 hidden layers and adopted 25 fuzzy logic rules. The proposed model is evaluated based on the settling time, rise time. The simulation results of the proposal model is performed using SIMULINK in MATLAB. It is found that the proposed model has attained low settling and rise time and low Integrated Squared of Errors (ISE) and Steady State Error rates (SSE). Thus, with these significant features, the proposed model can be practically implemented to acquire an efficient IPS that is optimally stable. To confirm the efficacy of the proposed system, comparison is undertaken with regard to Mean Square Error (MSE) for exposing the better performance of the proposed system. [ABSTRACT FROM AUTHOR]

The escalation of cloud services, driven by their accessibility, improved performance, and cost-effectiveness, has led cloud service providers to consistently seek methods to expedite job completion, thereby boosting profits and reducing energy consumption expenses. Despite developing numerous scheduling algorithms, many of these techniques address only a specific objective within the scheduling process. To efficiently achieve better optimization results for the cloud task scheduling problem, a novel approach, the Enhanced Whale Optimization Algorithm (EWOA), is proposed. EWOA integrates the WOA with the Lévy flight. The incorporation of Lévy flight is tailored to broaden the search space of WOA, expediting convergence with adaptive crossover. The EWOA model is simulated using the Cloudsim tool and evaluated under diverse test conditions. The effectiveness of EWOA is assessed by employing various parameters and comparing them with existing algorithms. The results demonstrate that EWOA outperforms other algorithms in resource utilization, energy consumption, and execution cost, establishing its superiority in addressing the complexities of multi-objective cloud task scheduling. [ABSTRACT FROM AUTHOR]

Image contrast is an important factor in distinguishing objects in the image from their background. Low-contrast images, caused by various factors such as poor lighting, are insufficient for human visual perception and many image processing applications. Therefore, image contrast enhancement (ICE) is a necessary preprocessing step in different image processing applications. The main purpose of ICE is to make image objects more easily distinguishable and to improve the quality of visual information in the image. In this paper, image contrast enhancement is studied as an optimization problem. First, a modified version of the gray wolf optimization (GWO) algorithm, a population-based meta-heuristic that mimics the social leadership and hunting behavior of gray wolves in nature, is adapted to the ICE problem. Second, a novel variant of GWO is proposed using Halton low-discrepancy sequence in the population initialization phase, instead of starting completely randomly. Third, unlike previous studies, an effective metric is used as a fitness function to measure the image quality by focusing on the contrast change without using a reference image. The experimental results on the TID2013 and CSIQ datasets show that the proposed Halton sequence-initialized GWO outperforms other variant metaheuristic algorithms and traditional histogram equalization-based methods, according to all utilized various evaluation metrics. [ABSTRACT FROM AUTHOR]

Taking a lot of time to solve optimization problems has become a challenge for metaheuristic algorithms. Due to independence of the metaheuristics components, parallel processing is a good option to reduce the computational time and to find high quality solutions that are close to the optimum with an acceptable cost. One of these metaheuristic algorithms is the Sailfish Optimizer (SFO) which is inspired by a group of hunting sailfish. The SFO algorithm uses a simple method to provide a dynamic balance between exploration and exploitation phases, creates a swarm diversity, avoids local optima, and guarantees high convergence speed. It has been shown that the SFO algorithm outperforms various state-of-art metaheuristic algorithms for multimodal and high dimensional benchmark functions and complicated real-world optimization problems in terms of accuracy and speed by CPU and GPU implementation. In this paper, to speedup this algorithm and increase its performance we propose a reconfigurable hardware version of SFO implemented on Field Programmable Gate Array (FPGA). The FPGA-based SFO can be a very good option in many applications with massive calculations. Due to the inherent parallelism and high computing capabilities of FPGA, the SFO algorithm gains optimum computational time despite the complexity of optimization problems. We have compared the performance of the proposed FPGA-based SFO with its CPU and GPU implementation and some other metaheuristic algorithms. The results show the FPGA implementation is considerably faster than the CPU and GPU implementation. Also, it outperforms other compared FPGA-based metaheuristic algorithms in terms of execution time and convergence speed. [ABSTRACT FROM AUTHOR]

Clustering is a popular data analysis technique that can explore the structure of data through cluster analysis. Similar data are put into the same cluster, while dissimilar data allocate to other clusters. The similarity/dissimilarity among data objects is determined using a distance function. Further, clustering algorithms aim to choose the optimal set of centroids for obtaining better partitioning, but clustering accuracy is always susceptible. This issue of clustering is addressed through meta-heuristic algorithms. This research also aims to handle the accuracy issue and presents a new algorithm for effective cluster analysis. The proposed clustering algorithm is inspired by a water flow optimizer (WFO). The WFO algorithm performance is validated on the well-defined clustering problems based on SSE, accuracy (AR) and detection rate (DR) parameters. The results indicate that the WFO algorithm gets higher clustering results in terms of SSE, AR and DR than the same class of algorithms. The performance is also validated using Friedman statistical test followed by a post hoc test. Results indicated that the proposed WFO gets better statistical results than other clustering algorithms. [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]

The advent of Internet of Things (IoT) in agriculture has revolutionized the way farmers monitor and manage their crops. IoT-enabled sensors can provide real-time data on various environmental parameters such as temperature, humidity, soil moisture, and crop growth, which can be used to make informed decisions and optimize crop yield. However, the vast amount of data generated by these sensors poses a significant challenge in terms of data processing and communication. To address this challenge, clustering is often used to group the sensors into clusters and elect a cluster head (CH) to communicate with the gateway node. The selection of an appropriate CH and the optimal path for data transmission are critical factors that affect the performance of the IoT system. In this paper, we propose a novel approach to optimize the CH selection and path selection using modified fuzzy logic, Whale optimization algorithm (WOA) and enhanced crow swarm optimization (ECSO). Fuzzy logic is used to evaluate the relevant parameters such as energy, distance, overhead, trust, and node degree to select the most suitable CH. ECSO is then employed to find the optimal path for data transmission based on the selected CH. We evaluate the proposed approach using simulation experiments in a smart agriculture scenario. The results show that the proposed approach outperforms existing approaches in terms of throughput, packet delivery ratio, delay, and energy efficiency. The proposed system demonstrates substantial advantages in terms of throughput, with a remarkable throughput value of 68 Mbps, outperforming the other methods, including EQSR (12.5 Mbps), E2S-DRL (60 Mbps), and OSEAP. The packet delivery ratio (PDR) is also significantly higher for the proposed system, reaching 90.9%. The proposed approach can significantly improve the performance of IoT-enabled smart agriculture systems, leading to better crop yield and higher profitability for farmers. The results of the simulation experiments demonstrate the superiority of the proposed approach over the existing one's throughput, packet delivery ratio (PDR), delay, and energy consumption efficiency is found in the result section. [ABSTRACT FROM AUTHOR]

Machine learning and data mining rely on feature selection to reduce the dimension of data and increase the performance of algorithms. As a result of such a large search space, feature selection is a challenging task. Recently, evolutionary techniques have been gaining a lot of attention and showing some promise for solving feature selection problems. Recent studies have shown that Whale Optimization Algorithm (WOA) is widely used in various fields (e.g., data mining, machine learning, and cloud computing). Motivated by the extensive research efforts in the feature selection and WOA, we present a review of high-quality articles related to WOA-based feature selection algorithms published between 2017 and 2023. This paper discusses and compares WOA-based feature selection schemes based on merits and demerits, evaluation techniques, simulation environments, and important parameters. We begin by introducing feature selection process, and concepts of metaheuristic followed by their surveys. This study summarizes several domains where WOA is used and explains different types of features. Moreover, it categorizes the variants of WOA based on their learning process, parameter tuning, binary/discrete, and hybridization. According to the investigation results, few variations of WOA add new parameters or operators to the original. In addition, 60% of feature selection algorithms based on WOA focus on improving learning process. Finally, current issues and challenges are also discussed to identify future research areas. [ABSTRACT FROM AUTHOR]

The presence of overlapping mangoes and leaves makes the segmentation process inaccurate. In addition, the variation in colour, texture, shape, temperature and shadow effects of the real conditioned image makes segmentation even more complex. The presence of outliers would further affect the classification accuracy. Many studies have reported the deep learning-based technique, but those methods failed to provide an effective solution due to the high processing stage and limited data usage. Hence, this work introduces a machine learning-based technique that reduces the problem of time-consuming predictions. This work aims to estimate the defected region of mangoes by enhanced segmentation and optimal feature selection to enhance classification accuracy. To detect mango fruit defects, initially, the collected mango images are pre-processed to smoothen and reduce image noise. This is achieved using the guided Gabor bilateral filter; the technique can reduce image noise without information loss. Then the obtained pre-processed image is segmented by considering the defect as a region of interest. The segmentation is achieved using the fuzzy level set method (FLSM), which creates clusters for an image's dynamic variation. Then, the features are extracted using the dual-tree complex transform (DT-CT) and the optimal features are selected using the metaheuristic algorithm adaptive tunicate swarm optimizer (ATSO). The obtained optimal features are used for the detection process, which uses an Extreme Gradient Tree boost classifier (EGTBoost) classifier and the output is generated using vote-based classification. This classifier accurately classifies the diseased and healthy mangoes. The experimentation is carried out on the Kaggle and the real-time datasets. The accuracy and precision values achieved by the proposed model are 0.969 and 0.986 on the Kaggle dataset, respectively. [ABSTRACT FROM AUTHOR]

There has been severe illness and mortality globally as a result of the COVID-19 pandemic brought on by the SARS-CoV-2 virus. As the virus continues to spread, it has also undergone mutations leading to the emergence of severity among patients. This severity has raised concerns due to their potential to be more transmissible, cause more severe disease, and escape vaccine-induced immunity. In this study, the goal is to create a technique for identifying and classifying COVID-19 severity using image data. Then the input image is preprocessed using data standardization and image augmentation. The Conditional Generative adversarial networks (CGAN) model is used for feature extraction and the classification is achieved through a novel modified MobileNet. Moreover, a modified Mobile Net is also enhanced through a Hybrid Artificial Eco-System with Monarch Butterfly Optimization (HAEMBO), which is the combination of Artificial Ecosystem optimizer (AEO) and Monarch Butterfly Optimization (MBO). The HAEMBO algorithm enriches the performance of the modified MobileNet. The Recurrent Neural Network (RNN) is employed for severity score detection. The PYTHON platform is used for implementation. The results demonstrated that the proposed method is able to accurately detect and classify COVID-19 severity. [ABSTRACT FROM AUTHOR]

Each mobile user in a typical multi-user mobile cloud computing (MCC) system has a number of independent tasks to do. In the modern world of finite resources and growing demands, it is critical to make the best use of multiple available resources in order to optimize their edge server placements. In this article, we explore the best ways to deploy edge servers in a cost-effective and efficient manner. The issue of reducing the quantity of edge servers while maintaining the need for access delay in MCC setting has been addressed. The selection of the fewer computational access points co-located with an edge server to ensure optimal service for all users is one of the primary issues. The other is determining how to appropriately allocate offloading tasks to edge servers. We partition the mobile networks into clusters in response to these difficulties, and the cluster heads are co-located with edge servers. We redefine the term "dominating set" and convert the problem under consideration into the dual-modeled game theory (DGT) equivalent of the minimal dominating set problem. We provide new optimizer-based techniques to find the best solutions depending on various scenarios. Resource sharing and clustering can be done using an adapted Gaussian distribution function with the whale optimization algorithm (AGDF-WOA). The offloading choices can be made by each user using AGDF-WOA-DGT progress. Its effective reduction of edge servers and load balance that leads to lower energy and cost make it a desirable option for MCC through simulations. [ABSTRACT FROM AUTHOR]

Logistics distribution is one of the main sources of carbon dioxide emissions at present, and there are also such distribution problems in the shipbuilding process. With the increasing attention paid to environmental problems, how to effectively reduce the energy consumption of block transportation and improve the utilization rate of resources in the factory is the key problem that China's shipbuilding industry needs to solve at present. This article considers the time windows for block transportation tasks, as well as the self-loading constraints of different types of flat cars, and establishes an optimization model that minimizes the empty transport time and energy consumption of the flat cars as the optimization objective. Then, an Improved Genetic Whale Optimization Algorithm is designed, which combines the cross and mutation ideas of genetic algorithms and proposes a whale individual position updating mechanism under a mixed strategy. Furthermore, the performance and computational efficiency of the algorithm are verified through comparative analysis with other classical optimization algorithms on standard test examples. Finally, the shipyard's block transportation example proves that the energy-saving ship block transportation scheduling method can effectively improve the efficiency of shipbuilding enterprise's block transportation and reduce the energy consumption in the block transportation process. It proves the engineering practicality of the green dispatching method proposed in this paper, which can further provide a decision-making method for shipyard managers. [ABSTRACT FROM AUTHOR]

Due to the current environmental situation and human health, a green manufacturing system is very essential in the manufacturing world. Several researchers have developed various types of green manufacturing models by considering green products, green investments, carbon emission taxes, etc. Motivated by this topic, a green production model is formulated by considering selling price, time, warranty period and green level dependent demand with a carbon emission tax policy. Also, the production rate of the system is an unknown function of time. Per unit production cost of the products is taken as increasing function of production rate and green level of the products. In our proposed model, carbon emission rate is taken as linear function of time. Then, an optimization problem of the production model is constructed. To validate of our proposed model, a numerical example is considered and solved it by AHA. Further, other five metaheuristics algorithms (AEFA, FA, GWOA, WOA and EOA) are taken to compare the results obtained from AHA. Also, concavity of the average profit function and convergence graph of different metaheuristics algorithms are presented. Finally, a sensitivity analysis is carried out to investigate the impact of different system parameters on our optimal policy and reach a fruitful conclusion from this study. [ABSTRACT FROM AUTHOR]

Economic development relies on access to electrical energy, which is crucial for society's growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia's Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95–1.025 p.u.). After reviewing the current distribution network's operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system's integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training and testing sets), followed by data augmentation techniques. Both feature extraction and classification tasks are employed by a Custom CNN. The experimental results show that the proposed approach using the Custom CNN model exhibits better performance with an accuracy of 84% than applying the same approach using other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, and ResNet50V2, that present relatively lower accuracies, ranging from 74 to 82%; these four models are used as both feature extractors and classifiers. To increase the accuracy and other performance metrics, Grey Wolf Optimization (GWO), and Modified Gorilla Troops Optimization (MGTO) metaheuristic optimizers are applied to each model separately for hyperparameter tuning. In this case, the experimental results show that the Custom CNN model, refined with MGTO optimization, reaches an exceptional accuracy of 93.13% in just 10 iterations, outperforming the other state-of-the-art methods, and the other four used pretrained models based on the BreakHis dataset. [ABSTRACT FROM AUTHOR]

Archimedes Optimization Algorithm (AOA) is a recent optimization algorithm inspired by Archimedes' Principle. In this study, a Modified Archimedes Optimization Algorithm (MDAOA) is proposed. The goal of the modification is to avoid early convergence and improve balance between exploration and exploitation. Modification is implemented by a two phase mechanism: optimizing the candidate positions of objects using the dimension learning-based (DL) strategy and recalculating predetermined five parameters used in the original AOA. DL strategy along with problem specific parameters lead to improvements in the balance between exploration and exploitation. The performance of the proposed MDAOA algorithm is tested on 13 standard benchmark functions, 29 CEC 2017 benchmark functions, optimal placement of electric vehicle charging stations (EVCSs) on the IEEE-33 distribution system, and five real-life engineering problems. In addition, results of the proposed modified algorithm are compared with modern and competitive algorithms such as Honey Badger Algorithm, Sine Cosine Algorithm, Butterfly Optimization Algorithm, Particle Swarm Optimization Butterfly Optimization Algorithm, Golden Jackal Optimization, Whale Optimization Algorithm, Ant Lion Optimizer, Salp Swarm Algorithm, and Atomic Orbital Search. Experimental results suggest that MDAOA outperforms other algorithms in the majority of the cases with consistently low standard deviation values. MDAOA returned best results in all of 13 standard benchmarks, 26 of 29 CEC 2017 benchmarks (89.65%), optimal placement of EVCSs problem and all of five real-life engineering problems. Overall success rate is 45 out of 48 problems (93.75%). Results are statistically analyzed by Friedman test with Wilcoxon rank-sum as post hoc test for pairwise comparisons. [ABSTRACT FROM AUTHOR]

The specifications of AISC 360 and Eurocode 4 for the design of composite columns limit the maximum steel tube yield stress and concrete compressive strength. In this study, the limitations mentioned in the design codes are evaluated, and a new simplified relation for the nominal compressive capacity of square high-strength concrete-filled high-strength steel tube (HsCHsST) stub columns is proposed. The present study was carried out in three parts. The first part involves compiling a test database of square-filled composite columns with high-strength materials to achieve the aptest relation. The second part consists of developing a simplified relation for determining the effects of material strength on the nominal compressive strength of columns ( P n ) based on the compiled database using a hybrid gene expression programming-invasive weed optimization algorithm. Finally, a new resistance factor ( ϕ c ) for axially loaded HsCHsST columns is determined in the third part. The predicted results for nominal compressive strength were compared with specifications of AISC 360 and Eurocode 4 in terms of various performance parameters based on measured results to validate the proposed relation. This study's findings can be used as a suitable tool in the design compressive strength of square HsCHsST members. [ABSTRACT FROM AUTHOR]

The research aims to propose a feature selection model for hydraulic analysis as such a model has not been proposed previously. For this purpose, hybrids of three metaheuristic algorithms, particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) with two machine learning models which are support vector machine (SVM) and K-nearest neighbor (KNN) are employed. The dataset considered was hydraulic having an association with flood and possessed topographic, geo-environmental, and human-induced variables. The dataset considered had multicollinearity heteroscedasticity and autocorrelation problems. The metaheuristic algorithms were evaluated by varying the number of population size. Among them, PSO performed better by providing an appropriate number of features with a lower number of iterations. We have analyzed the performance of SVM with different kernels; linear, radial basis function (RBF), sigmoid, and polynomial, as the original SVM is designed only for linear datasets but the hydraulic dataset possesses non-linear characteristics as well. The performance of different kernels in terms of their accuracies is evaluated and recorded. This study showed that RBF performed the best and sigmoid showed the least accuracy for GA, PSO, and ACO algorithms. The performance of KNN is evaluated in terms of accuracies by varying the K-values. It was found that KNN shows low accuracy with a small K-value which then attained a maximum level by increasing K-values, and it finally started decreasing, explicitly, by further enhancing K-values. While comparing the performance of hybrids of GA, PSO, and ACO with SVM and KNN, it was analyzed that KNN performed better with these meta-heuristics with PSO-KNN which performed the best among the baseline models. Thus, the study proposes that PSO-KNN can be utilized as a feature selection technique to obtain optimal data subsets for hydraulic modeling and analysis. [ABSTRACT FROM AUTHOR]

The Fennec Fox algorithm (FFA) is a new meta-heuristic algorithm that is primarily inspired by the Fennec fox's ability to dig and escape from wild predators. Compared with other classical algorithms, FFA shows strong competitiveness. The "No free lunch" theorem shows that an algorithm has different effects in the face of different problems, such as: when solving high-dimensional or more complex applications, there are challenges such as easily falling into local optimal and slow convergence speed. To solve this problem with FFA, in this paper, an improved Fenna fox algorithm DEMFFA is proposed by adding sin chaotic mapping, formula factor adjustment, Cauchy operator mutation, and differential evolution mutation strategies. Firstly, a sin chaotic mapping strategy is added in the initialization stage to make the population distribution more uniform, thus speeding up the algorithm convergence speed. Secondly, in order to expedite the convergence speed of the algorithm, adjustments are made to the factors of the formula whose position is updated in the first stage, resulting in faster convergence. Finally, in order to prevent the algorithm from getting into the local optimal too early and expand the search space of the population, the Cauchy operator mutation strategy and differential evolution mutation strategy are added after the first and second stages of the original algorithm update. In order to verify the performance of the proposed DEMFFA, qualitative analysis is carried out on different test sets, and the proposed algorithm is tested with the original FFA, other classical algorithms, improved algorithms, and newly proposed algorithms on three different test sets. And we also carried out a qualitative analysis of the CEC2020. In addition, DEMFFA is applied to 10 practical engineering design problems and a complex 24-bar truss topology optimization problem, and the results show that the DEMFFA algorithm has the potential to solve complex problems. [ABSTRACT FROM AUTHOR]

The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm's ability to escape from local optimal. To verify IMRFO's performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms. [ABSTRACT FROM AUTHOR]

Several real-world scientific and industrial workflow applications adopt elastic and cost-efficient cloud services to fulfill their requirement. There are two stakeholders in the system, namely the user and the provider each of which tries to maximize its profit and at the same time minimize their possible overall costs. Since task scheduling algorithms for workflow executions determine which task should be possibly executed on what resources, they have a drastic impact on both the user's quality of experiences and on the underlying resource utilization. Indeed, an efficient task scheduling algorithm can meet service-level agreement (SLA) for both sides. For the sake of the importance of the issue, this survey presents a subjective taxonomy on the task scheduling schemes in the literature for workflow executions in cloud computing environments to be a guideline for future improvement. It classifies the literature based on the proposed algorithms in the literature, objectives, stakeholders' requirements, and evaluation metrics. This survey highlights research trends, challenges, research gaps, potential solutions, and future direction. It can also pave the way for further processing, improvement and strengthening of existing approaches or devising novel ones for interested researchers in the field of task scheduling problems. [ABSTRACT FROM AUTHOR]

Arithmetic optimization algorithm (AOA) is a meta-heuristic optimization method based on mathematical operators proposed in recent years. Although it has good performance, it can also lead to insufficient local search ability and falling into local optima when solving complex optimization problems. In order to make up for the above shortcomings, the optimization performance of AOA is further improved. This paper proposes a hybrid algorithm based on AOA and particle swarm optimization (PSO) called HAOAPSO. Firstly, a compound opposition-based learning (COBL) strategy is introduced to broaden the scope of finding optimal solutions to help the algorithm better jump out of local optima. Secondly, PSO is combined with AOA that integrates COBL to improve the algorithm's local search ability, so as to improve the overall search efficiency of the algorithm. In addition, experiments are performed on 23 classical benchmark functions with different characteristics and five engineering design optimization problems, and the experimental results of HAOAPSO are compared with those of other well-known optimization algorithms to comprehensively evaluate the performance of the proposed algorithm. The simulation results show that HAOAPSO can provide better solutions in most cases when solving global optimization problems such as engineering, with better convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

The stochastic fractal search (SFS) algorithm, among population-based metaheuristic automation algorithms, is a robust optimization algorithm for solving optimization problems in different fields of science, inspired by the diffusion feature and natural growth phenomenon seen regularly in random fractals. However, as in population-based optimization algorithms, it is a great challenge to effectively design the selection process in the SFS method. To imitate the selection process in nature effectively and accurately, the dynamic-fitness-distance-balance (dFDB) selection method has been used in the SFS algorithm in six different versions. In this way, the dFDB-SFS algorithm has been developed, which more effectively mimics nature with exploitation, exploration, and balanced search capabilities. Firstly, the performance of the proposed dFDB-SFS algorithm was investigated in CEC 2020 benchmark test functions. Wilcoxon and Friedman statistical analyses of the results obtained from the test functions were made and according to these analysis results, the best version of the proposed approach was determined. Secondly, the performance of the proposed algorithm was investigated in determining photovoltaic module parameters, which is one of the real-world engineering problems. In this article, the dFDB-SFS algorithm uses the root mean square error (RMSE) as the objective function to estimate the unknown parameters of the single diode model (SDM), double diode model (DDM), and PV module models. In terms of a quantitative and qualitative performance evaluation, it reveals that the proposed algorithm provides better results than other proposed algorithms in terms of accuracy and robustness when obtaining PV parameters. [ABSTRACT FROM AUTHOR]

In scenarios where complex analyses are routinely conducted on similar structures, such as in a redesign process to meet performance requirements or when input parameters require frequent adjustments within a specified domain, a practical approach involves the use of metamodels calibrated using machine learning methodologies. In our investigation, we introduce a metamodel that utilizes an artificial neural network to analyze 3D nonlinear structures undergoing plastic deformations and large strains. Snap-through and snapback behaviors are addressed through network training, which is based on 10,000 Force vs Displacement curves (target outputs) obtained from nonlinear finite element analyses. This interplay between finite element analysis and machine learning, as demonstrated here, exhibits promising potential as an effective technique. The results indicate that the proposed deep neural network can learn from the simulations of finite elements. The discussion explores scenarios where the utilization of AI in the analysis of nonlinear structures is justified. [ABSTRACT FROM AUTHOR]

Mobile object detection and tracking is an important application of wireless sensor network. However, longevity of such application is a challenge as sensor nodes in wireless sensor network operates on battery power. Therefore, there is the requirement for increasing the lifetime of this application by optimal usage of energy while tracking. Of course, while optimizing the energy usage, tracking accuracy should not be compromised. A number of research works are carried out in the past to enhance the wireless sensor network lifetime but no work resolved the problem completely. This paper studies the issue of data routing to sink in energy constrained wireless sensor network based application where tracking information collected from different sensor nodes are processed to determine the object's presence in the monitoring area. This paper proposes an energy efficient hybrid artificial bee colony-modified Dijkstra (HABC-MD) algorithm for cluster based network for optimum usage of energy during routing of packet. The objective of this algorithm is to save the energy of the network by optimizing the number of node-to-node transmission while tracking. It uses metaheuristic approach based artificial bee colony algorithm for selection of cluster head in each round and heuristic approach-based Dijkstra algorithm for selection of optimum route between source node and sink for packet transmission. Modified Dijkstra algorithm considers both distance between source and destination and energy of the source node for determination of optimum route to sink. The performance of HABC-MD is compared with existing hybrid LEACH-Dijkstra algorithm and is found that performance of our algorithm is better in comparison to later algorithm. [ABSTRACT FROM AUTHOR]

In recent years, Sine Cosine Algorithm (SCA) is a kind of meta-heuristic optimization algorithm with simple structure, simple parameters and trigonometric function principle. It has been proved that it has good competitiveness among the existing optimization algorithms. However, the single mechanism of SCA leads to its insufficient utilization of the information of the whole population, insufficient ability to jump out of local optima and poor performance at solving complex objective function. Therefore, this paper introduces social learning strategy (SL) and elite opposition-based learning (EOBL) strategy to improve SCA, and proposes novel algorithm: enhancing Sine Cosine Algorithm based on elite opposition-based learning and social learning (ESLSCA). Social learning strategy takes full advantage of information from the entire population. The elite opposition-based learning strategy provides a possibility for the algorithm to jump out of local optima and increases the diversity of the population. To demonstrate the performance of ESLSCA, this paper uses 22 well-known benchmark test functions and CEC2019 test function set to evaluate ESLSCA. The comparisons show that the proposed ESLSCA has better performance than the standard SCA and it is very competitive among other excellent optimization algorithms. [ABSTRACT FROM AUTHOR]

Multi-path and multi-hop routing are multi-objective optimization problems involving multiple constraints that need to be addressed in the current scenario in wireless sensor networks. The routing process is challenging due to the constrained energy resources and transmission bandwidth. The conventional strategies possess shortcomings, like, as high computing complexity, extensive problem-solving time, complexity in achieving optimal values, and ease of falling into local solutions. Hence, the aim is to propose a hybrid metaheuristic algorithm, known as a multi-objective optimized multi-path and multi-hop routing algorithm (MMMRA). It incorporates the chimp optimization algorithm (COA) for determining the optimal multi-path route based on multi-objective function and ant colony optimization for determining the optimal multi-hop routing. The proposed MMMRA is implemented using NS-2 and to evaluate the performance, nine various scenarios are considered. The MMMRA is validated using different performance measures and compared with other benchmark algorithms. The simulation results indicate that the MMMRA exhibits percentage improvement in terms of residual energy by 1.63%, 4.96%, 6.89%, 7.51%, and 9.67% over IPSMT, BIM2RT, SCP, PSOBS, and RDICMR algorithms respectively. Moreover, the HND and FND of the MMMRA algorithm perform better in all three scenarios (center, corner, and outside positions of sink node), especially when the sink node is placed at the center position, the HND of MMRA shows a percentage improvement by 24% and 12.73% over IPSO–GWO, and COA–HGS algorithms respectively. Similarly, the FND of MMRA shows percentage improvement by 21.05% and 9.5% over IPSO–GWO, and COA–HGS algorithms respectively. [ABSTRACT FROM AUTHOR]

The meta-heuristic algorithms require a lot of fitness calculations to get good enough solutions, which constitutes an obstacle to solving computationally complex practical problems. Recently, it has been found that surrogate-assisted meta-heuristic algorithms show potential in solving expensive complex optimization problems. This paper proposes an efficient surrogate-assisted Taguchi salp swarm algorithm (SATSSA) to solve expensive complex optimization problems. The SATSSA uses a combination of the local surrogate-assisted model (LSAM), global surrogate-assisted model (GSAM), and k-means clustering surrogate-assisted model (KCSAM) to fit the fitness function. To enhance the prediction ability of the model, an improved salp swarm algorithm with the Taguchi method (TSSA) is proposed to update and predict the model. GSAM is mainly used to capture the entire landscape of the search space. KCSAM is designed to capture part of the search space to improve the exploration capability of the algorithm. LSAM is used to capture the contours around the optimal individual. The proposed SATSSA is compared with other four excellent algorithms on 30D, 50D, and 100D benchmark functions. In addition, SATSSA is also applied to intrusion detection. Simulation results show that SATSSA is effective in improving detection rate and reducing false alarm rate. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) as an emerging technology is widely used in various applications such as remote healthcare, smart environment, and intelligent transportation systems. It is necessary to address users' concerns about cost, ease of use, privacy, and comprehensive security to grow the popularity of this technology. Intrusion Detection System (IDS) plays an indispensable role in security and preventing unauthorized users to access authorized network resources through analyzing network patterns. Several techniques such as metaheuristic algorithms, machine learning, fuzzy logic, and artificial intelligence algorithms can be applied to increase the accuracy of IDS, feature selection, and network patterns classification. In this paper, a hybrid intrusion detection system based on Support Vector Machine (SVM) and Grey Wolf Optimization (GWO) is presented that utilizes the advantages of these algorithms. In the proposed approach, the support vector machine has been used to train and differentiate anomaly records from normal records and grey wolf optimization has been used to find the kernel function, feature selection, and adjust optimal parameters for the SVM in order to improve the classification. The conducted simulations prove that the proposed approach outperforms in terms of detection accuracy, precision, recall, and F-score on both NSL-KDD and TON_IoT datasets. [ABSTRACT FROM AUTHOR]

Alternative energy sources are needed for a sustainable world due to rapidly increasing energy consumption, fossil fuels, and greenhouse gases worldwide. A hybrid renewable energy system (HRES) must be optimally dimensioned to be responsive to sudden load changes and cost-effective. In this study, the aim is to reduce the carbon emissions of a university campus by generating electricity from a hybrid energy production system with solar panels, wind turbine, a diesel generator, and battery components. On the university campus where the hybrid energy system will be installed, the ambient temperature, solar radiation, wind speed, and load demands have been recorded in our database. Optimization algorithms were used to select the power values of the system components to be installed using these data in an efficient and inexpensive manner according to the ambient conditions. For optimal sizing of HRES components, gray wolf optimizer combined with cuckoo search (GWOCS) technique was investigated using MATLAB/Simulink. In this way, it has been tried to increase their efficiency by combining current optimization techniques. The cornerstone of our optimization efforts for both on-grid and off-grid models pivots on a constellation of critical decision variables: the power harvested from wind turbines, the productivity of solar panels, the capacity of battery storage, and the power contribution of diesel generators. In our pursuit of minimizing the annual cost metric, we employ a tailor-made function, meticulously upholding an array of constraints, such as the quotient of renewable energy and the potential risk of power disruption. A robust energy management system is integral to our design, orchestrating the delicate power flow balance among micro-grid components—vital for satisfying energy demand. Upon analyzing the outcomes of the study, it is apparent that the proposed Scenario 1 HRES effectively utilizes solar and battery components within the off-grid model, surpassing the efficiency of four other hybrid scenarios under consideration. Regarding optimization processes, the off-grid model exhibits superior results with the implementation of the GWOCS algorithm, delivering faster and more reliable solutions relative to other methodologies. Conversely, the optimization of the on-grid model reaches its optimal performance with the application of the cuckoo search algorithm. A comprehensive comparison from both technical and economic view points suggests the on-grid model as the most feasible and suitable choice. Upon completion of the optimization process, the load demand is catered to by a combination of a 2963.827-kW solar panel, a 201.8896-kW battery, and an additional purchase of 821.9 MWh from the grid. Additionally, an energy surplus sale of 1379.8 MWh to the grid culminates in an annual cost of system (ACS) of 475782.8240 USD, a total net present cost of 4815520.2794 USD, and a levelized cost of energy of 0.12754 USD/kWh. Solar panels cover the entire system, and the renewable energy fraction is 100%. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS is categorized as NP-Hard due to the high number of existing solutions. Various metaheuristic methods have been developed to address the FS problem, yielding promising results. Particularly, particle swarm optimization (PSO), an evolutionary computing (EC) approach guided by swarm intelligence, has gained widespread adoption owing to its implementation simplicity and potential for global search. This paper analyzes several variants of PSO algorithms and introduces a new FS method called HPSO. The proposed approach utilizes an asynchronously adaptive inertia weight and an improved constriction factor. Additionally, it incorporates a chaotic map and a MAD fitness function with a feature count penalty to tackle the clustering FS problem. The efficiency of the developed method is evaluated against the genetic algorithm (GA) and well-known variants of PSO algorithms, including PSOs with fixed inertia weights, PSOs with improved inertia weights, PSOs with fixed constriction factors, PSOs with improved constriction factors, PSOs with adaptive inertia weights, and PSO's includes advanced learning exemplars and sophisticated structure topologies. This paper assesses two different reference text data sets, Reuters-21578 and Webkb. In comparison with competitive methods, the proposed HPSO method achieves higher clustering precision and selects a more informative feature set. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are increasingly being used in a variety of applications, such as smart healthcare systems and the Internet of Things (IoT). Trust in WSNs is the confidence in secure, efficient collaboration among nodes, crucial for reliable data packet routing. However, their susceptibility to trust-related challenges such as different threats, high energy use, and communication issues among nodes, pose substantial challenges to data integrity and availability. Recognizing the pivotal role of trust in ensuring secure data exchange and dependable device communication within the IoT framework, this paper introduces a novel trust-aware optimization technique. The approach integrates trust management into routing protocols, employing the resilient decision-making algorithm Dempster-Shafer Theory (DST) for trusted clustering and the Whale Optimization Algorithm (WOA) for routing. The primary objective is to enhance the reliability and energy efficiency of WSNs by incorporating trustworthiness considerations into optimal cluster and route selection. The proposed DST-WOA technique is evaluated using the MATLAB Simulator, demonstrating a significant 23%, 22%, and 4% improvement over existing protocols (TBSIOP, SQEER, SSDBN-TAA) in network lifetime, energy efficiency and throughput, respectively. The proposed research contributes to the development of more robust and sustainable WSNs for applications in areas such as smart healthcare systems, and IoT. [ABSTRACT FROM AUTHOR]

Medical breakthroughs are currently being made to widen human beings' existence. For the vast majority of online medical care applications, Wireless Body Area Networks (WBANs) have emerged as an intriguing and important invention. Route loss and obstacles in offering essential information are significant criteria that drain power from the battery source and impair battery lifespan. By employing Optimal K-Means Clustering (OKMC), all body sensor nodes are positioned in tandem to construct cluster head selection. This research explores a novel metaheuristic method for strengthening network lifetime optimization and a sophisticated route selection strategy incorporating the Energy Enrichment Multi-Hop Routing (EEMR) protocol. The EEMR is intended to operate in a pair of phases. The Enhanced Flower Bee Optimisation Algorithm (EFBOA) is laid out as the initial phase, with the key objective of augmenting the network lifetime of the WBAN by laying down a network of clusters. The next phase employs Dynamic Local Hunting and Location Discarding (DLH-LD) to figure out the fastest route among all possible paths. The results of this analysis are validated and implemented in real-time using MATLAB and the Network Simulator. Various protocols, such as Mobility-supporting Adaptive Threshold-based Thermal-aware Energy-efficient Multi-hop Protocol (M-ATTEMPT), Even Energy Utilization Convention Routing (EECR), and Energy Mindful Posterior Routing (EMPR), are being compared to parameters such as path loss, throughput rate, energy consumption, cost estimation, and network lifetime. The outcomes revealed that, in comparison with conventional approaches, the suggested EEMR performs substantially better than existing routing protocols, spanning a range of performance indicators. Additionally, the research and simulation findings show that the suggested protocol is 30% more energy efficient than existing protocols, extending the life of the network. The numerical results exhibit an extensive performance enhancement of 95% in network throughput rate. [ABSTRACT FROM AUTHOR]

The rapid spread and daily usage of social networks have made them vulnerable to spammers. Therefore, detecting and eliminating spam and spammers has become more than necessary to reduce the risks that it poses to users' security. In order to achieve this goal, it is crucial to determine the exact features that help identify and classify whether a user is spam or not. This paper proposes a wrapper-based method for selecting the most important features. It is based on combining two recent metaheuristic algorithms, the Henry Gas Solubility Optimization Algorithm (HGSO) and the Equilibrium Optimizer Algorithm (EO), with the goal of choosing a small and most influential subset of features that give good performance and help in the spammer profile detection process. For the purpose of showing the ability of the proposed method to achieve the desired goals, several comparisons are conducted on a modified Social Honeypot dataset. The first comparison is made between HGSOEO and the two algorithms (HGSO and EO) that were used to develop the proposed algorithm to prove the power of hybridization. The two next comparisons are made against some classical filter- and wrapper-based feature selection methods. The last comparison is carried out against some well-known metaheuristic algorithms for feature selection. Experiments and analysis of the results show that the proposed model is more accurate than the algorithms and methods that we compared it to. [ABSTRACT FROM AUTHOR]

Quantum-behaved Particle Swarm Optimization (QPSO) is a meta-heuristic optimization algorithm, which is widely used in many research fields and practical problems due to its flexibility and low computational cost. However, the existing QPSO algorithms and their variants still have problems such as insufficient search capabilities, lack of adaptivity, and prone to stagnation. This paper proposes a novel ensemble algorithm, ACQPSOW-HGS, based on Quantum-behaved Particle Swarm Optimization (QPSO) and Hunger Games Search (HGS). By combining three improvements and introducing three hybrid strategies, our algorithm has made a comprehensive development, effectively improving the stability and solution accuracy in a large number of test functions and the parameter identification application, which is superior compared with many existing algorithms. First, we design the Weibull distribution random number generation operator, the distance-guided adaptive control technique, and the chaotic update mechanism to deal with the weak randomness, insufficient adaptability, and susceptibility to stagnation of the original QPSO algorithm, respectively. Integrating the above improvements, ACQPSOW is proposed as an improved variant of QPSO. Second, the proposed ensemble algorithm ACQPSOW-HGS is built on ACQPSOW and HGS and combined with specific hybrid strategies to add population diversity and improve search efficiency, including the Selection-Crossover-Mutation mechanism, the elite local search mechanism, and the information exchange mechanism. Finally, the experiments, on 23 benchmark functions and the IEEE CEC 2017 test suite, demonstrate that ACQPSOW-HGS outperforms comparison algorithms in terms of convergence speed and solution accuracy through non-parametric statistical tests. Moreover, ACQPSOW-HGS was applied to the fractional-order hyper-chaotic financial system for parameter identification to illustrate the applicability and robustness in solving real-world problems. [ABSTRACT FROM AUTHOR]

Fog computing is a computer architecture consisting of fog nodes, a collection of near-user edge devices. These fog nodes collaborate to perform computational services like data retrieval, processing, storage, etc. Resource allocation is one of the most critical and challenging problems in the fog environment for industrial applications. The significant aspects to consider while allocating resources are response time, throughput, and energy consumption. The proposed work formulated the resource allocation problem as a bi-objective minimization problem. The main aim of the work is to reduce energy consumption and makespan while allocating service requests to virtual machines. The proposed technique uses the league championship algorithm to select an efficient resource for productive and resourceful service allocation of tasks in fog computing. The proposed algorithm's performance is assessed by comparing it to three well-known metaheuristic algorithms. Finally, the simulation results show that the proposed algorithm is superior in terms of makespan and energy consumption. [ABSTRACT FROM AUTHOR]

Semantic segmentation of aerial images plays a pivotal role in extracting detailed information about land cover, infrastructure, and natural features. Traditional single-channel segmentation models struggle to harness the rich information present in multi-channel aerial images, such as multi-spectral or hyperspectral data. While the DeepLabV3 + architecture has shown remarkable success in semantic segmentation tasks by exploiting multi-scale context and atrous convolutions, its performance on aerial images remains suboptimal due to the unique challenges of this domain. With the motive of addressing the difficulties in the existing semantic segmentation techniques for aerial images, the adoption of deep learning techniques is utilized. A multi-objective derived Adaptive Multichannel deeplabv3 + (AMC-Deeplabv3 +) with a new meta-heuristic algorithm called Improved Beluga whale optimization (IBWO) algorithm is proposed in this paper. Here, the hyperparameters of Multichannel deeplabv3 + are optimized by the IBWO algorithm and this model intends to set new benchmarks in the accuracy and contextual understanding of aerial image segmentation by integrating multi-channel data processing techniques and preserving spatial context. The proposed model attains improved accuracies of 98.65% & 98.72% for dataset 1 and 2 respectively and also achieves the dice coefficient of 98.73% & 98.85% respectively, with a computation time of 113.0123 s. The evolutional outcomes of the proposed model show significantly better than the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is crucial to transforming high-dimensional data into low-dimensional data. The FS approach selects influential traits and ignores the rest. This approach improves machine learning (ML) classifiers by reducing computational complexity and solution time. This empirical study presents a novel and effective methodology that uses two contemporary state-of-the-art soft-computing algorithms, the Grey Wolf Optimizer (GWO) and the Whale Optimization Algorithm (WOA). We have also created the hybrid version (hGWWO) of these two approaches as our novel, innovative scientific contribution. The baseline algorithms above have been used previously for feature selection across different domains. According to our understanding, these three algorithms are being used for the first time in glaucoma identification, particularly on the publicly available benchmark dataset, ORIGA. The rising global prevalence of glaucoma prompted this proposed methodology's focus on the illness. This illness is second only to cataracts in causing visual loss. Medical imaging professionals are examining retinal scans to diagnose glaucoma. Manual eye screening and retinal fundus imaging for confirmation of this infection require skilled ophthalmologists. The screening analysis method is time-consuming, requires experienced staff, and is subject to observational differences. In order to overcome these issues and to support the medical fraternity, an artificial intelligence-supported computer-aided clinical decision support system (CA-CDSS) is implemented in the present endeavor for confirmation of this disease from retinal fundus images. Nature-inspired computing strategies for feature selection and ML models for classification are employed to classify fundus retinal images under investigation. From the ORIGA dataset, sixty-five features were retrieved. A subset of most influential features is selected from the original dataset using three soft-computing-based FS methods. ML classifiers are trained using this portion of data and evaluated using a 70:30 technique. The suggested method yielded 96.8% accuracy, 0.981 specificity, 0.992 sensitivity, 0.969 precision, and a 0.982 F1-score. This study shows fresh initiatives with positive effects on ophthalmologists, researchers, and the public. [ABSTRACT FROM AUTHOR]

Review studies are vital for advancing knowledge in many scientific fields, including mathematics education, amid burgeoning publications. Based on an extensive consideration of existing review typologies, we conducted a meta-review and bibliometric analysis to provide a comprehensive overview of and deeper insights into review studies within mathematics education. After searching Web of Science, we identified 259 review studies, revealing a significant increase in such studies over the last five years. Systematic reviews were the most prevalent type, followed by meta-analyses, generic literature reviews, and scoping reviews. On average, the review studies had a sample size of 99, with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines commonly employed. Despite certain studies offering nuanced distinctions among review types, ambiguity persisted. Only about a quarter of the studies explicitly reported employing specific theoretical frameworks (particularly, technology, knowledge, and competence models). Co-authored publications were most common within American institutions and the leading countries are the United States, Germany, China, Australia, and England in publishing most review studies. Educational review journals, educational psychology journals, special education journals, educational technology journals, and mathematics education journals provided platforms for review studies, and prominent research topics included digital technologies, teacher education, mathematics achievement, and learning disabilities. In this study, we synthesised a range of reviews to facilitate readers' comprehension of conceptual congruities and disparities across various review types, as well as to track current research trends. The results suggest that there is a need for discipline-specific standards and guidelines for different types of mathematics education reviews, which may lead to more high-quality review studies to enhance progress in mathematics education. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) is a form of short-range wireless optical communication that uses light in the visible band for both communication and illumination. The limited switching frequencies of light-emitting diodes (LEDs), which are mostly preferred as transmitters in VLC systems, adversely affect the speed of communication. Non-orthogonal multiple access (NOMA) as an access method, and multiple-input multiple-output (MIMO) are used in VLC systems to raise the sum rate. Several methods have been proposed in the literature to determine needed power allocation coefficients for indoor MIMO-NOMA based VLC systems. In this study, meta-heuristic optimization algorithms are proposed to determine the power allocation coefficient that are subject to various constraints including fairness. The numeric results demonstrate the superiority of meta-heuristic methods over sum rate performance. In cases where the user signal strength values differ and the number of users increases, or in other words, the solution becomes more complex, the sum rate acquired by traditional methods reduces, whereas the sum rate using the suggested methods almost does not change and stays high. [ABSTRACT FROM AUTHOR]