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The Internet of Things (IoT) is the underlying technology that has enabled connecting daily apparatus to the Internet and enjoying the facilities of smart services. IoT marketing is experiencing an impressive 16.7% growth rate and is a nearly USD 300.3 billion market. These eye-catching figures have made it an attractive playground for cybercriminals. IoT devices are built using resource-constrained architecture to offer compact sizes and competitive prices. As a result, integrating sophisticated cybersecurity features is beyond the scope of the computational capabilities of IoT. All of these have contributed to a surge in IoT intrusion. This paper presents an LSTM-based Intrusion Detection System (IDS) with a Dynamic Access Control (DAC) algorithm that not only detects but also defends against intrusion. This novel approach has achieved an impressive 97.16% validation accuracy. Unlike most of the IDSs, the model of the proposed IDS has been selected and optimized through mathematical analysis. Additionally, it boasts the ability to identify a wider range of threats (14 to be exact) compared to other IDS solutions, translating to enhanced security. Furthermore, it has been fine-tuned to strike a balance between accurately flagging threats and minimizing false alarms. Its impressive performance metrics (precision, recall, and F1 score all hovering around 97%) showcase the potential of this innovative IDS to elevate IoT security. The proposed IDS boasts an impressive detection rate, exceeding 98%. This high accuracy instills confidence in its reliability. Furthermore, its lightning-fast response time, averaging under 1.2 s, positions it among the fastest intrusion detection systems available. [ABSTRACT FROM AUTHOR]

A lattice of integers is the collection of all linear combinations of a set of vectors for which all entries of the vectors are integers and all coefficients in the linear combinations are also integers. Lattice reduction refers to the problem of finding a set of vectors in a given lattice such that the collection of all integer linear combinations of this subset is still the entire original lattice and so that the Euclidean norms of the subset are reduced. The present paper proposes simple, efficient iterations for lattice reduction which are guaranteed to reduce the Euclidean norms of the basis vectors (the vectors in the subset) monotonically during every iteration. Each iteration selects the basis vector for which projecting off (with integer coefficients) the components of the other basis vectors along the selected vector minimizes the Euclidean norms of the reduced basis vectors. Each iteration projects off the components along the selected basis vector and efficiently updates all information required for the next iteration to select its best basis vector and perform the associated projections. [ABSTRACT FROM AUTHOR]

Many intrusion detection algorithms that use optimization have been developed and are commonly used to detect intrusions. The process of selecting features and the parameters of the classifier are essential parts of how well an intrusion detection system works. This paper provides a detailed explanation and discussion of an improved intrusion detection method for multiclass classification. The proposed solution uses a combination of the modified teaching–learning-based optimization (MTLBO) algorithm, the modified JAYA (MJAYA) algorithm, and a support vector machine (SVM). MTLBO is used with supervised machine learning (ML) to select subsets of features. Selection of the fewest features possible without impairing the accuracy of the results in feature subset selection (FSS) is a multiobjective optimization issue. This paper presents MTLBO as a mechanism and investigates its algorithm-specific, parameter-free idea. This study used the modified JAYA (MJAYA) algorithm to optimize the C and gamma parameters of the support vector machine (SVM) classifier. When the proposed MTLBO-MJAYA-SVM algorithm was compared with the original TLBO and JAYA algorithms on a well-known intrusion detection dataset, it was found to outperform them significantly. [ABSTRACT FROM AUTHOR]

The exponential growth of Internet of Things (IoT) devices has resulted in an unparalleled surge in the need for energy-efficient strategies to guarantee the sustainability and durability of IoT networks. This study article provides a thorough examination of energyefficiency optimization in Internet of Things (IoT) networks, with a specific emphasis on the creation of algorithms, methodologies, and empirical investigations. The article commences by providing an overview of the significant significance of energy efficiency in Internet of Things (IoT) networks and its direct influence on the lifetime of devices, scalability of networks, and environmental sustainability. This emphasizes the urgent need for inventive approaches to tackle the difficulties presented by resource-limited Internet of Things (IoT) devices. Subsequently, the present study undertakes an in-depth examination of existing methodologies and algorithms for optimizing energy efficiency in Internet of Things (IoT) networks. This paper presents a comprehensive examination of each category, including insights into their respective strengths, limits, and suitability for various Internet of Things (IoT) applications. This study presents innovative algorithms and strategies that are especially developed to improve energy efficiency in Internet of Things (IoT) networks. These advancements use cutting-edge technology such as machine learning, edge computing, and low-power device design. The paper provides comprehensive explanations of these methodologies, accompanied by simulations and performance assessments, to showcase their efficacy in attaining energy conservation while maintaining network dependability and service excellence. The case studies presented provide valuable perspectives on the practical use of energy-efficient technologies, demonstrating their effectiveness in reducing operating expenses, carbon emissions, and system downtime. Moreover, this study aims to discuss the many problems and unresolved research inquiries pertaining to energy-efficient Internet of Things (IoT) networks. The aforementioned points underscore the need for further investigation in several aspects, including scalability challenges, security implications, and standardization efforts. These areas of focus are crucial in order to foster the ongoing expansion and long-term viability of Internet of Things (IoT) ecosystems. [ABSTRACT FROM AUTHOR]

The significance of big data are prone to complication in solving optimization issues. In several scenarios, one requires adapting several contradictory goals and satisfies various criterions. This made the research on multi-objective optimization more vital and has become main topic. This paper presents theoretical analysis and comparative study of top ten optimization algorithms with respect to DMS. The performance analysis and study of optimization algorithms in big data streaming are explicated. Here, the top ten algorithms of optimization based on recency and popularity are considered. In addition, the performance analysis based on Efficiency, Reliability, Quality of solution, and superiority of DMS algorithm over other top 10 algorithms are examined. From analysis, the DMS provides better efficiency as it endeavours less computational effort to generate better solution, due to acquisition of both DA and MS algorithm's benefits and DMS takes less time to process a task. Moreover, the DMS needs less number of iterations in the process of optimization and helps to stop optimization process in local optimum. In addition, the DMS has better reliability as it poses the potential to handle specific level of performance. In addition, the DMS utilizes heuristic information for attaining high reliability. Moreover, the DMS produced high computation accuracy, which reveals its solution quality. From the analysis, it is noted that DMS attained improved outcomes in terms of efficiency, reliability and solution quality in contrast to other top 10 optimization algorithms. [ABSTRACT FROM AUTHOR]

Deepfakes are digital audio, video, or images manipulated using machine learning algorithms. These manipulated media files can convincingly depict individuals doing or saying things they never actually did. Deepfakes pose significant risks to our lives, including national security, financial markets, and personal privacy. The ability to create convincing deep fakes can also harm individuals' reputations and can be used to spread disinformation and fake news. As such, there is a growing need for reliable and accurate methods to detect deep fakes and prevent their harmful effects. In this paper, a hybrid convolutional neural network (CNN) and recurrent neural network (RNN) with a particle swarm optimization (PSO) algorithm is utilized to demonstrate a deep learning strategy for detecting deepfake videos. High accuracy, sensitivity, specificity, and F1 score were attained by the proposed approach when tested on two publicly available datasets: Celeb-DF and the Deepfake Detection Challenge Dataset (DFDC). Specifically, the proposed method achieved an average accuracy of 97.26% on Celeb-DF and an average accuracy of 94.2% on DFDC. The results were compared to other state-of-the-art methods and showed that the proposed method outperformed many. The proposed method can effectively detect deepfake videos, which is essential for identifying and preventing the spread of manipulated content online. [ABSTRACT FROM AUTHOR]

This paper proposes a new time-respecting graph (TRG) representation for contact sequence temporal graphs. Our representation is more memory-efficient than previously proposed representations and has run-time advantages over the ordered sequence of edges (OSE) representation, which is faster than other known representations. While our proposed representation clearly outperforms the OSE representation for shallow neighborhood search problems, it is not evident that it does so for different problems. We demonstrate the competitiveness of our TRG representation for the single-source all-destinations fastest, min-hop, shortest, and foremost paths problems. [ABSTRACT FROM AUTHOR]

At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption. [ABSTRACT FROM AUTHOR]

In this paper we develop and analyze a variational data assimilation method with efficient decoupled iterative numerical algorithms for the Stokes–Darcy equations with the Beavers–Joseph interface condition. By using Tikhonov regularization and formulating the variational data assimilation into an optimization problem, we establish the existence, uniqueness, and stability of the optimal solution. Based on the weak formulation of the Stokes–Darcy equations, the Lagrange multiplier rule is utilized to derive the first order optimality system for both the continuous and discrete variational data assimilation problems, where the discrete data assimilation is based on a finite element discretization in space and the backward Euler scheme in time. By rescaling the optimality system and then analyzing its corresponding bilinear forms, we prove the optimal finite element convergence rate with special attention paid to recovering uncertainties missed in the optimality system. To solve the discrete optimality system efficiently, three decoupled iterative algorithms are proposed to address the computational cost for both well-conditioned and ill-conditioned variational data assimilation problems, respectively. Finally, numerical results are provided to validate the proposed methods. [ABSTRACT FROM AUTHOR]

The authors consider optimization transformations of the algorithm for constructing minimal generating sets of solutions of systems of linear homogeneous equations (SLHE) over the set of natural numbers. The paper describes the features of such SLHEs, substantiates optimization transformations, and presents examples of the algorithm before and after optimization transformations. It illustrates the applying the algorithm with examples of the analysis of Petri net properties and the construction of a basic solution set in the fields of complex, real, and rational numbers and over finite fields. [ABSTRACT FROM AUTHOR]

Emerging technology selection is crucial for enterprise integration, driving innovation, competitiveness, and streamlining operations across diverse sectors like finance and healthcare. However, the decision-making process for technology adoption is often complex and fraught with uncertainties. Bipolar fuzzy sets offer a nuanced representation of uncertainty, allowing for simultaneous positive and negative membership degrees, making them valuable in decision-making and expert systems. In this paper, we introduce dynamic averaging and dynamic geometric operators under bipolar fuzzy environment. We also establish some of the fundamental crucial features of these operators. Moreover, we present a step by step mechanism to solve MADM problem under bipolar fuzzy dynamic aggregation operators. In addition, these new techniques are successfully applied for the selection of the most promising emerging technology for enterprise integration. Finally, a comparative study is conducted to show the validity and practicability of the proposed techniques in comparison to existing methods. [ABSTRACT FROM AUTHOR]

Whenever a new supervised machine learning (ML) algorithm or solution is developed, it is imperative to evaluate the predictive performance it attains for diverse datasets. This is done in order to stress test the strengths and weaknesses of the novel algorithms and provide evidence for situations in which they are most useful. A common practice is to gather some datasets from public benchmark repositories for such an evaluation. But little or no specific criteria are used in the selection of these datasets, which is often ad-hoc. In this paper, the importance of gathering a diverse benchmark of datasets in order to properly evaluate ML models and really understand their capabilities is investigated. Leveraging from meta-learning studies evaluating the diversity of public repositories of datasets, this paper introduces an optimization method to choose varied classification and regression datasets from a pool of candidate datasets. The method is based on maximum coverage, circular packing, and the meta-heuristic Lichtenberg Algorithm for ensuring that diverse datasets able to challenge the ML algorithms more broadly are chosen. The selections were compared experimentally with a random selection of datasets and with clustering by k-medoids and proved to be more effective regarding the diversity of the chosen benchmarks and the ability to challenge the ML algorithms at different levels. [ABSTRACT FROM AUTHOR]

In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm's feasibility, effectiveness, and generalizability. Through empirical validation using established datasets like diabetes and breast cancer, the algorithm consistently enhances model performance across various domains. Beyond its working and experimental analysis, DGO's wide-ranging applications in machine learning, logistics, and engineering for solving real-world problems have been highlighted. The study also considers the challenges and practical implications of implementing DGO in multiple scenarios. As optimization remains crucial in research and industry, DGO emerges as a promising avenue for innovation and problem solving. [ABSTRACT FROM AUTHOR]

The parameter extraction of Photovoltaic (PV) cell and module is a necessary to simulate and evaluate the performance of the PV system. The parameter extraction is a complex and challenging task due to its non-linear nature. Researchers are used several metaheuristic algorithms to solve the non-linear problem of parameter extraction. However, the demand for most accurate and reliable methods is increasing to get precise estimation of parameters. In this paper, a novel hybrid optimization algorithm is proposed based on the Spotted-Hyena optimization (SHO) and Ant Lion Optimization (ALO). The hybrid method is called as Spotted Hyena - Ant Lion (SH-AL) optimization. The optimization algorithm is applied in two stages. In stage 1, essential parameters are identified and extracted using SHO and passed to stage 2. In stage 2, identified parameters are optimized using ALO for accurate model of PV cell. Different type of PV cells such as thin film, mono and multi crystalline are examined under various irradiance conditions to extract the parameters. The proposed algorithm is validated by comparing the results with other algorithms and proposed algorithm is proved its superiority. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid multilevel inverter (MLI) with reduced switch count, which can generate higher output voltage level with minimum number of DC input sources. The operation of this proposed MLI is carried out with unequal DC sources to achieve the desired output voltage level. The reduced MLI output voltage is set to minimal total harmonic distortion (THD) with the help of modified JAYA (MJAYA) algorithm. A JAYA algorithm with improved steps by adopting an accelerating parameter has been proposed in this research work to obtain a faster convergence of the objective function. The MJAYA algorithm has provided the suitable switching angles for the proposed three-phase 15-level MLI and reduced the output voltage THD to 2.23%, which satisfies the standard set by IEEE-519. To prove the efficiency of this proposed modified algorithm, the comparative analysis is carried out through MATLAB program and Simulink tool using common JAYA and modified particle swarm optimisation algorithms. The performance and productivity of the proposed MLI have been investigated through simulation and experimental setups. [ABSTRACT FROM AUTHOR]

Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years, performing rapid and high-quality parameter control is still a main concern. For instance, deciding the proper population size yielding a good balance between quality of results and computing time is constantly a hard task, even more so in the presence of an unexplored optimization problem. In this paper, we propose a self-adaptive strategy based on the on-line population balance, which aims for improvements in the performance and search process on population-based algorithms. The design behind the proposed approach relies on three different components. Firstly, an optimization-based component which defines all metaheuristic tasks related to carry out the resolution of the optimization problems. Secondly, a learning-based component focused on transforming dynamic data into knowledge in order to influence the search in the solution space. Thirdly, a probabilistic-based selector component is designed to dynamically adjust the population. We illustrate an extensive experimental process on large instance sets from three well-known discrete optimization problems: Manufacturing Cell Design Problem, Set covering Problem, and Multidimensional Knapsack Problem. The proposed approach is able to compete against classic, autonomous, as well as IRace-tuned metaheuristics, yielding interesting results and potential future work regarding dynamically adjusting the number of solutions interacting on different times within the search process. [ABSTRACT FROM AUTHOR]

The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining. [ABSTRACT FROM AUTHOR]

Swarm intelligence optimization algorithm has been proved to perform well in the field of parameter optimization. In order to further improve the performance of intelligent optimization algorithm, this paper proposes an improved and adaptive tunicate swarm algorithm (IMATSA) based on tunicate swarm algorithm (TSA). IMATSA improves TSA in the following four aspects: population diversity, local search convergence speed, jumping out of local optimal position, and balancing global and local search. Firstly, IMATSA adopts Tent map and quadratic interpolation to initialize population and enhance the diversity. Secondly, IMATSA uses Golden-Sine algorithm to accelerate the convergence of local search. Thirdly, in the process of global development, IMATSA adopts Levy flight and the improved Gauss disturbance method to adaptively improves and coordinates the ability of global development and local search. Then, this paper verifies the performance of IMATSA based on 14 benchmark functions experiment, ablation experiment, parameter optimization experiments of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT), Wilcoxon signed rank test and image multi-threshold segmentation experiment with the performance metrics are convergence speed, convergence value, significance level P-value, Peak Signal-to-Noise Ratio (PSNR) and Standard Deviation (STD). Experimental results show that IMATSA performs better in three kinds of benchmark functions; each component of IMATSA has a positive effect on the performance; IMATSA performs better in parameter optimization experiments of SVM experiment and GBDT; there is significant difference between IMATSA and other algorithms by Wilcoxon signed rank test; in image segmentation, the performance is directly proportional to the number of thresholds, and compared with other algorithms, IMATSA has better comprehensive performance. [ABSTRACT FROM AUTHOR]

This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of prediction models and corresponding changes in system parts and power management based on the accumulated data. We first develop a hybrid power model to evaluate system responses under different conditions. We then build prediction models using five artificial intelligence algorithms. Among them, the light gradient boosting machine and extreme gradient boosting methods achieve the highest accuracies for predicting solar radiation and load responses, respectively. Therefore, we apply these two models to forecast solar and load responses. Third, we introduce extended-window algorithms and investigate the effects of window sizes and replacement costs on system performance. The results show that the optimal window size is one week, and the system cost is 13.57% lower than the cost of the system that does not use the extended-window algorithms. The proposed method also tends to make fewer component replacements when the replacement cost increases. Finally, we design experiments to demonstrate the feasibility and effectiveness of systems using extended-window model prediction. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for encryption of RGB color images by combining two encryption approaches: the spatial approach and the transformation approach. The proposed method uses the 3D fractional modified Henon map (3D FrMHM) and the discrete fractional Krawtchouk moments (FrDKM). We have also proposed a new hybrid optimization algorithm (H-SSAOA) to optimize the parameters of the proposed Henon map and the parameters of the Krawtchouk fractional moments. This algorithm is based on the hybridization of two metaheuristic algorithms: the "Salp Swarm Algorithm" (SSA) and the "Arithmetic Optimization Algorithm" (AOA). The simulation results reveal the optimization efficiency of the proposed hybrid algorithm H-SSAOA compared to other meta-heuristic algorithms and the efficiency of the suggested encryption method for encrypting RGB color images in terms of sensitivity to the security key and resistance to different attacks. [ABSTRACT FROM AUTHOR]

By integrating distributed generation resources with the distribution network, the stability and reliability of the distribution network will increase. Due to the advantages of microgrids and the need to implement them, as well as the high cost of installation of distributed generation resources, the existence of a comprehensive and optimal method by considering various aspects of microgrid design is felt more than ever. In this paper, an optimal method for designing microgrids with various conditions is presented. In the first stage, the design of microgrids is discussed on a multi-purpose basis, considering its economic aspects. At this stage, to make a compromise between the economic aspects, the proposed problem is modeled as two-objective functions. In the second stage, the design of distributed generation sources is done in the first level and then in the second level, the optimal placement of switches is done to determine the electrical boundaries of microgrids. In this paper, the discussion of optimal microgrid location based on economic planning using a two-level particle swarm optimization (PSO) algorithm on the standard IEEE 33 Bus network in MATLAB software was performed and for this network, three microgrids with two keys were used. [ABSTRACT FROM AUTHOR]

Given the interest and complexity, of the hybrid flow shops (HFS) problem in industry, he has been extensively considered, the HFS, is a complex combinatorial problem supported in many original world applications. We consider a hybrid flow shop FH4(P3, P2)||Cmax to applied in this paper. In this papers we attempt to optimize the makespan which refers to the last task completion time by an adequate meta-heuristic algorithm based on electromagnetism mechanism (EM). We also present analysis on the performance of the EM-algorithm adapted to HFS scheduling problems. The electromagnetism-like mechanism method gave us efficient and fair results comparing to particle swarm and genetic algorithm. [ABSTRACT FROM AUTHOR]

Wearable devices are a growing field of research that can have a wide range of applications. The energy harvester is the most common source of power for wearable devices as well as in wireless sensor networks that require a battery-free operation. However, their power is restricted; consequently, power saving is crucial for wearable devices. Finding the best schedule for the various tasks that run on the wearable device can help to reduce power consumption. This paper presents a task scheduler for wearable medical devices based on Gaining–Sharing Knowledge (GSK) algorithm. The purpose of this task scheduler is to handle the tasks of a heart rate sensor and a temperature sensor to optimize the energy consumption throughout wearable medical devices. The proposed GSK-based scheduling algorithm is assessed against the state-of-the-art technique. The data used in our experiments are collected from an in-lab prototype. [ABSTRACT FROM AUTHOR]

Cloud computing is considered a key Internet technology. Cloud providers offer services through the Internet, such as infrastructure, platforms, and software. The scheduling process of cloud providers' tasks concerns allocating clients' tasks to providers' resources. Several mechanisms have been developed for task scheduling in cloud computing. Still, these mechanisms need to be optimized for execution time and makespan. This paper presents a new task-scheduling mechanism based on Discrete Prey–Predator to optimize the task-scheduling process in the cloud environment. The proposed Discrete Prey–Predator mechanism assigns each scheduling solution survival values. The proposed mechanism denotes the prey's maximum surviving value and the predator's minimum surviving value. The proposed Discrete Prey–Predator mechanism aims to minimize the execution time of tasks in cloud computing. This paper makes a significant contribution to the field of cloud task scheduling by introducing a new mechanism based on the Discrete Prey–Predator algorithm. The Discrete Prey–Predator mechanism presents distinct advantages, including optimized task execution, as the mechanism is purpose-built to optimize task execution times in cloud computing, improving overall system efficiency and resource utilization. Moreover, the proposed mechanism introduces a survival-value-based approach, as the mechanism introduces a unique approach for assigning survival values to scheduling solutions, differentiating between the prey's maximum surviving value and the predator's minimum surviving value. This improvement enhances decision-making precision in task allocation. To evaluate the proposed mechanism, simulations using the CloudSim simulator were conducted. The experiment phase considered different scenarios for testing the proposed mechanism in different states. The simulation results revealed that the proposed Discrete Prey–Predator mechanism has shorter execution times than the firefly algorithm. The average of the five execution times of the Discrete Prey–Predator mechanism was 270.97 s, while the average of the five execution times of the firefly algorithm was 315.10 s. [ABSTRACT FROM AUTHOR]

Inspired by the migration and reproduction of species in nature to explore suitable habitats, this paper proposed a new swarm intelligence algorithm called the Migration and Reproduction Algorithm (MARA). This new algorithm discusses how to transform the behavior of an organism looking for a suitable habitat into a mathematical model, which can solve optimization problems. MARA has some common features with other optimization methods such as particle swarm optimization (PSO) and the fireworks algorithm (FWA), which means MARA can also solve the optimization problems that PSO and FWA are used to, namely, high-dimensional optimization problems. MARA also has some unique features among biology-based optimization methods. In this paper, we articulated the structure of MARA by correlating it with natural biogeography; then, we demonstrated the performance of MARA on sets of 12 benchmark functions. In the end, we applied it to optimize a practical problem of power dispatching in a multi-microgrid system that proved it has certain value in practical applications. [ABSTRACT FROM AUTHOR]

Spam emails have become a pervasive issue in recent years, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. In this paper, we introduce a novel approach to spam email detection by presenting significant advancements to the Dandelion Optimizer (DO) algorithm. The DO is a relatively new nature-inspired optimization algorithm inspired by the flight of dandelion seeds. While the DO shows promise, it faces challenges, especially in high-dimensional problems such as feature selection for spam detection. Our primary contributions focus on enhancing the DO algorithm. Firstly, we introduce a new local search algorithm based on flipping (LSAF), designed to improve the DO's ability to find the best solutions. Secondly, we propose a reduction equation that streamlines the population size during algorithm execution, reducing computational complexity. To showcase the effectiveness of our modified DO algorithm, which we refer to as the Improved DO (IDO), we conduct a comprehensive evaluation using the Spam base dataset from the UCI repository. However, we emphasize that our primary objective is to advance the DO algorithm, with spam email detection serving as a case study application. Comparative analysis against several popular algorithms, including Particle Swarm Optimization (PSO), the Genetic Algorithm (GA), Generalized Normal Distribution Optimization (GNDO), the Chimp Optimization Algorithm (ChOA), the Grasshopper Optimization Algorithm (GOA), Ant Lion Optimizer (ALO), and the Dragonfly Algorithm (DA), demonstrates the superior performance of our proposed IDO algorithm. It excels in accuracy, fitness, and the number of selected features, among other metrics. Our results clearly indicate that the IDO overcomes the local optima problem commonly associated with the standard DO algorithm, owing to the incorporation of LSAF and the reduction in equation methods. In summary, our paper underscores the significant advancement made in the form of the IDO algorithm, which represents a promising approach for solving high-dimensional optimization problems, with a keen focus on practical applications in real-world systems. While we employ spam email detection as a case study, our primary contribution lies in the improved DO algorithm, which is efficient, accurate, and outperforms several state-of-the-art algorithms in various metrics. This work opens avenues for enhancing optimization techniques and their applications in machine learning. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed optimization problem of networked Euler–Lagrange (EL) systems with actuator faults and unknown model parameters. In order to solve this issue, some event‐triggered fault‐tolerant (ETFT) optimal coordination algorithms with the mechanism of intermittent communication and intermittent controller update are designed for the first time to compensate for the impact of physical faults on the system, and reduce communication costs and control resource consumption. For practical applications, it is difficult for the system to have a secure environment if there exist malicious attacks blocking network channels among agents. Therefore, the resilient ETFT optimal coordination with intermittent communication subject to asynchronous denial‐of‐service (DoS) attacks is considered. Especially, auxiliary systems with adaptive gains are introduced in the algorithms due to the unavailability of the EL model parameters, which also enable virtual data exchange between agents and protect actual state information. Note that the algorithms are fully distributed as the global topology information is not required. Finally, the feasibility of the proposed algorithms is verified through numerical simulation of examples. [ABSTRACT FROM AUTHOR]

The no-free-lunch (NFL) theory has become the main reason of developing new metaheuristics in decades. Besides, the strict dimension mapping has been implemented in many population-based metaheuristics, especially the swarm-based metaheuristics. Regarding this problem, this paper proposes a new swarm-based metaheuristics called focus and shake algorithm (FSA). FSA has novel approach in the dimension mapping between the agent and its reference during the directed motion. It combines the strict dimension mapping called as focus approach and the randomized dimension mapping called as shake approach to enhance its exploration ability. FSA deploys two directed motions based on two references. The first reference is constructed based on the balance mixture between two finer agents while the second reference is constructed based on the balance mixture of the finest agent and a randomly picked agent. In the competing assessment, FSA competes with five brand new swarm-based metaheuristics: migration algorithm (MA), total interaction algorithm (TIA), lyrebird optimization algorithm (LOA), osprey optimization algorithm (OOA), and kookaburra optimization algorithm (KOA). The result exhibits that FSA is finer than MA, TIA, LOA, OOA, and KOA in 19, 21, 21, 19, and 20 functions out of 23 functions respectively. The result also shows that the superiority of FSA takes place in both unimodal and multimodal problems. In the future, the cross-dimension mapping can be more explored to develop finer swarm-based metaheuristics. [ABSTRACT FROM AUTHOR]

This paper introduces a novel metaheuristic named the stochastic shaking algorithm (SSA), which is rooted in swarm intelligence principles. The innovation lies in its unique utilization of iteration for selecting references during guided searches through a stochastic approach. The optimization process involves two sequential steps: the primary reference in the first step is the finest swarm member, while in the second step, it is the mean of all finer swarm members plus the finest one. This primary reference is then combined with a randomly chosen solution within the space, serving as the secondary reference. SSA undergoes evaluation in two contexts. The first involves assessing its performance using a set of 23 classic functions as a theoretical use case. The second involves tackling the economic load dispatch problem (ELD), a practical use case featuring a system with 13 generators of various energy resources. The study compares SSA against five other metaheuristics--One to One Based Optimization (OOBO), Kookaburra Optimization Algorithm (KOA), Language Education Optimization (LEO), Total Interaction Algorithm (TIA), and Walrus Optimization Algorithm (WaOA). Results indicate SSA's superiority over OOBO, KOA, LEO, TIA, and WaOA in 21, 13, 11, 16, and 14 functions out of 23 functions, respectively. Additionally, the evaluation of the economic load dispatch problem reveals intense competition among the six metaheuristics. [ABSTRACT FROM AUTHOR]

Ribonucleic acid (RNA) structure alignment is an important problem in computational biology to identify structural similarity of RNAs. Obtaining an efficient method for this problem is challenging due to the high computational time for the optimal solution and the low accuracy of a heuristic solution. In this paper, an efficient algorithm is proposed based on a mathematical model called longest arc-preserving common subsequence. The proposed algorithm uses a heuristic technique and high-performance computing to optimize the solution of RNA structure alignment, both in terms of the running time and the accuracy of the output. Extensive experimental studies on a multicore system are conducted to show the effectiveness of the proposed algorithm on two types of data. The first is simulated data that consists of 450 comparisons of RNA structures, while the second is real biological data that consists of 357 comparisons of RNA structures. The results show that the proposed algorithm outperforms the best-known heuristic algorithm in terms of execution time, with a percentage improvement of 71% and increasing the length of the output, i.e., accuracy, by approximately 45% in all studied cases. Finally, future approaches are discussed. [ABSTRACT FROM AUTHOR]

Let N = {n1, n2, ..., nk} be a finite set of positive numbers. The problem of finding the minimal number of additions required to compute all elements of N starting from 1 (called the addition sequence problem) is NP-complete. It is equivalent to finding the minimum number of multiplications needed to compute a group exponentiation gn1, gn2, ..., gnk, where g is an element in a group. This paper aims to propose a new metaheuristic algorithm using a simulated annealing strategy to generate a short addition sequence. The performance of the proposed algorithm is measured by considering two parameters: The size of N and the domain of ni,1 ≤ i ≤ k. The proposed algorithm is a new trade-off between the length of the generated addition sequence and the average running time of generating addition sequences. It sometimes produces longer addition sequences than exact algorithms that are slower, and it is slower than suboptimal algorithms that produce longer addition sequences. [ABSTRACT FROM AUTHOR]

The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees' individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in the simplest possible way, leading to slow and premature convergence similar to other iterative algorithms. This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to rectify the abovementioned deficiencies. The SEB-ChOAs' performance is evaluated on 23 standard benchmarks, 20 benchmarks of IEEE CEC-2005, 10 cases of IEEE CEC06-2019 test-suite, and 12 constrained real-world engineering problems of IEEE CEC-2020. The SEB-ChOAs are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the most well-known optimization algorithms, Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), Henry Gas Solubility Optimization (HGSO), as almost novel optimization algorithms, and jDE100 and DISHchain1e+12, as winners of IEEE CEC06-2019 competition, and also EBOwithCMAR and CIPDE as superior secondary optimization algorithms. The SEB-ChOAs reached the first rank among almost all benchmarks and demonstrated very competitive results compared to jDE100 and DISHchain1e+12 as the best-performing optimizers. Statistical evidence shows that the SEB-ChOA outperforms the PSO, GA, SMA, MPA, ALO, and HGSO optimizers while producing results comparable to those of the jDE100 and DISHchain1e+12 algorithms. [ABSTRACT FROM AUTHOR]

Many recent swarm-based metaheuristics are trapped in the exploitation of the highest quality as the main or the only reference and the neighbourhood search with the reduction of local search space during the iteration. Regarding to this issue, this paper introduces a novel metaheuristic called swarm space hopping algorithm (SSHA). SSHA consists of three searches. First, a directed search toward the highest quality is performed. Second, the directed search toward the resultant of better agents or away from the other agent is performed. Third, the arithmetic crossover between the agent and a randomized solution in the first half or second half of space is performed. In this work, three evaluations are performed to assess the performance of SSHA. The first evaluation is the benchmark evaluation to compare the performance of SSHA with other recent metaheuristics: northern goshawk optimization (NGO), zebra optimization algorithm (ZOA), clouded leopard optimization (CLO), osprey optimization algorithm (OOA), and total interaction algorithm (TIA). The result exhibits that SSHA is better than NGO, ZOA, CLO, OOA, and TIA in 21, 20, 17, 17, and 21 functions. In the second evaluation, the individual search evaluation to compare the contribution between the first and second searches is performed, with the result that the second search outperforms the first search. The third evaluation is performed to assess the contribution of the third search in the optimization process, and the result shows that the contribution of the third search is significant only in three functions. [ABSTRACT FROM AUTHOR]

Metaheuristic optimization algorithms (MHA) play a significant role in obtaining the best (optimal) values of the system's parameters to improve its performance. This role is significantly apparent when dealing with systems where the classical analytical methods fail. Fractional-order (FO) systems have not yet shown an easy procedure to deal with the determination of their optimal parameters through traditional methods. In this paper, a recent, systematic. And comprehensive review is presented to highlight the role of MHA in obtaining the best set of gains and orders for FO controllers. The systematic review starts by exploring the most relevant publications related to the MHA and the FO controllers. The study is focused on the most popular controllers such as the FO-PI, FO-PID, FO Type-1 fuzzy-PID, and FO Type-2 fuzzy-PID. The time domain is restricted in the articles published through the last decade (2014:2023) in the most reputed databases such as Scopus, Web of Science, Science Direct, and Google Scholar. The identified number of papers, from the entire databases, has reached 850 articles. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was applied to the initial set of articles to be screened and filtered to end up with a final list that contains 82 articles. Then, a thorough and comprehensive study was applied to the final list. The results showed that Particle Swarm Optimization (PSO) is the most attractive optimizer to the researchers to be used in the optimal parameters identification of the FO controllers as it attains about 25% of the published papers. In addition, the papers that used PSO as an optimizer have gained a high citation number despite the fact that the Chaotic Atom Search Optimization (ChASO) is the highest one, but it is used only once. Furthermore, the Integral of the Time-Weighted Absolute Error (ITAE) is the best nominated cost function. Based on our comprehensive literature review, this appears to be the first review paper that systematically and comprehensively addresses the optimization of the parameters of the fractional-order PI, PID, Type-1, and Type-2 fuzzy controllers with the use of MHAs. Therefore, the work in this paper can be used as a guide for researchers who are interested in working in this field. [ABSTRACT FROM AUTHOR]

In many seasonal industries, customer demands are constantly changing over time, and accordingly the facility layout should be re-optimized in a timely manner to adapt to changing material handling patterns among manufacturing departments. This paper investigates the aperiodic facility layout problem (AFLP) that involves arranging facilities layout and re-layout aperiodically in a dynamic manufacturing environment during a given planning horizon. The AFLP is decomposed into a master problem and a combination set of static facility layout problems (FLPs, the slave problems) without loss of optimality, and all problems are formulated as mixed-integer linear programming (MILP) models that can be solved by MIP solvers for small-sized problems. An exact backward dynamic programming (BDP) algorithm with a computational complexity of O(n2) is developed for the master problem, and an improved linear programming based problem evolution algorithm (PEA-LP) is developed for the traditional static FLP. Computational experiments are conducted on two new problems and twelve well-known benchmark problems from the literature, and the experimental results show that the proposed solution approach is promising for solving the AFLP with practical sizes of problem instances. In addition, the improved PEA-LP found new best solutions for five benchmark problems. [ABSTRACT FROM AUTHOR]

The gas centrifuge process is widely used in the world to separate binary and multi component mixtures of isotopes. The majority of a plant cost is related to the number of centrifuges in a cascade. The cascade should be built so that it uses the fewest possible centrifuges for a given product and waste concentrations. Minimizing the number of centrifuges and the total flow rates, is a key point in designing and optimizing isotope separation cascades. The main purpose of this paper is to present a novel swarm intelligence based algorithm to solve these kinds of problems. This novel algorithm, called the Horse Herd Optimization Algorithm (HOA), is inspired by the behavioral patterns of horses in their habitats. In this work, it is demonstrated that the suggested algorithm can solve complicated multidimensional problems. HOA is tested by some test functions of high-dimensions and the results are compared with the strongest available optimization algorithms. In next, the proposed algorithm is used to optimize gas centrifuge cascades for the separation of binary and multicomponent mixtures of isotopes in several cases. Also, ideal and optimum cascades are compared in different separating regimes of the stages, using this method. Considering the performance of HOA in solving multidimensional problems, this algorithm is proposed for the optimization of long cascades. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel memetic algorithm (MA) for the solution of the resource leveling problem (RLP). The evolutionary framework of the MA is based on integration of a genetic algorithm and simulated annealing methods along with a resource leveling heuristic. The main objective of the proposed algorithm is to integrate complementary strengths of different optimization methods and incorporate the individual learning as a separate process for achieving a successful optimization method for the RLP. The performance of the MA is compared with the state-of-the-art leveling methods. For small instances up to 30 activities, mixed-integer linear models are presented for two leveling metrics to provide a basis for performance evaluation. The computational results indicate that the new integrated framework of the MA outperforms the state-of-the-art leveling heuristics and meta-heuristics and provides a successful method for the RLP. The limitations of popular commercial project management software are also illustrated along with the improvements achieved by the MA to reveal potential contributions of the proposed integrated framework in practice. [ABSTRACT FROM AUTHOR]

Tunicate Swarm Algorithm (TSA) is a new bio-based optimization technique that has proven not only to be able to compete with other methods but has also shown successful performance in classic design engineering problems/benchmark test problems. However, like some population-based methods, TSA tends to be trapped in local optima, converging to global optima in a long time, unbalanced exploitation/exploration, and the inability to effectively solve high-capacity engineering problems. In this paper, the M-TSA, which is a Modified version of the TSA, is proposed to overcome such problems. M-TSA was developed in three steps. The first is the new movement strategy that improves the movement of tunicates with a spiral movement, the second is the new herd strategy that improves the herd movement of tunics with the Levy movement, and the third is the consideration of the FAD effect. In this study, the efficiency and robustness of the M-TSA algorithm is tested on the CEC'17 test suite, six real-life design engineering problems, and two complex power system engineering problems. The test results were compared with other techniques reported in the literature and with the original TSA. Comparing the results from the M-TSA technique with other techniques proves the effectiveness of M-TSA with better exploration/exploitation balance and optimal solution finding. In this paper, MATLAB 2020b software is used for optimization problems simulation. [ABSTRACT FROM AUTHOR]

This paper introduces a new metaheuristic algorithm called Migration Algorithm (MA), which is helpful in solving optimization problems. The fundamental inspiration of MA is the process of human migration, which aims to improve job, educational, economic, and living conditions, and so on. The mathematical modeling of the proposed MA is presented in two phases to empower the proposed approach in exploration and exploitation during the search process. In the exploration phase, the algorithm population is updated based on the simulation of choosing the migration destination among the available options. In the exploitation phase, the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions. MA’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite. In addition, MA’s results are compared with the performance of twelve well-known metaheuristic algorithms. The optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems. The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions. Also, the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications. [ABSTRACT FROM AUTHOR]

In this paper, the object of the study is determining air traffic scheduling management by optimizing mathematical models. The problem in this study is that the COVID-19 pandemic has significantly disrupted air traffic, resulting in changes to regulations, travel restrictions and a decrease in passenger demand. One of the problems that must be resolved is how to organize and adapt flight schedules to current conditions by focusing on mathematical models that are used to optimize or increase the efficiency of managing flight schedules or air traffic. Mathematical models can help find ways to optimize the use of available resources, such as airport capacity, flight routes and flight frequency. The results obtained in this research are a mathematical model that specifically takes into account the variables involved in setting air traffic schedules during the COVID-19 pandemic so that capacity limits at airports and airspace are always normal. An optimization model was developed from previous research, namely the model that takes into account ground and air delays as well as the use of alternative paths and avoids deviations from the initial more accurate flight plan, which overall indicates that the maximum time and maximum distance values for each item have been optimized to achieve better values. This research has the novelty of producing a mathematical model using variables, objective functions, capacity limits, flight structure limits and variable domains, which then produces an algorithm with data input processes, determining optimization models, determining variables, determining objective functions, determining problems. The results of this model can be recommended to airlines in scheduling flights during the pandemic [ABSTRACT FROM AUTHOR]

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Differential evolution (DE) is a proficient optimizer and has been broadly implemented in real life applications of various fields. Several mutation based adaptive approaches have been suggested to improve the algorithm efficiency in recent years. In this paper, a novel self-adaptive method called SaMDE has been designed and implemented on the mutation-based modified DE variants such as modified randomized localization-based DE (MRLDE), donor mutation based DE (DNDE), and sequential parabolic interpolation based DE (SPIDE), which were proposed by the authors in previous research. Using the proposed adaptive technique, an appropriate mutation strategy from DNDE and SPIDE can be selected automatically for the MRLDE algorithm. The experimental results on 50 benchmark problems taken of various test suits and a real-world application of minimization of the potential molecular energy problem validate the superiority of SaMDE over other DE variations. [ABSTRACT FROM AUTHOR]

Featuring agile controllability, high-level autonomy, and powerful field operational advantages, autonomous surface vessels (ASVs) are gaining increasing attention worldwide. Central to the control of ASVs, path planning is one of the key technologies that ensure the navigation safety. However, many of the previous works to solve the path planning problem are to find a collision-free and shortest path, but the solutions are not satisfied by the safety requirements and the non-holonomic constraints of ASVs. This paper expands on our previous work on the improved artificial fish algorithm (IAFSA) to address these challenges. The expanding technique is introduced to modify the grid cost, and a B-spline-based path smoother is applied to the algorithm to enhance its feasibility in cooperating with the ASV controller. Simulation experiments have been conducted to illustrate the effectiveness of the proposed method. Moreover, the new algorithm is tested in the USV control system model in a practical environment. The simulation results have demonstrated that the proposed method is more efficient than the other state-of-the-art algorithms, and the simulation with the ASV model illustrated its excellent performance cooperating with the control system. Therefore, the proposed method can be considered as a reliable algorithm to solve the path planning problem of ASVs. [ABSTRACT FROM AUTHOR]

The popularity of video services such as video call or video on-demand has made it impossible for people to live without them in their daily lives. It can be anticipated that the explosive growth of vehicular communication owing to the widespread use of in-vehicle video infotainment applications in the future will result in increasing fragmentation and congestion of the wireless transmission spectrum. Accordingly, effective bandwidth management algorithms are demanded to achieve efficient communication and stable scalability in next-generation vehicular networks. To the best of our current knowledge, a noticeable gap remains in the existing literature regarding the application of the latest advancements in network communication technologies. Specifically, this gap is evident in the lack of exploration regarding how cutting-edge technologies can be effectively employed to optimize bandwidth allocation, especially in the realm of video service applications within the forthcoming vehicular networks. In light of this void, this paper presents a seamless integration of cutting-edge 6G communication technologies, such as terahertz (THz) and visible light communication (VLC), with the existing 5G millimeter-wave and sub-6 GHz base stations. This integration facilitates the creation of a network environment characterized by high transmission rates and extensive coverage. Our primary aim is to ensure the uninterrupted playback of real-time video applications for vehicle users. These video applications encompass video conferencing, live video, and on-demand video services. The outcomes of our simulations convincingly indicate that the proposed strategy adeptly addresses the challenge of bandwidth competition among vehicle users. Moreover, it notably boosts the efficient utilization of bandwidth from less crowded base stations, optimizes the fulfillment of bandwidth prerequisites for various video applications, and elevates the overall video quality experienced by users. Consequently, our findings serve as a successful validation of the practicality and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid nature-inspired metaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimization during the optimization process, leveraging their respective strengths to improve performance. To improve randomness, the hybrid approach uses two high-quality pseudorandom number generators—the 64-bit and 32-bit versions of the SIMD-Oriented Fast Mersenne Twister. The effectiveness of the hybrid algorithm is evaluated on the NP-hard Container Relocation Problem, focusing on a test set of restricted Container Relocation Problems with higher complexity. The results show that the hybrid algorithm outperforms the individual Genetic Algorithm and the African Buffalo Optimization, which use standard pseudorandom number generators. The adaptive switch method allows the algorithm to adapt to different optimization problems and mitigate problems such as premature convergence and local optima. Moreover, the importance of pseudorandom number generator selection in metaheuristic algorithms is highlighted, as it directly affects the optimization results. The use of powerful pseudorandom number generators reduces the probability of premature convergence and local optima, leading to better optimization results. Overall, the research demonstrates the potential of hybrid metaheuristic approaches for solving complex optimization problems, which makes them relevant for scientific research and practical applications. [ABSTRACT FROM AUTHOR]

We propose in this paper a new minimization algorithm based on a slightly modified version of the scalar auxialiary variable (SAV) approach coupled with a relaxation step and an adaptive strategy. It enjoys several distinct advantages over popular gradient based methods: (i) it is unconditionally energy diminishing with a modified energy which is intrinsically related to the original energy, and thus no parameter tuning is needed for stability; (ii) it allows the use of large step-sizes which can effectively accelerate the convergence rate. We also present a convergence analysis for some SAV based algorithms, which include our new algorithm without the relaxation step as a special case. We apply our new algorithm to several illustrative and benchmark problems and compare its performance with several popular gradient based methods. The numerical results indicate that the new algorithm is very robust, and its adaptive version usually converges significantly faster than those popular gradient decent based methods. [ABSTRACT FROM AUTHOR]

This paper presents the minimization of total harmonic distortion in a seven-level cascaded H-bridge multilevel inverter with resistive load using the teaching–learning-based optimization algorithm. The minimization of Total Harmonic Distortion (THD)is a challenging optimization problem due to the fact that nonlinear equations are involved. Recently, bio-inspired algorithms have become very popular approaches to solving various optimization problems in different areas of engineering. For this reason, the results obtained with the Teaching–Learning-Based Optimization (TLBO)algorithm were compared with three other popular bio-inspired algorithms, the genetic algorithm, differential evolution, and particle swarm optimization. The comparative analysis, conducted by sweeping the modulation index, made it possible to obtain graphs and data on the behavior of the four analyzed algorithms. Finally, it was concluded that the TLBO algorithm is very effective and is able to solve the THD-minimization problem. Its main advantage over the other algorithms is the fact that it does not require control parameters for its correct operation in the solution of the problem. [ABSTRACT FROM AUTHOR]

This study examines the research climate on machine learning applications in renewable energy (MLARE). Therefore, the publication trends (PT) and bibliometric analysis (BA) on MLARE research published and indexed in the Elsevier Scopus database between 2012 and 2021 were examined. The PT was adopted to deduce the major stakeholders, top-cited publications, and funding organizations on MLARE, whereas BA elucidated critical insights into the research landscape, scientific developments, and technological growth. The PT revealed 1218 published documents comprising 46.9% articles, 39.7% conference papers, and 6.0% reviews on the topic. Subject area analysis revealed MLARE research spans the areas of science, technology, engineering, and mathematics among others, which indicates it is a broad, multidisciplinary, and impactful research topic. The most prolific researcher, affiliations, country, and funder are Ravinesh C. Deo, National Renewable Energy Laboratory, United States, and the National Natural Science Foundation of China, respectively. The most prominent journals on the top are Applied Energy and Energies, which indicates that journal reputation and open access are critical considerations for the author's choice of publication outlet. The high productivity of the major stakeholders in MLARE is due to collaborations and research funding support. The keyword co-occurrence analysis identified four (4) clusters or thematic areas on MLARE, which broadly describe the systems, technologies, tools/technologies, and socio-technical dynamics of MLARE research. Overall, the study showed that ML is critical to the prediction, operation, and optimization of renewable energy technologies (RET) along with the design and development of RE-related materials. [ABSTRACT FROM AUTHOR]

Cloud computing is a rapidly advancing paradigm that enables users to access various services and resources anytime, anywhere. With this advancement, security has become a major concern for business organisations and individuals, and hence, it is essential to ensure that the services are provided with high data security. Numerous researches have focused on devising effective techniques to enhance data security. However, with the increasing connectivity, security still remains to be a major challenge. This paper devises a novel data protection scheme in the cloud by using the Twofish encryption algorithm and a key generation scheme with the Bald Eagle Pelican Optimization (BEPO) Algorithm. The proposed Twofish+BEPO_KeyGen is implemented in various phases, like initialization, registration, key generation, data encryption, authentication, validation and data sharing, and data decryption. Here, the Twofish algorithm is used to encrypt the data that has to be outsourced to the cloud, and for encryption, the security key required is generated by the BEPO algorithm. The efficacy of the Twofish+BEPO_KeyGen approach is examined by considering metrics, like memory usage, validation time, normalized variance, and conditional privacy, and is detected to have achieved values of 76.3 MB, 37.278 s, 1.665, and 0.926, correspondingly. [ABSTRACT FROM AUTHOR]

This paper presents two hybrid optimization methods known as PSOHHO and DPSOHHO optimization algorithms. In the first method, using a number of formulae, the top populations are exchanged between the two algorithms and a new population is created and in the second method, we adopted the parallel optimization and optimized its performance. In this method, unlike other parallel methods, the population does not remain constant. With this ability, the strengths of an algorithm can be used to compensate for the weaknesses of the other algorithm. In these methods, no changes are made to the algorithms. The main goal is to use existing algorithms. These methods attain the optimal solution in the shortest time possible. Two algorithms of particle swarm optimization (PSO) and Harris Hawks's optimization (HHO) are used to present this method and two truss samples and CEC209 are considered to confirm the performance of this method. Based on the results, these methods have rapid convergence speed and acceptable results compared to other methods. [ABSTRACT FROM AUTHOR]