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This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm’s efficiency. The architecture, called the Multi-Agent Exploration–Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm’s average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.

Metamaterials have unique physical properties. They are made of several elements and are structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials’ exact structure, geometry, size, orientation, and arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, amplifying, or bending them to achieve benefits not possible with ordinary materials. Microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave components, filters, and antennas with a negative refractive index utilize metamaterials. This paper proposed an improved dipper throated-based ant colony optimization (DTACO) algorithm for forecasting the bandwidth of the metamaterial antenna. The first scenario in the tests covered the feature selection capabilities of the proposed binary DTACO algorithm for the dataset that was being evaluated, and the second scenario illustrated the algorithm’s regression skills. Both scenarios are part of the studies. The state-of-the-art algorithms of DTO, ACO, particle swarm optimization (PSO), grey wolf optimizer (GWO), and whale optimization (WOA) were explored and compared to the DTACO algorithm. The basic multilayer perceptron (MLP) regressor model, the support vector regression (SVR) model, and the random forest (RF) regressor model were contrasted with the optimal ensemble DTACO-based model that was proposed. In order to assess the consistency of the DTACO-based model that was developed, the statistical research made use of Wilcoxon’s rank-sum and ANOVA tests.

This study proposes a new nature-inspired metaheuristic that mimics the behaviour of the prairie dogs in their natural habitat called the prairie dog optimization (PDO). The proposed algorithm uses four prairie dog activities to achieve the two common optimization phases, exploration and exploitation. The prairie dogs' foraging and burrow build activities are used to provide exploratory behaviour for PDO. The prairie dogs build their burrows around an abundant food source. As the food source gets depleted, they search for a new food source and build new burrows around it, exploring the whole colony or problem space to discover new food sources or solutions. The specific response of the prairie dogs to two unique communication or alert sound is used to accomplish exploitation. The prairie dogs have signals or sounds for different scenarios ranging from predator threats to food availability. Their communication skills play a significant role in satisfying the prairie dogs' nutritional needs and anti-predation abilities. These two specific behaviours result in the prairie dogs converging to a specific location or a promising location in the case of PDO implementation, where further search (exploitation) is carried out to find better or near-optimal solutions. The performance of PDO in carrying out optimization is tested on a set of twenty-two classical benchmark functions and ten CEC 2020 test functions. The experimental results demonstrate that PDO benefits from a good balance of exploration and exploitation. Compared with the results of other well-known population-based metaheuristic algorithms available in the literature, the PDO shows stronger performance and higher capabilities than the other algorithms. Furthermore, twelve benchmark engineering design problems are used to test the performance of PDO, and the results indicate that the proposed PDO is effective in estimating optimal solutions for real-world optimization problems with unknown global optima. The PDO algorithm source codes is publicly available at https://www.mathworks.com/matlabcentral/fileexchange/110980-prairie-dog-optimization-algorithm.

This paper presents a new metaheuristic algorithm by enhancing one of the recently proposed optimizers named Aquila optimizer (AO). The enhanced AO (enAO) algorithm is constructed by employing a novel modified opposition-based learning (OBL) mechanism and Nelder-Mead (NM) simplex search method. The novel modified OBL aids the AO in further diversification while the NM method increases the intensification. The enAO algorithm is first demonstrated to have more extraordinary ability than the original AO algorithm by employing challenging benchmark functions from the CEC 2019 test suite. The constructed enAO algorithm is proposed to design a PID plus second-order derivative (PIDD2) controller used in an automatic voltage regulator (AVR) system. To reach better efficiency, a novel objective function is also proposed in this paper. Initially, the proposed enAO-PIDD2 approach is demonstrated to be superior in terms of transient and frequency responses along with robustness and disturbance rejection compared to other available and best performing PID, fractional order PID (FOPID), PID acceleration (PIDA), and PIDD2 controllers tuned with different practical algorithms. Moreover, the superior performance of the proposed approach is also demonstrated comparatively using other available techniques for the AVR system reported in the last six years.

Weather prediction is paramount for many applications and scenarios, among them is agriculture. In order to efficiently irrigate the crops with the exact needed water amount, weather forecasting can be used to optimize the quantity of required irrigation water such that the crops are neither dried up nor over-irrigated. This paper proposes a Machine Learning (ML)-based weather forecasting model, which utilizes the Social Spider Algorithm-Least Square-Support Vector Machine (SSA-LS-SVM) algorithm. The simulation results are used to predict the prime weather and soil parameters such as the atmospheric temperature, pressure, and soil humidity for 24, 48, and 72 hours based on previous 39 days’ hourly data for Amman city. The predicted values showed low relative mean square errors compared with the actual values and the LS-SVM predictor.

Software-defined networking (SDN) separates the control layer from the data layer, and decisions tomanage the network are issued through a controller. The distributed SDN architecture is an effectivesolution addressing modern WAN SDN architectures and allows multiple controllers to managedifferent parts of the network to ensure efficient and stable operation. To solve the problems of highswitch migration cost, load imbalance, and inefficient load balancing in SDN multi-controller environments,we propose a deep learning-based controller load prediction switch migration (LPSM) strategythat uses a migration switch selection algorithm, target controller selection algorithm, and switchmigration decision algorithm. Then, we propose a load balancing algorithm based on this decisionalgorithm. The final experimental results show that the LPSM reduces the migration cost by 16%and 8%, respectively, compared with time-sharing switch migration (TSSM) and distributed decisionmigration (DDM) strategies, reduces load variance from 0.02 to 0.004 compared with the DDMstrategy, and improves load balancing efficiency by 27.6% compared with the TSSM strategy.

Regardless of the past research work in cloud computing some of the challenges still exist related to workload distribution in cloud data centers. Especially in the infrastructure as a service IaaS cloud model. Efficient task allocation is a crucial process in cloud data center due to the restricted number of resource and virtual machines (VM). IaaS is one of the main models of cloud computing because this model handles the backend where servicer like VM and data centers are managed. Cloud service providers should ensure high service delivery performance in such models avoiding situations such as hosts being overloaded or under loaded as this result causes VM failure and make higher network execution time. Therefore, to overcome these problems, this paper proposed an improved load balancing technique known as the HBAC algorithm which dynamically allocates resources by hybridizing the Artificial Bee Colony (ABC) algorithm with the Bat algorithm. The proposed HBAC algorithm was tested and compared with other state-of-the-art algorithms on 200-20000 even tasks by using CloudSim on standard workload format (SWF) data sets file size (200kb and 400kb). The proposed HBAC showed an improved accuracy rate in task distribution of VM in a cloud datacenter and reduced the makespan (energy level) in the datacenter. Based on the ANOVA comparison test results, a 1.98 percent improvement on accuracy or task distribution of VM occurs and 0.98 percent reduced makespan or energy level of cloud data center. The results are consistent with different services broker policies which are used during simulation process for the proposed algorithm in cloud datacenter. In future research the proposed algorithm used for predication approach for resource managements system in cloud data center.

Maha Gharaibeh,1 Mwaffaq Elheis,1 Ruba Khasawneh,1 Mamoon Al-Omari,1 Mohammad Jibril,1 Khalid Dilki,1 Eyhab El-Obeid,1 Maryam Altalhi,2 Laith Abualigah3 1Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan; 2Department of Management Information System, College of Business Administration, Taif University, Taif, 21944, Saudi Arabia; 3Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, JordanCorrespondence: Maha Gharaibeh; Laith Abualigah, Tel +96 2799716333, Email mmgharaibeh@just.edu.jo; aligah.2020@gmail.comIntroduction: Hospitalized patients with coronavirus disease (COVID-19) often undergo chest x-ray (CXR). Utilizing CXR findings could reduce the cost of COVID-19 treatment and the resultant pressure on the Jordanian healthcare system.Methods: We evaluated the association between the CXR severity score, based on the Radiographic Assessment of Lung Edema (RALE) scoring system, and outcomes of patients with COVID-19. The main objective of this work is to assess the role of the RALE scoring system in predicting in-hospital mortality and clinical outcomes of patients with COVID-19. Adults with a positive severe acute respiratory syndrome COVID-19 two reverse-transcription polymerase chain reaction test results and a baseline CXR image, obtained in November 2020, were included. The RALE severity scores were calculated by expert radiologists and categorized as normal, mild, moderate, and severe. Chi-square tests and multivariable logistic regression were used to assess the association between the severity category and admission location and clinical characteristics.Results: Based on the multivariable regression analysis, it has been found that male sex, hypertension, and the RALE severity score were significantly associated with in-hospital mortality. The baseline RALE severity score was associated with the need for critical care (P< 0.001), in-hospital mortality (P< 0.001), and the admission location (P=0.002).Discussion: The utilization of RALE severity scores helps to predict clinical outcomes and promote prudent use of resources during the COVID-19 pandemic.Keywords: COVID-19, chest radiograph, RALE score, prognosis, statistical analysis

One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems. Firstly, we introduce tent chaotic map in the initialization stage to improve the diversity of the initialization population. Secondly, an exploration factor is proposed to optimize parameters for improving the ability of exploration. Finally, a random walk strategy is proposed to enhance the exploitation capability of HHO further and help search agent jump out the local optimal. Results from systematic experiments conducted on 23 benchmark functions and the CEC2017 test functions demonstrated that the proposed method can provide a more reliable solution than other well-known algorithms.

This paper provides an in-depth literature review of the Black Hole Algorithm (BHA) which is considered as a recent metaheuristic. BHA has been proven to be very efficient in different applications. There has been several modifications and variants of this algorithm in the literature, so this work reviews various variants of the BHA. The applications of BHA in engineering problems, clustering, task scheduling, image processing, etc. have been thoroughly reviewed as well. This review article sheds lights on the pros and cons of this algorithm and enables finding a right variant of this algorithm for a certain application area. The paper concludes with an in-depth future direction.

This theoretical research study proposes a novel Chaotic Quasi-Oppositional Arithmetic Optimization Algorithm (COAOA) for thermo-economic optimization of a shell and tube condenser working with refrigerant mixtures. Arithmetic Optimization Algorithm (AOA) is a recently emerged metaheuristic algorithm considering different mathematical operators to optimize the candidate solutions over a wide range of search domains. The effectiveness the COAOA is assessed by applying it to a set of benchmark optimization problems and comparing the obtained solutions with that of the original AOA and its quasi-oppositional variant. The COAOA has been employed to acquire the minimum value of the total annual cost of the shell and tube condenser by iteratively varying nine decision variables of mass flow rate, shell diameter, the tube inside diameter, tube length, number of tube passes, tube layout, tube pitch ratio, the total number of baffles, and diameter ratio. Three different case studies are solved using different refrigerant pairs used for in-tube flow to show the proposed metaheuristic optimizer's efficiency and effectivity on real-world mixed-integer optimization problem. Optimal results retrieved for different mixture pairs with varying mass fractions are compared with each other, and parametric configuration yielding the minimum total cost is decided. Finally, a comprehensive sensitivity analysis is performed to investigate the influences of the design variables over the considered problem objective. Overall analysis results indicate that COAOA can be an excellent optimizer to obtain a shell and tube condenser's optimal configuration within a reasonable computation time.

In the last decade, the application of membrane-inspired evolutionary algorithms in real-life problems has attracted much attention due to their flexibility and parallelizability. Almost seven years have passed since the first membrane algorithms survey paper was published in 2014. Considering the importance and ongoing research on such algorithms and their applications in various disciplines, this paper presents a comprehensive review of the published literature and suggests future directions. This review aims to summarize and analyze membrane algorithms based on the used nature-inspired algorithm, membrane structure, membrane rules, and their merits and demerits. Furthermore, an extensive bibliography about their real-world applications is presented.

Solar Photovoltaic (PV) system is one of the most significant forms of renewable energy resources, and it requires accuracy to assess, design, and extraction of its parameters. Several methods have been extensively applied to mimic the nonlinearity and multi-model behavior of the PV system. However, there is no method to date that can guarantee the extracted parameter of the PV model is the most accurate one. Therefore, this paper presents a unique approach known as Hybridized Arithmetic Operation Algorithm based on Efficient Newton Raphson (HAOAENR) to experimentally extract the parameters of the single-diode and double-diode PV models at the variability of the climatic changes. Firstly, the objective function is efficiently designed to roughly predict the initial root values of the PV equation. Secondly, the Lévy flight and Brownian strategies are integrated in the four operators of AOA to thoroughly analyze the feature space of this problem. Additionally, the four operators of the AOA is divided into two phases to equilibrium between the exploration and exploitation tendencies. Furthermore, the chaotic map and robust mutation techniques are systematically employed in the beginning and halves of generations to ensure the algorithm can reach globally at few numbers iterations. Finally, a nonlinearly adjustable damping parameter of the Levenberg-Marquardt technique is linked with the NR method to replicate the fluctuation behaviours of the PV models. The experimental findings revealed that the proposed HAOAENR outperformed all other methods found in the literature, with average RMSE values close to zero values for both PV models.

Harris Hawks Optimization (HHO) algorithm was proposed recently under the metaheuristic algorithms, which can fix many problems in various domains. However, it needs to improve in local search, which may lead to a loss of diversity, stuck in a local minimum, which procures premature convergence. Two steps have been introduced in this paper to avoid these issues. Firstly, integrating Opposition-based learning (OBL) with HHO accelerates the search process and enhances the choice of better solutions, namely OHHO. Then, using reinforcement learning to enhance the research technique of OHHO, called RLOHHO. CEC 2015 and CEC 2017 benchmark functions and real engineering problems are utilized to evaluate the efficiency. Finally, the proposed versions of HHO are compared with efficient optimization algorithms. The experiment results illustrate that the RLOHHO's version achieved better solutions than the original HHO, OHHO, and other similar published algorithms in the literature.

Adoption of cutting-edge technology such as the Internet of Things (IoT) as well as Deep Learning ideas in smart agricultural system has expanded dramatically in recent years in order to ensure humanity's long-term viability. It is critical to distinguish the plant photos captured for monitoring and, as a result, disease and pest prevention. Currently, deep learning methods are employed for an efficient recognition of plant disorders. However, categorization and recognition of plant diseases is a difficult task due to the presence of low-intensity data in the image, the presence of noise in the sample, and differences in the position, chrominance, and colour similarities among both healthy and diseased plants. plant images. Hence, the overarching objective is to design the custom and efficient deep learning model that can yield an accurate identification of plant diseases under the above mentioned real time environment. In this research article, novel ensemble of residual convolutional networks and Swin transformers has been proposed. Swin Transformers (ST) is hierarchal architecture which brings the greater efficiency and flexibility at various scales and has linear computational complexity with respect to the image size. Therefore, combining the Swin Transformers and powerful Residual networks will extract the numerous deep key point features that aids for the better classification and recognition of plant diseases under different real time circumstances. PlantVillage Kaggle datasets are used for extended experimentation, and various performance measures including as accuracy, precision, recall, specificity, and F1-score are computed and analysed. To prove the superiority of the proposed model, existing architecture such as FCN-8 s, CED-Net, SegNet, DeepLabv3, Densenets and Centernets. The experimental findings show that the suggested model outperformed the other existing techniques in terms of accuracy, precision, recall, and F1-score. Also comprehensive analysis shows that the proposed model is more capable and reliable in identifying diseases in plants under real time circumstances.