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This work presents a new version of the salp swarm optimizer (SSA), called "mSSA," that uses complex mathematical expressions to dynamically manipulate the crucial control parameter ( c 1 ) during optimization. These expressions are carefully designed to modulate the shift in search strategy from exploratory to exploitative, improving the flexibility and speed of convergence of the algorithm. To evaluate the performance of the developed mSSA variants, a thorough examination is carried out on twenty-three benchmark test functions alongside their application to the complex task of software module classification. The process of classifying defective software modules involves developing a multilayer perceptron (MLP) classifier that is suited to the particular complexity and heterogeneity of the task. Selecting the best optimizer is made easier by systematically evaluating the different mSSA versions as MLP classifier trainers. Based on metrics like classification accuracy, convergence speed, and avoidance of local minima, a comparative analysis in opposition to six previously published metaheuristic optimizers shows that mSSA3, when combined with the developed MLP classifier, outperforms both other mSSA variations and state-of-the-art metaheuristic optimizers in terms of overall performance. The excellent classification accuracy, swift convergence, and ability to avoid local minima of mSSA3 highlight its superiority and establish it as a cutting-edge method in the application of metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

The fundamental objective of a modern power system lies in ensuring reliable and effective energy access for its customers. The assessment and determination of optimal operating conditions for power systems involve the utilization of the optimal power flow (OPF) tool. By considering critical factors such as generator power, bus voltages, and line power flow limits while satisfying the power balance equations, the OPF tool enables the identification of the most favorable configuration for efficient power system operation. Traditional optimization methods have limitations in addressing complex power system problems due to poor convergence and long computational times. As a result, computational intelligence tools have gained popularity in recent years. These tools are versatile and enable efficient solution of power system problems by effectively handling qualitative constraints. This paper presents a well-organized and comprehensive review of the algorithms used in power system optimization in the existing literature, encompassing the most recent developments in the field. Specifically, it examines the application of various population-based artificial intelligence techniques that have gained widespread adoption over the past decade (2012–2022). The aim of these techniques is to resolve an OPF problem. This paper organizes the reviewed papers into various types of population-based metaheuristic algorithms, each one implemented sequentially to deal with the OPF problem in the same chronological order in which they appeared in the literature. [ABSTRACT FROM AUTHOR]

Ensemble learning has become a cornerstone in various classification and regression tasks, leveraging its robust learning capacity across disciplines. However, the computational time and memory constraints associated with almost all-learners-based ensembles necessitate efficient approaches. Ensemble pruning, a crucial step, involves selecting a subset of base learners to address these limitations. This study underscores the significance of optimization-based methods in ensemble pruning, with a specific focus on metaheuristics as high-level problem-solving techniques. It reviews the intersection of ensemble learning and metaheuristics, specifically in the context of selective ensembles, marking a unique contribution in this direction of research. Through categorizing metaheuristic-based selective ensembles, identifying their frequently used algorithms and software programs, and highlighting their uses across diverse application domains, this research serves as a comprehensive resource for researchers and offers insights into recent developments and applications. Also, by addressing pivotal research gaps, the study identifies exploring selective ensemble techniques for cluster analysis, investigating cutting-edge metaheuristics and hybrid multi-class models, and optimizing ensemble size as well as hyper-parameters within metaheuristic iterations as prospective research directions. These directions offer a robust roadmap for advancing the understanding and application of metaheuristic-based selective ensembles. [ABSTRACT FROM AUTHOR]

Detecting tumors using gene analysis in microarray data is a critical area of research in artificial intelligence and bioinformatics. However, due to the large number of genes compared to observations, feature selection is a central process in microarray analysis. While various gene selection methods have been developed to select the most relevant genes, these methods' efficiency and reliability can be improved. This paper proposes a new two-phase gene selection method that combines the ReliefF filter method with a novel version of the spider wasp optimizer (SWO) called RSWO-MPA. In the first phase, the ReliefF filter method is utilized to reduce the number of genes to a reasonable number. In the second phase, RSWO-MPA applies a recursive spider wasp optimizer guided by the marine predators algorithm (MPA) to select the most informative genes from the previously selected ones. The MPA is used in the initialization step of recursive SWO to narrow down the search space to the most relevant and accurate genes. The proposed RSWO-MPA has been implemented and validated through extensive experimentation using eight microarray gene expression datasets. The enhanced RSWO-MPA is compared with seven widely used and recently developed meta-heuristic algorithms, including Kepler optimization algorithm (KOA), marine predators algorithm (MPA), social ski-driver optimization (SSD), whale optimization algorithm (WOA), Harris hawks optimization (HHO), artificial bee colony (ABC) algorithm, and original SWO. The experimental results demonstrate that the developed method yields the highest accuracy, selects fewer features, and exhibits more stability than other compared algorithms and cutting-edge methods for all the datasets used. Specifically, it achieved an accuracy of 100.00%, 94.51%, 98.13%, 95.63%, 100.00%, 100.00%, 92.97%, and 100.00% for Yeoh, West, Chiaretti, Burcyznski, leukemia, ovarian cancer, central nervous system, and SRBCT datasets, respectively. [ABSTRACT FROM AUTHOR]

The Chinese Postman Problem (CPP) is a well-known optimization problem involving determining the shortest route, modeling the system as an undirected graph, for delivering mail, ensuring all roads are traversed while returning to the post office. The Directed Chinese Postman Problem (DCPP) extends the Chinese Postman Problem (CPP), where the underlying graph representing the system incorporates exclusively directed edges. Similarly to CPP, this problem has plenty of applications in route optimization, interactive system analysis, and circuit design problems. However, due to the added constraint (directionality of edges), DCPP results are more challenging to solve. Although methods to solve it in literature are proposed, typically by using minimum-cost-flow algorithms, the meta-heuristics approaches proposed to deal with it are very limited. In this paper, we propose an innovative meta-heuristic approach to solve DCPP by using an ant colony optimization (ACO) algorithm, i.e., an algorithm that simulates in a simplified way the behavior of some species of ants to solve optimization problems. The efficiency of our ant colony optimization for solving the Directed Chinese Postman Problem (ACO-DCPP) is measured by comparing the ACO outcomes with the results obtained by a recursive algorithm that explores all the possible solutions. Results show that ACO-DCPP is stable and gets the global optimum frequently by using an extremely limited number of solutions explored. [ABSTRACT FROM AUTHOR]

Data mining and machine learning require feature selection because features can dramatically improve model performance. In contrast, there are no polynomial solutions for selecting a subset feature. It is possible to achieve this by using meta-heuristic algorithms, specifically population-based algorithms that are able to provide a subset of features that is optimal and not exact. Meta-heuristic algorithms face challenges such as staying in local minima, easily falling into local optimum, weakly global searchability, premature convergence, and slow convergence speeds. However, recent research has limitations such as high complexity and weak initialization. In order to overcome these limitations, a three-stage model is proposed. In the first stage, the correlation of features and the correlation of features with class are considered during feature selection and used to create the initial population in the pathfinder optimization algorithm (PFA). PFA is a population-based algorithm and has some drawbacks, in the last iterations, the fluctuation rate (A) and vibration vector (ε) parameters converge to 0, and finding a new solution is impossible. As a second stage, a fuzzy inference system is designed to adjust these parameters adaptively and is called fuzzy-pathfinder optimization (FPO). In the third stage, FPO is used to select relevant features based on classification error, proportion of selected features, and redundancy. Finally, different algorithms such as simulated annealing (SA), differential evolutionary (DE), genetic algorithm (GA), particle swarm optimization (PSO), PFA, estimation of distribution algorithm (EDA), and symmetrical uncertainty criterion (SUC-PSO) are used for comparison. Based on the results, the proposed model is able to reach an average accuracy of 96% on average. Based on a comparison of the proposed algorithm with SA, DE, GA, PSO, PFA, EDA, and SUC-PSO, the objective function is improved by 17.3%, 5.6%, 3.0%, 4.5%, 5.0%, 0.5%, and 1.2%, respectively. The use of comprehensive objective functions, the adaptive adjustment of parameters, and the creation of a targeted initial population are key strengths of FPO. [ABSTRACT FROM AUTHOR]

This paper proposes a novel social cognitive learning-based metaheuristic called kids Learning Optimizer (KLO), inspired by the early social learning behavior of kids organized as families in societal setup. In a society, people are organized as family groups (parents and children) where they interact with each other within and outside their family. This interaction plays a vital role in early brain development, mannerisms, and behavioral learning. Early childhood learning is affected by various stimuli in their surroundings at different stages of life (newborn, infant, toddler, pre-school). This idea motivated us to map the decentralized learning concept of families and their interactions into a new algorithm where search agents (individuals) are arranged/organized in families, and they interact with each other at different stages of life to find the optimal solution. The algorithm is tested against 116 challenging benchmark functions including 31 unimodal, 63 multimodal, 14 CEC'2017 functions, and eight constrained functions. The algorithm is compared with 10 state-of-the-art algorithms. Friedman's Mean Rank (FMR) and Wilcoxon rank-sum test (WRS) are used to measure the performance of competing algorithms. In the first two experiments, unimodal and multimodal benchmark functions are used to measure the explorative and exploitative ability of the algorithm. FMR and WRS show KLO outperformed all other algorithms. In the third experiment, the proposed method is tested against 14 CEC'2017 functions and eight constrained functions along with three real life mechanical engineering optimization problems. KLO proved to be better or equivalent to other competing algorithms. [ABSTRACT FROM AUTHOR]

It is very tiresome for a practiser to detect the best feasible sizing design of structural members including reinforced concrete columns that is a highly nonlinear and complicated structural engineering optimization problem. This is due to such a design is practically conducted via conventional trial-and-error computing methods in which resistance to external loads, cost efficiency, and aesthetic factors, etc. have to be considered. This study focuses on minimizing the design cost of primarily proposed reinforced concrete column design problem via three overnew swarm-based optimizers such as Coati Optimization Algorithm, Fox Optimizer and Pelican Optimization Algorithm (POA) that are firstly utilized for this purpose. In this regard, the type of steel rebar distribution, the characteristic strength of the concrete, the height and width of the column section, and the number and diameter of the rebars are treated as discrete design variables of the newly proposed complex reinforced concrete column design cost optimization problem. In solution, the design requirements specified in practice code provisions should also be met. Here, Turkish Building Earthquake Code 2018 specifications are considered as practice structural design constraints. Consequently, the algorithmic performances of three overnew swarm-based metaheuristic optimization algorithms are compared and evaluated in detail. Amongst them, the POA shows most fruitful algorithmic design solution performance. [ABSTRACT FROM AUTHOR]

The artificial hummingbird algorithm (AHA) has been applied in various fields of science and provided promising solutions. Although the algorithm has demonstrated merits in the optimization area, it suffers from local optimum stagnation and poor exploration of the search space. To overcome these drawbacks, this study redesigns the update mechanism of the original AHA algorithm with the natural survivor method (NSM) and proposes a novel metaheuristic called NSM-AHA. The strength of the developed algorithm is that it performs population management not only according to the fitness function value but also according to the NSM score value. The adopted strategy contributes to NSM-AHA exhibiting powerful local optimum avoidance and unique exploration ability. The optimization ability of the proposed NSM-AHA algorithm was compared with 21 state-of-the-art algorithms over CEC 2017 and CEC 2020 benchmark functions with dimensions of 30, 50, and 100, respectively. Based on the Friedman test results, it was observed that NSM-AHA ranked 1st out of 22 competitive algorithms, while the original AHA ranked 8th. This result highlights that the NSM update mechanism provides a remarkable evolution in the convergence performance of the original AHA algorithm. Furthermore, two constrained engineering problems including the optimization of single-diode solar cell model (SDSCM) parameters and the design of a power system stabilizer (PSS) are solved with the proposed algorithm. The NSM-AHA algorithm provided better results compared to other algorithms with a value of 9.86E − 04 root mean square error for SDSCM and 1.43E − 03 integral time square error for PSS. The experimental results showed that the proposed NSM-AHA is a competitive optimizer for solving global and engineering problems. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) system performance and reliability can be improved through the detection of defects in PV modules and the evaluation of their effects on system operation. In this paper, a novel system is proposed to detect and classify defects based on electroluminescence (EL) images. This system is called Fault Detection and Classification (FDC) and splits into four modules, which are (1) Image Preprocessing Module (IPM), (2) Feature Extraction Module (FEM), (3) Feature Selection Module (FSM), and (4) Classification Module (CM). In the first module (i.e., IPM), the EL images are preprocessed to enhance the quality of the images. Next, the two types of features in these images are extracted and fused together through FEM. Then, during FSM, the most important and informative features are extracted from these features using a new feature selection methodology, namely, Feature Selection-based Chaotic Map (FS-CM). FS-CM consists of two stages: filter stage using chi-square to initially select the most effective features and a modified selection stage using an enhanced version of Butterfly Optimization Algorithm (BOA). In fact, BOA is a popular swarm-based metaheuristic optimization algorithm that has only recently found success. While BOA has many benefits, it also has some drawbacks, including a smaller population and an increased likelihood of getting stuck in a local optimum. In this paper, a new methodology is proposed to improve the performance of BOA, called chaotic-based butterfly optimization algorithm. Finally, these selected features are used to feed the proposed classification model through CM. During CM, Hybrid Classification Model (HCM) is proposed. HCM consists of two stages, which are binary classification stage using Naïve Bayes (NB) and multi-class classification stage using enhanced multi-layer perceptron. According to the experimental results, the proposed system FDC outperforms the most recent methods. FDC introduced 98.2%, 89.23%, 87.2%, 87.9%, 87.55%, and 88.20% in terms of accuracy, precision, sensitivity, specificity, g-mean, and f-measure in the same order. [ABSTRACT FROM AUTHOR]

Efficient flood risk management hinges on the precise mapping and assessment of areas vulnerable to flooding. This research endeavors to advance the flood susceptibility mapping in Jeddah, Saudi Arabia by harnessing the long short-term memory (LSTM) algorithm enriched with two sophisticated metaheuristic optimizers: invasive weed optimization (IWO) and harmony search (HS). The process commenced with the utilization of synthetic aperture radar (SAR) imagery to construct a detailed flood inventory map. A comprehensive geodatabase encompassing various flood conditioning factors—encompassing lithology, land use, proximity to water bodies, hydrologic soil group (HSG), topographical features (such as slope, plan curvature, and aspect), and hydrological indices [including profile curvature, Topographic Wetness Index (TWI), flow accumulation, Topographic Position Index (TPI), altitude, Terrain Ruggedness Index (TRI), and Stream Power Index (SPI)] was meticulously curated. To develop the model, 70% of this dataset was employed, while the remaining 30% served to validate the predictive efficacy of the resultant flood susceptibility maps. These maps' accuracy was quantitatively gauged through the receiver operating characteristic curve and the area under the curve (AUC) statistics. Findings reveal that the integration of LSTM with IWO and HS metaheuristic algorithms significantly enhances accuracy (LSTM-IWO: AUC = 0.900; LSTM-HS: AUC = 0.876) in comparison to the standalone LSTM approach (AUC = 0.863). The implementation of these hybrid algorithms manifests as a potent and economically viable approach for detailed geospatial modeling of flood susceptibility, providing invaluable insights to bolster flood mitigation, preparedness, and emergency response strategies. [ABSTRACT FROM AUTHOR]

Hybrid energy system (HES) is considered a solution to the energy supply issue, particularly in rural areas to achieve their sustainable development goals. The rise in energy consumption has increased the appeal of renewable resources, because of their potential to supply consumers with competitive, carbon-free electricity. This paper suggests strategies for managing energy and the most recently published optimizers for designing a stand-alone HES positioned in a remote region of southwest Egypt. This HES includes two green energy sources (wind and solar) and a storage system for energy (battery) as the first backup in addition to a second backup (diesel). The most recent sizing techniques employing the Chernobyl disaster optimizer, dynamic control cuckoo search (DCCS), and gold rush optimizer have been suggested to obtain the optimal design of the utilized HES. Furthermore, an in-depth evaluation of the applied optimization approaches has been achieved based on a comparative study. A detailed analysis of the studied algorithms aims to identify the optimum algorithm that provides the lowest possible cost at the highest level of reliability for the proposed HES. The simulation results verified that, the DCCS algorithm outperformed other algorithms, indicating its potential for achieving promising solutions. [ABSTRACT FROM AUTHOR]

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. [ABSTRACT FROM AUTHOR]

In this study, we introduce the pied kingfisher optimizer (PKO), a novel swarm-based meta-heuristic algorithm that draws inspiration from the distinctive hunting behavior and symbiotic relationships observed in pied kingfishers in the natural world. The PKO algorithm is structured around three distinct phases: perching/hovering for prey (exploration/diversification), diving for prey (exploitation/intensification), and fostering symbiotic relations. These behavioral aspects are translated into mathematical models capable of effectively addressing a wide array of optimization challenges across diverse search spaces. The algorithm's performance is rigorously evaluated across thirty-nine test functions, which encompass various unimodal, multimodal, composite, and hybrid ones. Additionally, eight real-world engineering optimization problems, including both constrained and unconstrained scenarios, are considered in the assessment. To gauge PKO's efficacy, it is subjected to a comparative analysis against 3 categories of rival optimizers. The 1st category comprises well-established and widely-cited optimizers such as particle swarm optimization and genetic algorithm. The 2nd category encompasses recently published algorithms, including Harris Hawks optimization, Whale optimization algorithm, sine cosine algorithm, Grey Wolf optimizer, gravitational search algorithm, and moth-flame optimization. The 3rd category includes advanced algorithms, such as covariance matrix adaptation evolution strategy and Ensemble Sinusoidal Differential Covariance Matrix Adaptation with Euclidean Neighborhood (LSHADE-cnEpSin). The comparative analysis employs various performance metrics, including the Friedman mean rank and the Wilcoxon rank-sum test, to reveal PKO's effectiveness and efficiency. The overall results highlight PKO's exceptional ability to tackle intricate optimization problems characterized by challenging search spaces. PKO demonstrates superior exploration and exploitation tendencies while effectively avoiding local optima. The source code for the PKO algorithm is publicly accessible at https://www.mathworks.com/matlabcentral/fileexchange/160043-pied-kingfisher-optimizer-pko. [ABSTRACT FROM AUTHOR]

This paper proposes a metaheuristic algorithm, called advanced differential evolution (ADE), by improving the DE algorithm. The ADE algorithm was developed with the goal of creating an optimization framework that addresses the challenges of exploration and exploitation balance, avoiding local minima, utilizing chaos theory for diverse initialization, and improving solution quality and convergence speed. By incorporating these features, ADE aims to enhance the effectiveness of optimization processes. The proposed algorithm utilizes chaos theory to generate the initial population, which is subsequently divided into two sub-populations with adaptive sizes. The size of each sub-population is determined using a formula based on the number of iterations during the algorithm's execution. The first sub-population has a larger size in the beginning and the second one has a smaller size, but the total size of these two populations is always constant. The main contribution of this paper is the proposal of two novel improved differential evolution algorithms, namely MDE1 and MDE2, which are utilized for exploration within these sub-populations. The proposed ADE is tested on 29 well-known benchmarks and six engineering problems, and the results are compared with seven other algorithms. Various statistical experiments are carried out showing that the proposed algorithm provides significant superiority over other well-known algorithms. [ABSTRACT FROM AUTHOR]

The integration of cloud and edge computing, along with machine learning, plays a vital role in the development of efficient healthcare systems in smart cities. However, machine and deep learning (DL) models are prone to delayed convergence and Type-I and Type-II errors due to data vastness and high degree imbalance. To overcome the shortcomings of previous frameworks, this work aims to propose an optimization method with DL, 'Deep-Chaotic Nature Whale Optimization' (Deep-CNWO) for early prediction of Blood Pressure disorders among patients under at-home supervision. A simplex search algorithm is integrated to improve the update mechanism of whale optimization algorithm (WOA), thereby creating a CNWO algorithm. The purpose of this hybrid optimization is to increase the accuracy and efficiency of DL models. Leveraging the power of DL and CNWO, this method (Deep-CNWO) provides an effective solution for early detection and proactive management of a chronic disease in at-home healthcare settings. We collected relevant data from clinical studies, including vital signs and patient contextual information, to train and evaluate the deep-CNWO model. The CNWO optimization approach has been used to improve the predictive performance and convergence of DL models. Experiments performed on imbalanced datasets using deep-CNWO have given 99.90% accuracy. The average F-score for emergency cases has improved by 22%, while the average accuracy has increased by 5.72% across all three classes, compared to the results reported in previous related work. Deep-CNWO improves the convergence of DL and reduces Type-I and Type-II errors. The experimental results demonstrate the efficacy of our proposed method for remote patient monitoring and highlight its potential for quick intervention during emergencies. [ABSTRACT FROM AUTHOR]

This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared ( R 2 ) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach. [ABSTRACT FROM AUTHOR]

This study introduces the CP-EODE algorithm, a novel hybrid of the Equilibrium Optimizer (EO), and the Differential Evolution (DE) algorithm. It addresses EO's tendency toward premature convergence by enhancing its exploration capabilities. The motivation for this research stems from the need for more efficient and economically viable designs in engineering, particularly in the optimization of shell-and-tube heat exchangers (STHEs) and photovoltaic module parameters. The CP-EODE algorithm leverages DE's exploration strengths to enhance EO's performance adopting a cooperative parallel approach that divides the population into EO and DE sub-populations for a more effective search space exploration. The validation of CP-EODE's effectiveness commenced with its application to 29 benchmark functions, where a Friedman test yielded an F r value of 22.19, significantly surpassing the critical value of 7.81, and a Wilcoxon signed rank test confirmed statistical improvements with a z-score beyond − 1.96. Four quality metrics were discussed to provide a detailed assessment of CP-EODE's performance highlighting its robust exploration and exploitation capabilities. In practical applications, CP-EODE demonstrated significant advancements in STHE design achieving up to 19.706% reduction in heat exchanger surface area compared to traditional and other algorithmic designs, showcasing its potential to enhance engineering design efficiency and cost-effectiveness. A critical examination of the algorithm's convergence rate revealed its swift optimization capability, with rapid attainment of optimal solutions in various test cases, further establishing CP-EODE's advantage in handling complex optimization challenges. Through rigorous statistical analysis and performance metrics, CP-EODE emerges as a superior solution for complex optimization challenges, underscoring the value of hybrid algorithmic strategies in engineering optimizations. [ABSTRACT FROM AUTHOR]

The primary objective of a power system is to provide safe and reliable electrical energy to consumers. This objective is achieved by maintaining the stability of the power system, a multifaceted concept that can be divided into three distinct classes. The focus of this study is on one of these classes, voltage stability. A critical component in maintaining voltage stability is the automatic voltage regulator (AVR) system of synchronous generators. In this paper, a novel control method, the sigmoid-based fractional-order PID (SFOPID), is introduced with the aim of improving the dynamic response and the robustness of the AVR system. The dandelion optimizer (DO), a successful optimization algorithm, is used to optimize the parameters of the proposed SFOPID control strategy. The optimization process for the DO-SFOPID control strategy includes a variety of objective functions, including error-based metrics such as integral of absolute error, integral of squared error, integral of time absolute error, and integral of time squared error, in addition to the user-defined Zwee Lee Gaing's metric. The effectiveness of the DO-SFOPID control technique on the AVR system has been rigorously investigated through a series of tests and analyses, including aspects such as time domain, robustness, frequency domain, and evaluation of nonlinearity effects. The simulation results are compared between the proposed DO-SFOPID control technique and the fractional-order PID (FOPID) and sigmoid-based PID (SPID) control techniques, both of which have been tuned using different metaheuristic algorithms that have gained significant recognition in recent years. As a result of these comparative analyses, the superiority of the DO-SFOPID control technique is confirmed as it shows an improved performance with respect to the other control techniques. Furthermore, the performance of the proposed DO-SFOPID control technique is validated within an experimental setup for the AVR system. The simulation results show that the proposed DO-SFOPID control technique is highly successful in terms of stability and robustness. In summary, this study provides comprehensive evidence supporting the effectiveness and superiority of the DO-SFOPID control technique on the AVR system through both simulation and experimental results. [ABSTRACT FROM AUTHOR]

Communication quality is compromised when wireless sensor network (WSN) operate in harsh environments, which can be improved by supplementing the nodes. This paper proposes a deployment strategy to optimize the placement of new nodes in WSN, specifically in complex environments with limited communication. To achieve this, the paper introduces the concept of strong connectivity relationships and presents a novel metaheuristic algorithm, namely double-state differential evolution (DSDE), which divides the optimization process into two states and adopts different optimization strategies. The proposed DSDE can improve the overall network communication glowing at a lower computing cost. Extensive experiments show that the proposed DSDE has better performance than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The forensic-based investigation (FBI) is a metaheuristic algorithm inspired by the criminal investigation process. The collaborative efforts of the investigation and pursuit teams demonstrate the FBI's involvement during the exploitation and exploration phases. When choosing the promising population, the FBI algorithm's population selection technique focuses on the same region. This research aims to propose a dynamic population selection method for the original FBI and thereby enhance its convergence performance. To achieve this objective, the FBI may employ dynamic oppositional learning (DOL), a dynamic version of the oppositional learning methodology, to dynamically navigate to local minima in various locations. Therefore, the proposed advanced method is named DOLFBI. The performance of DOLFBI on the CEC2019 and CEC2022 benchmark functions is evaluated by comparing it with several other popular metaheuristics in the literature. As a result, DOLFBI yielded the lowest fitness value in 18 of 22 benchmark problems. Furthermore, DOLFBI has shown promising results in solving real-world engineering problems. It can be argued that DOLFBI exhibits the best convergence performance in cantilever beam design, speed reducer, and tension/compression problems. DOLFBI is often utilized in truss engineering difficulties to determine the minimal weight. Its success is comparable to other competitive MAs in the literature. The Wilcoxon signed-rank and Friedman rank tests further confirmed the study's stability. Convergence and trajectory analyses validate the superior convergence concept of the proposed method. When the proposed study is compared to essential and enhanced MAs, the results show that DOLFBI has a competitive framework for addressing complex optimization problems due to its robust convergence ability compared to other optimization techniques. As a result, DOLFBI is expected to achieve significant success in various optimization challenges, feature selection, and other complex engineering or real-world problems. [ABSTRACT FROM AUTHOR]

The flexible job shop scheduling problem (FJSP) is a classic NP-hard problem, and the quality of its scheduling solution directly affects the operational efficiency of the manufacturing system. However, the traditional scheduling algorithms suffer from poor generalization when solving FJSP; there are problems such as long computational time and dimensional disasters, especially as the scale of FJSP increases. Therefore, an effective deep actor-critic reinforcement learning (DACRL) method is proposed for solving FJSP. Firstly, the FJSP is modeled as a multi-agent Markov decision process (MMDP), the state space, action space, and reward function in the MMDP are designed. Secondly, a DACRL model is constructed to solve FJSP. The actor network is responsible for choosing the most suitable scheduling rule in different states, while the critic network is responsible for outputting the value function of the actions and providing feedback to the actor network to better adjust the scheduling strategy. Finally, the proposed DACRL method is validated on benchmark FJSP instances of different scales. The experimental results show that the proposed method significantly outperforms the heuristic scheduling rules and double deep Q-network (DDQN) in terms of solution quality. Compared with the meta-heuristic algorithms and the self-learning genetic algorithm (SLGA), the proposed method has higher solution efficiency with the same solution quality. [ABSTRACT FROM AUTHOR]

Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic algorithm for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved Harris Hawks optimization (IHHO) algorithm for solving different engineering tasks. The proposed algorithm focused on random location-based habitats during the exploration phase and on strategies 1, 3, and 4 during the exploitation phase. The proposed modified Harris hawks in the wild would change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. To avoid being stuck in a local solution, random values were generated using logarithms and exponentials to explore new regions more quickly and locations. To evaluate the performance of the proposed algorithm, IHHO was compared to other five recent algorithms [grey wolf optimization, BAT algorithm, teaching–learning-based optimization, moth-flame optimization, and whale optimization algorithm] as well as three other modifications of HHO (BHHO, LogHHO, and MHHO). These optimizers had been applied to different benchmarks, namely standard benchmarks, CEC2017, CEC2019, CEC2020, and other 52 standard benchmark functions. Moreover, six classical real-world engineering problems were tested against the IHHO to prove the efficiency of the proposed algorithm. The numerical results showed the superiority of the proposed algorithm IHHO against other algorithms, which was proved visually using different convergence curves. Friedman's mean rank statistical test was also inducted to calculate the rank of IHHO against other algorithms. The results of the Friedman test indicated that the proposed algorithm was ranked first as compared to the other algorithms as well as three other modifications of HHO. [ABSTRACT FROM AUTHOR]

The integration of advanced technologies like cloud and edge computing has facilitated efficient resource coordination, resulting in improved overall management and widespread applicability. Simultaneously, addressing energy consumption, reliability, and server and communication link failure rates has become a pressing research concern. To address this challenge, an advanced combined-decomposition whale optimization algorithm has been developed. This algorithm utilizes a unique combined-decomposition operator to identify nearly optimal server coalitions, enhancing the quality of service performance. Through a training process at various utilization levels, servers determine their optimal utilization for achieving the maximum energy-to-reliability trade-off ratio. Then, a head server with the highest optimal utilization leads each of these clusters of servers. Unlike other population-based clustering methods, this algorithm incorporates the whale optimization algorithm, extending its exploration and exploitation capabilities beyond other leading scheduling algorithms. The integration of these techniques successfully achieves the dual objective of balancing energy and reliability, addressing existing challenges, and ensuring optimal energy-reliability trade-offs. Simulation experiments using various evaluation metrics demonstrate that the proposed approach enhances energy efficiency by approximately 17 to 35% and reliability by 25 to 55%, all while meeting quality service standards. [ABSTRACT FROM AUTHOR]

This article proposes the use of a leader white shark optimizer (LWSO) with the aim of improving the exploitation of the conventional white shark optimizer (WSO) and solving the economic operation-based load dispatch (ELD) problem. The ELD problem is a crucial aspect of power system operation, involving the allocation of power generation resources to meet the demand while minimizing operational costs. The proposed approach aims to enhance the performance and efficiency of the WSO by introducing a leadership mechanism within the optimization process, which aids in more effectively navigating the complex ELD solution space. The LWSO achieves increased exploitation by utilizing a leader-based mutation selection throughout each generation of white sharks. The efficacy of the proposed algorithm is tested on 13 engineer benchmarks non-convex optimization problems from CEC 2020 and compared with recent metaheuristic algorithms such as dung beetle optimizer (DBO), conventional WSO, fox optimizer (FOX), and moth-flame optimization (MFO) algorithms. The LWSO is also used to address the ELD problem in different case studies (6 units, 10 units, 11 units, and 40 units), with 20 separate runs using the proposed LWSO and other competitive algorithms being statistically assessed to demonstrate its effectiveness. The results show that the LWSO outperforms other metaheuristic algorithms, achieving the best solution for the benchmarks and the minimum fuel cost for the ELD problem. Additionally, statistical tests are conducted to validate the competitiveness of the LWSO algorithm. [ABSTRACT FROM AUTHOR]

With the growing adoption of cloud computing in both public and private sector enterprises, the industry has experienced rapid expansion. To fully unlock the potential of cloud computing, efficient task scheduling becomes crucial. In cloud computing, task scheduling involves optimizing the allocation of tasks to a diverse range of resources, such as virtual machines, with the goals of reducing makespan, maximizing resource utilization, and minimizing response times. This challenge becomes even more pronounced for large-scale tasks due to the NP-hard nature of the problem. Consequently, the integration of metaheuristic algorithms into task scheduling has emerged as a solution to equitably distribute complex and diverse tasks across limited resources within acceptable timeframes. To enhance the quality of cloud computing services, this research introduces the modified white shark optimizer (mWSO) as an alternative task scheduling technique. The improved variant mWSO boosts the performance of the original WSO by introducing the following three enhancement steps: (1) introduce memory-based WSO to boost the exploitation phase, (2) propose an exploration-exploitation balance phase to enhance the exploration phase, and (3) introduce a control randomization parameter to balance exploration and exploitation properly. The mWSO is subjected to testing on both the global optimization problems from CEC2020 and cloud task scheduling problems. The experimental results of mWSO demonstrate high performance for CEC2020 competition benchmarks compared to other state-of-the-art and recent metaheuristic algorithms. In the case of the task scheduling problem, the mWSO achieved − 0.01 to 13.53% and 0.62–10.42% makespan and energy consumption reduction, respectively, for CEA-Curie workloads. For HPC2N workloads, mWSO achieved 7.27–29.53% makespan reduction and 3.52–26.08% energy savings over the compared metaheuristics. The statistical validity of the performance is also verified using Wilcoxon's rank-sum test. The experimental results and comparison analysis reveal the consistent and better performance of the proposed mWSO to solve optimization problems. [ABSTRACT FROM AUTHOR]

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO's efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. The goal is to select a set of sensors based on norms of weighted distances cost function. The norms are the Euclidean distance and the Mahalanobis distance between the drone location and the sensors. The second one depends on the predicted covariance of the tracker. The Extended Kalman Filter (EKF) is used for state estimation with proper clutter and detection models. Since we use Multi-objects to track, the Joint Probability Distribution Function (JPDA) estimates the best measurement values with a preset gating threshold. The goal is to find a sensor or minimum set of sensors that would be enough to generate high-quality tracking based on optimum resource allocation. In the experimentation simulated with Stone Soup, one radar among five radars is selected at every time step of 50-time steps for 200 tracks distributed over 20 different ground truths. The proposed IPDOA provided optimum solutions for this complex problem. The obtained solution is an optimum offline solution that is used to select one or more sensors for any future flights within the vicinity of the 5 radars. Environment and conditions are assumed to be similar in future drone flights within the radars' defined zone. The IPDOA performance was compared with the other 8 metaheuristic optimization algorithms and the testing showed its superiority over those techniques for solving this complex problem. The proposed simulated model can find the most relevant sensor(s) capable of generating the best quality tracks based on weighted distance criteria (Euclidean and Mahalanobis). That would cut down the cost of operating extra sensors and then it would be possible to move them to other vicinity. [ABSTRACT FROM AUTHOR]

The escalating demand for sustainable and environmentally friendly energy sources has driven substantial growth in renewable energy adoption across residential and industrial sectors. To effectively meet this demand, the development of efficient and sustainable renewable energy conversion methods is crucial, with inverters playing a pivotal role in achieving this objective. While researchers strive to enhance inverter power handling capabilities and reduce output harmonic contents, the incorporation of additional power electronic switches and peripheral devices presents challenges such as increased circuit cost, complexity, and size. Additionally, the utilization of high-frequency switching techniques for achieving low output harmonics results in elevated switching losses and electromagnetic interference, adversely affecting sensitive electronic devices. This research introduces a novel approach to address these issues through the introduction of a reduced switch multilevel inverter topology. Unlike existing systems, the proposed topology employs a reduced number of power electronic switches and direct current sources to generate a stable output voltage waveform. Operating in a symmetric mode, the topology achieves a nine-level output voltage with enhanced harmonic elimination capabilities. A multiple-stepped selective harmonic elimination (SHE-PWM) switching control technique, employing a 1/3/3/1 distribution ratio, is utilized to extend the harmonic elimination range from 3 to 7 lower-order harmonics. To optimize the switching angles required for the proposed topology, the moth flame optimization (MFO) algorithm is employed and compared with particle swarm optimization (PSO) and whale optimization algorithms (WOA). The MFO algorithm exhibits faster convergence to the global optima, achieving an optimal fitness value of 3.322e−08 at 0.78 modulation points. This results in total harmonic distortion values of 0.7%, 0.757%, and 1.069% for MFO, PSO, and WOA, respectively, with corresponding total losses of 71.609W, 71.794W, and 79.792W. The proposed inverter topology is simulated using PSIM software and experimentally verified using a typhoon HIL-402 hardware-in-the-loop testing device. The simulation and experimental results provide compelling evidence for the superior performance of the MFO algorithm compared to PSO and WOA in achieving improved inverter performance. The proposed topology, in conjunction with the MFO algorithm, presents a promising solution for efficient and sustainable renewable energy conversion, thereby contributing to the advancement of renewable energy technologies in both residential and industrial settings. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

Solar photovoltaic energy is the potential energy in the universe for generating electricity and meeting the required load demand. However, on account of partial shading conditions, the difficult task in the PV system is to track global maxima instead of local maxima and maintain the uninterrupted power supply. To solve this problem, a new metaheuristic algorithm is introduced in this paper such as a heap-based optimizer (HBO). The proposed method is developed in MATLAB/Simulink software. The system is examined under distinct irradiation conditions and compared their performance with other methods. The simulation results reveal that the suggested HBO shows a reliable enhancement as compared to other studied methods with regard to tracking maximum power, convergence time, and settling time. The extracted power efficiencies are 99.85% for case 1, 99.96% for case 2, and 99.92% for case 3. It is found that HBO shows better enrichment than other studied methods. [ABSTRACT FROM AUTHOR]

Several optimization-based population search methods have been proposed; they use various operators that permit exploring the search space. These methods typically suffer from local search (LS) problems and are unbalanced between exploration and exploitation. Consequently, recent researchers sought to modify the algorithms to avoid search problems using local search techniques to intensify the exploitation when is necessary. This paper proposes a novel single-based local search optimization algorithm called the non-monopolize search (NO). The NO is a single-solution metaphor-free algorithm, and its operators are designed based to explore and exploit along the iterative process. The NO works only with a candidate solution, and the operators modify the dimension to move the current solution along the search space. The NO is an effective LS method that combines the benefits of exploration with exploitation. Different from other LS, the NO can escape from suboptimal solutions thanks to the randomness incorporated into its operators. This is the main advantage of the NO. Experiments are conducted on standard benchmark functions to validate the performance of the proposed non-monopolize search optimization technique. The results are compared with other well-known methods, and the proposed NO got better results. Moreover, the proposed NO can be considered a powerful alternative to improve the optimization algorithms' performance and help avoid local search problems. Source codes of NO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/156154-the-non-monopolize-search-no. [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]

Deep learning has been widely used in various research fields. However, researchers have discovered that deep learning models are vulnerable to adversarial attacks. Existing word-level attacks can be seen as a combinatorial optimization problem to effectively conduct textual adversarial attacks, but inappropriate search spaces and search methods may affect attack effectiveness. Sentence-level attacks are successfully used in the field of reading comprehension, but the generated examples sometimes lead to semantic deviation. To address these issues, we propose a hybrid textual adversarial attack method that effectively enhances the performance of textual adversarial attacks. To the best of our knowledge, we are the first to conduct textual adversarial attacks by hybridizing Whale Optimization Algorithm (WOA) with style transfer from multiple sentence and word levels. The WOA is improved by incorporating data characteristics and the Metropolis criterion to escape from local optima and by leveraging the mutation operator to increase population diversity. The improved WOA and style transfer algorithm are fused in a parallel and vertical way. Style transfer can increase population diversity and expand the search space, usually without destroying the semantics and syntax of sentences. The parallel combination improves attack performance by attacking from both word-level and sentence-level perspectives. As a black-box attack model, our method can attack without knowing the internal structure of the model. Compared with the state-of-the-art method, our framework can improve the attack success rate by 6.8%. Additionally, further experiments on grammatical error increase rates, semantic consistency, and transferability demonstrate that our model has excellent performance in many respects. [ABSTRACT FROM AUTHOR]

Due to using nature-inspired metaheuristic algorithms to solve real-world applications in different fields, this area has become very popular among researchers because of the flexibility of using optimization algorithms. Therefore, these algorithms can be measured based on their operator, diversity of populations, and balancing between the exploration and exploitation phases. The Grey Wolf Optimization algorithm is one of the competitive metaheuristic algorithms that was proposed in 2014. It mimics the hunting mechanism of wolves. It uses three types of wolves: alpha, beta, and delta for searching in the search space. Despite having competitive performance, GWO has problems regarding local optima and has low exploration. Therefore, enhancement GWO (EGWO) is proposed in this paper in order to solve these problems. EGWO used different methods to improve the performance of GWO: using gamma, z-position, and golden ratio. MGWO is evaluated using CEC2019 ten benchmark functions. Statistical results proved that EGWO outperforms well in six functions out of ten compared to GWO, fitness-dependent optimizer (FDO), cat swarm optimizer, and FOX optimizer. It is also used to solve two real-world applications such as pressure vessel design and 10-bar truss design problems. Results showed that EGWO is very competitive against GWO, FDO, and FOX. [ABSTRACT FROM AUTHOR]

Document classification is becoming increasingly essential for the vast number of documents available in digital libraries, emails, the Internet, etc. Textual records frequently contain non-discriminative (noisy and irrelevant) terms that are also high-dimensional, resulting in higher computing costs and poorer learning performance in Text Classification (TC). Feature selection (FS), which tries to discover discriminate terms or features from the textual data, is one of the most effective tasks for this issue. This paper introduces a novel multi-stage term-weighting scheme-based FS model designed for the single-label TC system to obtain the optimal set of features. We have also developed a hybrid deep learning fine-tuning network based on Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for the classification stage. The FS approach is worked on two-stage criteria. The filter model is used in the first stage, and the multi-objective wrapper model, an upgraded version of the Whale Optimization Algorithm (WOA) with Particle Swarm Optimization (PSO), is used in the second stage. The objective function in the above wrapper model is based on a tri-objective principle. It uses the Pareto front technique to discover the optimal set of features. Here in the wrapper model, a novel selection strategy has been introduced to select the whale instead of the random whale. The proposed work is evaluated on four popular benchmark text corpora, of which two are binary class, and two are multi-class. The suggested FS technique is compared against classic Machine Learning (ML) and deep learning classifiers. The results of the experiments reveal that the recommended FS technique is more effective in obtaining better results than the other results. [ABSTRACT FROM AUTHOR]

This paper aims to solve the Chinese Postman Problem (CPP) using an Ant Colony Optimization (ACO) algorithm. In graph theory, the CPP looks for the shortest closed path that visits every edge of a connected undirected graph. This problem has many applications, including route optimization, interactive system analysis, and flow design. Although numerous algorithms aimed at solving CPP are present in the literature, very few meta-heuristic algorithms are proposed, and no ACO applications have been proposed to solve them. This paper tries to fill this gap by presenting an ACO algorithm that solves CPP (ACO-CPP). To prove its consistency and effectiveness, ACO-CPP is compared with a Genetic Algorithm (GA) and a recursive algorithm throughout three experiments: (1) recursive-ACO-GA comparisons over randomly generated graphs for the attainment of the global optimum; (2) ACO-GA statistical comparisons over specifically generated graphs; (3) recursive-ACO-GA comparisons by changing ACO hyperparameters over randomly generated graphs for the attainment of the global optimum. The experiments prove that the ACO-CPP algorithm is efficient and exhibits a consistency similar to GA when the number of possible solutions to explore is relatively low. However, when that number greatly exceeds those explored, ACO outperforms GA. This suggests that ACO is more suitable for solving problems with a CPP structure. [ABSTRACT FROM AUTHOR]

Hybridization of metaheuristic algorithms has recently been introduced to increase the search capabilities of a traditional metaheuristic algorithms. The fuzzy optimization approach (FOA) is a metaheuristic algorithm that implements fuzzy logic systems to incorporate the knowledge and experience of an expert metaheuristic designer. This integration enables the FOA to guide the search strategy throughout the optimization process. It has been widely applied in high-dimensional problems due to its robustness. However, it presents some issues, such as a slow convergence rate, low exploration and exploitation mechanisms, and high computational effort. In this paper, hybrid search mechanisms are implemented to the original structure of FOA to increase its performance in terms of search capabilities over the limits of the fitness functions. The proposed method called ODM-FOA combines the advantages of Metropolis Hasting (MH) initialization, opposition-based learning (OBL), and Diversity measures to correctly identify and register prominent areas within the search space by approximating the fitness function and improving the exploration and exploitation of the search space. A comparison between ODM-FOA and the original FOA is implemented. Additionally, to evaluate the performance of the developed scheme, it is compared among six well-known metaheuristic algorithms on a set of benchmark functions. Experimental results demonstrate the effectiveness and robustness of the proposed approach against the other original FOA and the other methodologies in terms of solution quality, dimensionality, similarity, and convergence criteria. [ABSTRACT FROM AUTHOR]

Vortex search (VS) algorithm is a recently proposed swarm intelligence or evolutionary algorithm for solving continuous optimization problems inspired by the behavior of whirlpool. In this study, an approach based on VS algorithm is proposed to deal with uncapacitated facility location problem (UFLP) which is a pure problem in binary domain. The update mechanism of VS algorithm is not sufficiently useful for solving the binary optimization problems; therefore, a binary form of VS method called modified binary vortex search (for short MBVS) is proposed for solving UFLPs. Three important changes have been carried out on basic VS algorithm such as (1) converting continuous values to binary values; (2) using genetic mutation operators for enhancing the exploration ability and (3) a local search mechanism for extending the exploitation ability. Based on these changes, MBVS has been tested on fifteen different UFLP instances. The UFLPs dealt with in this study is one of the famous binary optimization problems. It is widely used for comparing the performance of superior algorithms. Once an analysis of 10 different transfer functions, a genetic mutation operator, a local search parameter and population size have been made on proposed method; then, it has been compared with some binary metaheuristic methods and their variants: genetic algorithm (GA)-based approaches such as GA-SP, GA-TP and GA-UP; binary particle swarm optimization algorithm (BPSO); binary versions of artificial bee colony (ABC) algorithm such as binABC, DisABC and ABCbin; binary versions of differential evolution (DE) algorithm such as DisDe/rand and binDE and binary variants of the artificial algae algorithm (AAA) such as AAA-Tanh, AAA-Sig and binAAA methods. The experimental results and comparisons reveal that MBVS algorithm is highly competitive and robust optimizer for the problem addressed in this study. [ABSTRACT FROM AUTHOR]

The process of feature mapping is used to represent features on a map with their corresponding properties. The features are displayed visually and their associated information is made available. Various abstraction approaches, including alignment approaches, are introduced to reduce the computational complexity. However, previous works based on standard treatments of feature maps often suffer from the negative effects of noise and background details. To solve this problem, we present a simple and effective implementation approach for Filter Pruning via Attention Consistency (FPAC), which determines a revolutionary filter pruning mechanism. Feature maps that concentrate on a single layer are inconsistent, which can affect the spatial dimension. The feature map with minimum consistency is less significant and is experimentally demonstrated. This study presents a novel layer-wise pruning technique using the Aphid Ant Mutualism (AAM) algorithm, which considers the sensitivity of various convolutional network layers to model inference and sets the optimal pruning ratio. The accuracy of the compressed model is enhanced by eliminating high redundancy through pruning correlated filters. The performance of FPAC is confirmed through the Caltech 256 image dataset. With VGG-16 on the Caltech 256 image dataset, the classification accuracy is enhanced from 93.96 to 94.03%. With ResNet-50 on the Caltech 256 image dataset, 45% FLOPs are achieved with an accuracy loss of only 0.53%. [ABSTRACT FROM AUTHOR]

Order acceptance and scheduling (OAS) is a critical part of production planning in manufacturing units. In real-world problems, where the manufacturing units are required to meet capacity limitations and delivery time obligations, OAS problem arises. In this study, a hybrid population-based heuristic of imperialists competitive algorithm (ICA) and well-known simulated annealing (SA) has been applied to deal with the variant of OAS with time windows and sequence-dependent setup time. As part of ICA, a specific representation has been proposed to capture all of the characteristics of an OAS solution. The neighborhood functions have been developed based on this specific representation. In order to improve conventional ICA, a roulette wheel is used for selecting these functions, which is periodically updated based on their prior effectiveness. As an additional approach to avoid being confined to a local optimum point, an escape strategy is employed. This part of the study utilizes eight local search functions that are different from those in the ICA in order to further diversify the population. The proposed algorithm has been implemented on benchmark instances, and results have been analyzed. The results of our experiments demonstrated that ICA-SA shows improvement over well-known existing methods both in terms of quality and speed. In addition, we found in 32 out of 1500 cases, ICA-SA yielded a better result than the upper bound generated by MILP. This improvement in optimal solutions was made possible due to the allowance of scheduling tasks with zero revenue in this study. Therefore by relaxing one of the constraints of MILP, better upper bounds can be achieved. [ABSTRACT FROM AUTHOR]

Autonomous flying robots (AFRs) have captured significant interest owing to their agile maneuverability, adaptability, and economical viability. However, the pursuit of enhancing their trajectory tracking performance presents an ongoing challenge. In light of this, our work introduces an innovative strategy that integrates optimization metaheuristic algorithms with a robust hybrid control framework for AFRs, resulting in an optimized and robust controller tailored for autonomous quadrotor robots. By optimizing the controller parameters, we aim to minimize the tracking error and improve the overall performance of AFRs. To evaluate our approach, this study comprehensively analyzes four metaheuristic algorithms in addition to the Improved Grey Wolf Optimization (I-GWO) which outperforms others in quality, convergence rate, and robustness. The proposed I-GWO integration yields a tracking error of 23.25, surpassing Grey Wolf Optimizer (GWO) (24.36), Artificial Bee Colony (ABC) (29.63), and Sine Cosine Algorithm (SCA) (2481.56). The I-GWO has also achieved its minimum objective value within less than 20 iterations compared to other algorithms. Extensive simulations show that our framework achieves optimal and accurate trajectory tracking, critical for safe and efficient AFR operations in various applications. This study emphasizes the importance of choosing suitable optimization algorithms and provides a systematic method for tuning controller gains applicable to different AFR types and control problems. Our contributions could advance more reliable and advanced AFR development in areas such as agriculture, inspection, monitoring, and search and rescue operations. A supplemental animated simulation of this work is available at https://youtu.be/aJMq8ROW51g. [ABSTRACT FROM AUTHOR]

Brain tumor is an active research topic in the area of medical imaging due to only 36% survival rate. The malignant tumor is a more dangerous type of tumor. The recent facts and figures show that 700,000 American are living in brain tumor and from them, 30.25% tumors are malignant. The diagnosis at the initial stage can help to minimize the human mortality rate due to brain tumor. Several researchers of computer vision proposed computer-aided diagnosis systems for this purpose but they faced several issues such as low accuracy and higher computational time. In this work, an end-to-end optimized deep learning system for multimodal brain tumor classification is proposed. The BRATS datasets are utilized for evaluation. The contrast is enhanced at the very first step by using hybrid division histogram equalization along ant colony optimization approach and train a newly design nine-layered CNN model. The features are extracted from second fully connected layer and optimized through differential evolution and mouth flame optimization. The output of both methods is fused using matrix length approach and passed in multi-class support vector machine (MC-SVM). In the experimental process, suggested method achieved an accuracy of 99.06, 98.76, 98.18 and 94.6%, on BRATS 2013, BRATS 2015, BRATS 2017 and BRATS 2018, respectively. Comparing results with existing techniques shows the superiority of proposed technique. [ABSTRACT FROM AUTHOR]

A population-based metaheuristic algorithm that takes its cues from the foraging strategy of sparrows is called the sparrow search algorithm (SSA). While SSA is competitive when compared to other algorithms, it nevertheless has a propensity to carry out imbalanced exploitation and exploration and find the local optimum. Therefore, the modified adaptive sparrow search algorithm (MASSA), an SSA modification, is created to address these problems. To increase population variety, the MASSA uses a chaotic reverse learning technique. Second, to balance the exploitation and exploration capacities, a dynamic adaptive weight is added. In the end, an adaptive spiral search technique improves algorithm performance. Among 23 classical test functions, of which 13 are multidimensional and the other 10 are fixed dimensional, the best chaotic operator is found. It is proven that MASSA is superior. Simulation studies demonstrate that the MASSA described in this study is superior to previous algorithms in terms of stability, convergence speed, and convergence accuracy. Finally, a sample robot path planning problem is resolved using MASSA, and the experimental outcomes confirmed the viability and usefulness of MASSA. [ABSTRACT FROM AUTHOR]