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In this study, we utilize two metaheuristic algorithms, particle swarm optimization, and biogeography-based optimization, to select and scale ground motion (GM) records for use in the time history analysis of structures. This method ensures that there is no alteration to the phase or shape of the response spectra of the records. The proposed methodology demonstrates an ability to search through hundreds of earthquake records and propose a combination of 11 record pairs and scaling factors, resulting in a mean spectrum that aligns with the target spectrum. We applied the proposed research to two sites in separate geographical regions in the United States: Memphis and San Francisco, and we followed the ASCE 7-22 procedure. Selected ground motions underwent scaling adjustments represented by scalar values in a user-defined range. Furthermore, we present error metrics, comparing the target spectrum with the mean spectrum derived from the selected records. To showcase the effectiveness of our approach, we conducted a comparative analysis against results obtained from PEER-NGA methodology. The outcomes show that the methodology can be viewed as an effective and reliable approach for obtaining appropriate GM records for the time history analysis of structures. [ABSTRACT FROM AUTHOR]

Grinding is one of the most widely employed machining processes in manufacturing. Achieving a successful grinding process characterized by low fault rates and short cycle times can significantly improve overall productivity and process efficiency. Nonetheless, finding optimal values for grinding process parameters is challenging due to complex underlying dynamics. Therefore, this work proposes a data-driven system that exploits various machine learning techniques and metaheuristic optimization algorithms to optimize grinding process parameters. Using data collected from grinding processes, the proposed system constructs a machine learning-based fault detection model and employs that model to define variable range constraints. In addition, a Gaussian process-based cycle time estimation model is developed. Process parameter optimization is performed using various metaheuristic algorithms based on the aforementioned methods. Experiments with actual internal cylindrical grinding process data have proven the proposed system's effectiveness during process parameter optimization. Furthermore, real-world validation data verifies the final optimization solution, reducing the fault rate and process cycle time by 77.83% and 17.64%, respectively. In-depth interviews with six domain experts in the grinding process also verify the proposed system's validity and real-world applicability. The proposed data-driven system is expected to bring substantial improvements in process productivity, especially when applied to manufacturing sites in practice. [ABSTRACT FROM AUTHOR]

In this paper, single-diode model (SDM) and double-diode model (DDM) parameters of the French RTC solar cell and the Photowatt PWP 201 photovoltaic (PV) module were extracted by combining five metaheuristic algorithms with three simulation current calculation methods (i.e., approximation method, Lambert W method and Newton–Raphson method), respectively. It was found that the parameter-extraction accuracies of the Lambert W (LW) method and the Newton–Raphson (NR) method are always approximately equal and higher than that of the approximation method. The best RMSEs (root mean square error) obtained by using the LW or the NR method on the solar cell and the PV module are 7.72986 × 10−4 and 2.05296 × 10−3 for SDM parameter extraction and 6.93709 × 10−4 and 1.99051 × 10−3 for DDM parameter extraction, respectively. The latter may be the highest parameter-extraction accuracy reported on the solar cell and the PV module so far, which is due to the adoption of more reasonable DDM parameter boundaries. Furthermore, the convergence curves of the LW and the NR method basically coincide, with a convergence speed faster than that of the approximation method. The robustness of a parameter-extraction method is mainly determined by the metaheuristic algorithm, but it is also affected by the simulation current calculation method and the parameter-extraction object. In a word, the approximation method is not suitable for application in PV-model parameter extraction because of incorrect estimation of the simulation current and the RMSE, while the LW and NR methods are suitable for the application for accurately calculating the simulation current and RMSE. In terms of saving computation resources and time, the NR method is superior to the LW method. [ABSTRACT FROM AUTHOR]

In the current network security framework, Intrusion Detection Systems (IDSs) happen to be among the major players in ensuring that the network activity is being monitored round the clock for any intrusions which may occur. The rising degree of cyber threats’ intricacy enforces the constant development of IDS methodologies to maintain effectiveness in detecting and reversing the emergence of any extra risks. Therefore, to settle the matter featured by, this research studies try to incorporate the most powerful metaheuristic algorithms, Lion Optimization Algorithm (LOA) and Grey Wolf Optimizer (GWO) in particular, to develop better detection accuracy and efficiency. The core obstacle recognized in this article is the fact that many systems of IDS send out false alarms and their mechanisms of detection of the true anomalies need to be improved immensely. In a nutshell, the change would unveil a fresh way of using LOA and GWO using them to promote the enhancement of internet defences systems in real-time. These schemes can discover previously unknown weaknesses or stealthy attacks. The core of this undertaking would consist in the conception and implementing of a Hybrid Network Intrusion Detection System, which will be created by blending the Lion Optimization Feature Selection (LOFS) and GWO smelters, denoted as LOFSGWO. Critically, the main purpose is to incorporate the GWO as a tool in the operations to cut down the dangerous parameters favourable towards an intrusion mechanism in the framework of a Hybrid CNN-LSTM Deep Learning system. Model tests reveal over 99.26% accuracy of low negative samples into out of a box that are served as testing as well as NSL-KDD dataset, which are similar to the simulation of WUSTL-EOM 2020 system. The obtained outcomes verify the relevance and efficiency of the suggested strategy, which may be used in the resolution of the issues faced in a network security today.

Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. This paper demonstrates the usefulness of such algorithms for solving a variety of challenging optimization problems in statistics using a nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA). This algorithm was proposed by one of the authors and its superior performance relative to many of its competitors had been demonstrated in earlier work and again in this paper. The main goal of this paper is to show a typical nature-inspired metaheuristic algorithmi, like CSO-MA, is efficient for tackling many different types of optimization problems in statistics. Our applications are new and include finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, estimating parameters in the commonly used Rasch model in education research, finding M-estimates for a Cox regression in a Markov renewal model, performing matrix completion tasks to impute missing data for a two compartment model, and selecting variables optimally in an ecology problem in China. To further demonstrate the flexibility of metaheuristics, we also find an optimal design for a car refueling experiment in the auto industry using a logistic model with multiple interacting factors. In addition, we show that metaheuristics can sometimes outperform optimization algorithms commonly used in statistics.

The purpose of this research is the evaluation of effective criteria for the desirability of financial stability integration based on the comparison of metaheuristic algorithms in banks listed on the Tehran Stock Exchange. Initially, through a systematic content screening process, the effective criteria for the desirability of financial stability integration are used to evaluate banks listed on the Tehran Stock Exchange. Then, relying on two algorithms of Particle Swarm Optimization and Gray Wolf, the study reveals that both innovative algorithms used in this study have the necessary capability to determine the desirability of the financial stability of banks listed on the Tehran Stock Exchange.Metaheuristic Algorithms, Financial Stability Integration, The Desirability of Banks' Efficiency. IntroductionOne of the most important changes in the economic systems of societies is the increasing focus on the functions of financial stability in the banking systems of countries, which has been increasingly taken into account in macroeconomic policies. It is important to note that, due to reasons such as international sanctions, the banking system in developing countries faces many challenges, including disruptions in the banking system and financial exchanges as a result of the reduced foreign trade. This can lead to increased financial costs and risks, reduce public trust in the banking system, diminish international interactions with foreign banks, and disrupt the economic balance. The purpose of this research is the evaluation of effective criteria for the desirability of financial stability integration based on the comparison of metaheuristic algorithms in banks listed on the Tehran Stock Exchange. Literature ReviewFinancial stability in the banking system is defined as a low level of vulnerability to possible risks, which creates a level of balance and stability in banking systems through the ability to resist economic challenges. Elsa et al. (2018) also considered the financial stability of banks as a basis for economic growth functions in a definition and stated that a dynamic banking system needs to control the risks and costs of commercial transactions in a balanced economy to achieve stable financial stability. On the other hand, Verma and Chakarwarty (2023) suggested that if financial stability does not govern the banking systems of countries and they do not have the necessary efficiency, the optimal direction of resources to industries faces a serious challenge, and this issue can affect the country's economic growth in a short period. MethodologyThis study employs a combined and applied methodology. Initially, through a systematic content screening process, the effective criteria for the desirability of financial stability integration are used to evaluate banks listed on the Tehran Stock Exchange. Then, relying on the two algorithms of Particle Swarm Optimization and Gray Wolf and extracting data related to the criteria identified between 2017 and 2018, efforts are made to determine the optimal point of desirability of financial stability integration for banks listed on the Tehran Stock Exchange. In this process, based on the expansion of the mathematical equations of each metaheuristic algorithm and the command codes of the MATLAB software, necessary actions are taken to answer the research questions. ResultThe results showed that both innovative algorithms used in this study have the necessary capability to determine the desirability of the financial stability of banks listed on the Tehran Stock Exchange. However, based on the Wilcoxon Signed-Rank Test coefficients, the Gray Wolf algorithm is more accurate than the Particle Swarm Optimization algorithm for predicting the function of the identified criteria in determining the desirability of financial stability of banks listed on the Tehran Stock Exchange. The results after executing command processes in MATLAB software indicated that both algorithms have the necessary capability to determine the desirability of the financial stability of banks admitted to the Tehran Stock Exchange. However, based on the coefficients of the Wilcoxon test, the Gray Wolf algorithm has a higher accuracy than the Particle Swarm Optimization algorithm for predicting the performance of the identified criteria in determining the desirability of the financial stability of accepted banks. It is also found that the most effective criterion in strengthening the determination of the desirability of financial stability of banks is the liquidity circulation "ϑ3" in the Gray Wolf algorithm. DiscussionIt is also found that the most effective criterion in strengthening the determination of the desirability of banks' financial stability is the Turnover Ratio in the Gray Wolf algorithm. The coefficients obtained in the Gray Wolf algorithm indicate a more effective optimization of effective criteria in determining the financial desirability of the country's banking system. This issue provides an explanation for the interpretation that banks can benefit from this algorithm for financial planning and covering their weaknesses in preserving resources even in the risky conditions of today's economy. ConclusionThe results show that the banks whose total value of transactions in the capital market is higher than the average value of their total shares over a certain period have higher capacities for liquidity circulation. Furthermore, in providing banking services in current and investment matters in competitive projects, these banks have the upper hand compared to other banks. The existence of such added value of shares in the capital market can be considered as contributing to higher returns and lower risk for investing in these banks. Therefore, as the basics of determining the comparative evaluation between algorithms, i.e., the constant return to scale (CRS) and the variable return to scale (VRS) showed banks with higher liquidity circulation and relying on the Gray Wolf algorithm reach the optimal point faster. This finding illustrates the flexibility of financial resources in timely allocation to the market and industries, which can bring higher returns for their shareholders in the long run.

This research explores the application of metaheuristic algorithms to refine parameter estimation in dynamic systems, with a focus on the inverted pendulum model. Three optimization techniques, Particle Swarm Optimization (PSO), Continuous Genetic Algorithm (CGA), and Salp Swarm Algorithm (SSA), are introduced to solve this problem. Through a thorough statistical evaluation, the optimal performance of each technique within the dynamic methodology is determined. Furthermore, the efficacy of these algorithms is demonstrated through experimental validation on a real prototype, providing practical insights into their performance. The outcomes of this study contribute to the advancement of control strategies by integrating precisely estimated physical parameters into various control algorithms, including PID controllers, fuzzy logic controllers, and model predictive controllers. Each algorithm ran 1000 times, and the SSA algorithm achieved the best performance, with the most accurate parameter estimation with a minimum error of 0.01501 N m and a mean solution error of 0.01506 N m. This precision was further underscored by its lowest standard deviation in RMSE (1.443 99 × 10−6 N m), indicating remarkable consistency across evaluations. The 95% confidence interval for error corroborated the algorithm's reliability in deriving optimal solutions. [ABSTRACT FROM AUTHOR]

Abstract Suspensions containing microencapsulated phase change materials (MPCMs) play a crucial role in thermal energy storage (TES) systems and have applications in building materials, textiles, and cooling systems. This study focuses on accurately predicting the dynamic viscosity, a critical thermophysical property, of suspensions containing MPCMs and MXene particles using Gaussian process regression (GPR). Twelve hyperparameters (HPs) of GPR are analyzed separately and classified into three groups based on their importance. Three metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and marine predators algorithm (MPA), are employed to optimize HPs. Optimizing the four most significant hyperparameters (covariance function, basis function, standardization, and sigma) within the first group using any of the three metaheuristic algorithms resulted in excellent outcomes. All algorithms achieved a reasonable R-value (0.9983), demonstrating their effectiveness in this context. The second group explored the impact of including additional, moderate-significant HPs, such as the fit method, predict method and optimizer. While the resulting models showed some improvement over the first group, the PSO-based model within this group exhibited the most noteworthy enhancement, achieving a higher R-value (0.99834). Finally, the third group was analyzed to examine the potential interactions between all twelve HPs. This comprehensive approach, employing the GA, yielded an optimized GPR model with the highest level of target compliance, reflected by an impressive R-value of 0.999224. The developed models are a cost-effective and efficient solution to reduce laboratory costs for various systems, from TES to thermal management.

In many engineering optimization problems, the number of function evaluations is severely limited by the time or cost constraints. These limitations present a significant challenge in the field of global optimization, because existing metaheuristic methods typically require a substantial number of function evaluations to find optimal solutions. This paper presents a new metaheuristic optimization algorithm that considers the information obtained by a radial basis function neural network (RBFNN) in terms of the objective function for guiding the search process. Initially, the algorithm uses the maximum design approach to strategically distribute a set of solutions across the entire search space. It then enters a cycle in which the RBFNN models the objective function values from the current solutions. The algorithm identifies and uses key neurons in the hidden layer that correspond to the highest objective function values to generate new solutions. The centroids and standard deviations of these neurons guide the sampling process, which continues until the desired number of solutions is reached. By focusing on the areas of the search space that yield high objective function values, the algorithm avoids exhaustive solution evaluations and significantly reduces the number of function evaluations. The effectiveness of the method is demonstrated through a comparison with popular metaheuristic algorithms across several test functions, where it consistently outperforms existing techniques, delivers higher-quality solutions, and improves convergence rates. [ABSTRACT FROM AUTHOR]

This paper provides six metaheuristic algorithms, namely Fast Cuckoo Search (FCS), Salp Swarm Algorithm (SSA), Dynamic control Cuckoo search (DCCS), Gradient-Based Optimizer (GBO), Northern Goshawk Optimization (NGO), Opposition Flow Direction Algorithm (OFDA) to efficiently solve the optimal power flow (OPF) issue. Under standard and conservative operating settings, the OPF problem is modeled utilizing a range of objectives, constraints, and formulations. Five case studies have been conducted using IEEE 30-bus and IEEE 118-bus standard test systems to evaluate the effectiveness and robustness of the proposed algorithms. A performance evaluation procedure is suggested to compare the optimization techniques' strength and resilience. A fresh comparison methodology is created to compare the proposed methodologies with other well-known methodologies. Compared to previously reported optimization algorithms in the literature, the obtained results show the potential of GBO to solve various OPF problems efficiently. [ABSTRACT FROM AUTHOR]

This paper examines the performance of three binary metaheuristic algorithms when applied to two distinct knapsack problems (0–1 knapsack problems (KP01) and multidimensional knapsack problems (MKP)). These binary algorithms are based on the classical mantis search algorithm (MSA), the classical quadratic interpolation optimization (QIO) method, and the well-known differential evolution (DE). Because these algorithms were designed for continuous optimization problems, they could not be used directly to solve binary knapsack problems. As a result, the V-shaped and S-shaped transfer functions are used to propose binary variants of these algorithms, such as binary differential evolution (BDE), binary quadratic interpolation optimization (BQIO), and binary mantis search algorithm (BMSA). These binary variants are evaluated using various high-dimensional KP01 examples and compared to several classical metaheuristic techniques to determine their efficacy. To enhance the performance of those binary algorithms, they are combined with repair operator 2 (RO2) to offer better hybrid variants, namely HMSA, HQIO, and HDE. Those hybrid algorithms are evaluated using several medium- and large-scale KP01 and MKP instances, as well as compared to other hybrid algorithms, to demonstrate their effectiveness. This comparison is conducted using three performance metrics: average fitness value, Friedman mean rank, and computational cost. The experimental findings demonstrate that HQIO is a strong alternative for solving KP01 and MKP. In addition, the proposed algorithms are applied to the Merkle-Hellman Knapsack Cryptosystem and the resource allocation problem in adaptive multimedia systems (AMS) to illustrate their effectiveness when applied to optimize those real applications. The experimental findings illustrate that the proposed HQIO is a strong alternative for handling various knapsack-based applications.

The Internet of Things (IoT) has radically reformed various sectors and industries, enabling unprecedented levels of connectivity and automation. However, the surge in the number of IoT devices has also widened the attack surface, rendering IoT networks potentially susceptible to a plethora of security risks. Addressing the critical challenge of enhancing security in IoT networks is of utmost importance. Moreover, there is a considerable lack of datasets designed exclusively for IoT applications. To bridge this gap, a customized dataset that accurately mimics real-world IoT scenarios impacted by four different types of attacks—blackhole, sinkhole, flooding, and version number attacks was generated using the Contiki-OS Cooja Simulator in this study. The resulting dataset is then consequently employed to evaluate the efficacy of several metaheuristic algorithms, in conjunction with Convolutional Neural Network (CNN) for IoT networks. • The proposed study's goal is to identify optimal hyperparameters for CNNs, ensuring their peak performance in intrusion detection tasks. • This study not only intensifies our comprehension of IoT network security but also provides practical guidance for implementation of the robust security measures in real-world IoT applications.

Metaheuristic techniques are capable of representing optimization frames with their specific theories as well as objective functions owing to their being adjustable and effective in various applications. Through the optimization of deep learning models, metaheuristic algorithms inspired by nature, imitating the behavior of living and non-living beings, have been used for about four decades to solve challenging, complex, and chaotic problems. These algorithms can be categorized as evolution-based, swarm-based, nature-based, human-based, hybrid, or chaos-based. Chaos theory, as a useful approach to understanding neural network optimization, has the basic idea of viewing the neural network optimization as a dynamical system in which the equation schemes are utilized from the space pertaining to learnable parameters, namely optimization trajectory, to itself, which enables the description of the evolution of the system by understanding the training behavior, which is to say the number of iterations over time. The examination of the recent studies reveals the importance of chaos theory, which is sensitive to initial conditions with randomness and dynamical properties that are principally emerging on the complex multimodal landscape. Chaotic optimization, in this regard, accelerates the speed of the algorithm while also enhancing the variety of movement patterns. The significance of hybrid algorithms developed through their applications in different domains concerning real-world phenomena and well-known benchmark problems in the literature is also evident. Metaheuristic optimization algorithms have also been applied to deep learning or deep neural networks (DNNs), a branch of machine learning. In this respect, the basic features of deep learning and DNNs and the extensive use of metaheuristic algorithms are overviewed and explained. Accordingly, the current review aims at providing new insights into the studies that deal with metaheuristic algorithms, hybrid-based metaheuristics, chaos-based metaheuristics as well as deep learning besides presenting recent information on the development of the essence of this branch of science with emerging opportunities, applicability-based optimization aspects and generation of well-informed decisions. [ABSTRACT FROM AUTHOR]

Abstract The ground vibration caused by rock blasting is an extremely hazardous outcome of the blasting operation. Blasting activity has detrimental effects on both the ecology and the human population living in proximity to the area. Evaluating the magnitude of blasting vibrations requires careful evaluation of the peak particle velocity (PPV) as a fundamental and essential parameter for quantifying vibration velocity. Therefore, this study employs models using the relevance vector machine (RVM) approach for predicting the PPV resulting from quarry blasting. This investigation utilized the conventional and optimized RVM models for the first time in ground vibration prediction. This work compares thirty-three RVM models to choose the most efficient performance model. The following conclusions have been mapped from the outcomes of the several analyses. The performance evaluation of each RVM model demonstrates each model achieved a performance of more than 0.85 during the testing phase, there was a strong correlation observed between the actual ground vibrations and the predicted ones. The analysis of performance metrics (RMSE = 21.2999 mm/s, 16.2272 mm/s, R = 0.9175, PI = 1.59, IOA = 0.8239, IOS = 0.2541), score analysis (= 93), REC curve (= 6.85E−03, close to the actual, i.e., 0), curve fitting (= 1.05 close to best fit, i.e., 1), AD test (= 11.607 close to the actual, i.e., 9.790), Wilcoxon test (= 95%), Uncertainty analysis (WCB = 0.0134), and computational cost (= 0.0180) demonstrate that PSO_DRVM model MD29 outperformed better than other RVM models in the testing phase. This study will help mining and civil engineers and blasting experts to select the best kernel function and its hyperparameters in estimating ground vibration during rock blasting project. In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency.