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This paper proposes a new meta-heuristic optimization algorithm called Cleaner Fish Optimization algorithm (CFO) inspired by cleaner fish. The CFO simulates the movement behavior of cleaner fish when performing "cleaning services" and the behavior of female may change its sex to become a male, and defines two modes of position update. In addition, a two-generation cycle operation strategy is proposed to realize the optimization process. To verify the effectiveness of the CFO algorithm, 23 well-known CEC benchmark functions, CEC-2017 benchmark functions and 4 engineering design problems are adopted. Simulation results show that the proposed algorithm has a faster convergence rate and better optimal solution when it compared with several other meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Lung cancer causes millions of deaths annually, and CT scans and lung X-rays are common diagnostic tools. However, large datasets can contain noisy and irrelevant features that can affect the effectiveness of deep learning classification systems. Feature selection is a preprocessing step that can minimize database dimensionality and improve classification accuracy by selecting essential features. In addition, to solve the disadvantage of expensive solution set analysis in the wrapper technique, this study offers an efficient evaluation methodology that significantly decreases the evaluation cost and enhances the efficacy of the feature selection method. Hence, the study proposed a Hybrid Spotted Hyena Optimization with Seagull Algorithm to solve feature selection problems, which efficiently obtained the optimum subset with the largest number of relevant attributes. Biomedical lung data are collected from the LIDC/IDRI and chest X-ray datasets, and the bicubic-interpolation approach is employed to eliminate noise present in the data. We used generative modeling technique, DCGAN, to perform data augmentation. The chosen lung characteristics are evaluated using a hybrid CNN-LSTM, which identified normal and abnormal features in biomedical lung data. The system's efficacy is determined by examining its accuracy, precision, recall, specificity, and sensitivity using a Python experimental design. In the LIDC/IDRI database, the proposed classifier achieved a sensitivity of 99.8%, specificity of 99.3%, precision of 99.14%, and accuracy of 99.6%. In the chest X-ray dataset, the classifier achieved a sensitivity of 99.62%, specificity of 97.8%, precision of 97.5%, and accuracy of 99.7%. [ABSTRACT FROM AUTHOR]

On the Internet, online microblogging and social networks have experienced tremendous growth. Millions of individuals share opinions on social networking platforms including Twitter, Facebook, YouTube, and Microblogging sites. Millions of individuals express their thoughts on social platforms namely Twitter, Facebook, YouTube, and microblogging sites. As user‐generated content is growing to a huge extent on the internet, the analysis, extraction identification and classification of opinion and sentiment polarity of different aspects has now become a challenging issue in sentiment analysis. In sentiment analysis, aspect level opinion mining, opinion spam review detection, extraction and representation of most relevant accessed data from the user‐generated data is a difficult process. The word vector representation using existing deep learning techniques affects the performance in aspect level opinion mining. Spammers propagate false opinion for payment exchange, which leads to degradation of the financial growth, business, and fame of the organization. To overcome the limitations aspect level opinion mining is used to recognize sentiment and polarity of aspects in the specified text. This work presents various input feature vectors of deep learning approaches aiming to improve performance in aspect level opinion mining. Next, three meta‐heuristic algorithms with k‐means clustering approach for classification of opinion spam reviews on social media transit tweets is presented. The proposed approaches are evaluated and compared with other existing approaches as well as other benchmark datasets namely Restaurant and Amazon reviews. [ABSTRACT FROM AUTHOR]

Electroencephalography is a core technology of brain computer interfaces. Even a few number of electrodes can produce complex signals that are difficult to interpret. This is particularly true when trying to detect complex mental states, such as the identification of human emotions. This work analyzes the impact of feature reduction using the SJTU Emotion EEG data set, considering inter‐subject and inter‐session EEG classification. It is well established that EEG data tends to be subject dependent, and that the brain patterns produced by a person in response to a specific stimuli will tend to change over their lifetime, we can say that the above classification task is one of the most challenging test scenarios. We extract 1068 time and frequency domain features, single and multiple channels. Feature reduction was analyzed using both filter and wrapper based approaches. In the first group we use principal component analysis, Kernel PCA and Locally Linear Embedding, and in the second three meta‐heuristic algorithms are employed. For classification, we apply multi‐layer perceptron, quadratic discriminant analysis, random forest and support vector machines. Results show up to 100% accuracy on the median test error for some splits in the 10‐fold cross‐validation process. Best results were achieved by filter‐based selection, with the best average accuracy reached by Random Forest (93.20%) and Multi‐Layer Perceptron (92.71%), using components found with Kernel PCA. To the best of our knowledge, these classification results are the best ever achieved for the SEED data set on the subject‐independent and session‐independent problem. Comparisons using a Leave‐One‐Subject‐Out cross‐validation setting shows that the proposed methodology achieves state‐of‐the‐art results using only 12 electrodes. These off‐the‐shelf ML techniques, coupled with filter based feature reduction, achieve the best results, outperforming complex classification models and wrapper based meta‐heuristics. [ABSTRACT FROM AUTHOR]

Deep learning and metaheuristic algorithms have recently increased in various sciences, including financial accounting information systems (FAISs). However, the existence of large datasets has dramatically increased the complexity of these hybrid networks, so to address this shortcoming, this paper aims to develop a quantum‐behaved chimp optimization algorithm (QCHOA) and deep neural network (DNN) for the prediction of the profit based on FAISs. Considering that there is no suitable dataset for the challenge, a novel dataset is developed utilizing the 15 features from the Chinese market dataset to compare more. This work designs QCHOA and five DNN‐based predictors to forecast profit. These algorithms include the universal learning CHOA (ULCHOA), the niching CHOA (NCHOA) as the two best‐modified versions of CHOA, the quantum‐behaved whale optimization algorithm (QWOA), and the quantum‐behaved grey wolf optimizer (QGWO) as the two best quantum‐behaved optimizers as well as classic CHOA. The most effective deep learning‐based predictors for forecasting the profit, ranked from highest to lowest, are DNN‐QCHOA, DNN‐NCHOA, DNN‐QWOA, DNN‐QGWO, DNN‐ULCHOA, DNN‐CHOA, and classic DNN, with corresponding ranking scores of 42, 36, 30, 24, 18, 12, and 6. As a final suggestion for profit prediction, the DNN‐CHOA is shown to be the most accurate model. [ABSTRACT FROM AUTHOR]

Anaemia occurs when the haemoglobin (Hgb) value falls below a certain reference range. It requires many blood tests, radiological images, and tests for diagnosis and treatment. By processing medical data from patients with artificial intelligence and machine learning methods, disease predictions can be made for newly ill individuals and decision‐support mechanisms can be created for physicians with these predictions. Thanks to these methods, which are very important in reducing the margin of error in the diagnoses made by doctors, the evaluation of data records in health institutions is also important for patients and hospitals. In this study, six hybrid models are proposed to classify non‐anaemia records, Hgb‐anaemia, folate deficiency anaemia (FDA), iron deficiency anaemia (IDA), and B12 deficiency anaemia by combining artificial intelligence and machine learning methods TreeBagger, Crow Search Algorithm (CSA), Chicken Swarm Optimization Algorithm (CSO) and JAYA methods. The proposed hybrid models are analysed with two different approaches, with/without applying the SMOTE technique to achieve high performance by better emphasizing the importance of parameters. To solve the multiclass anaemia classification problem, fuzzy logic‐based parameter optimization is applied to improve the class‐based accuracy as well as the overall accuracy in the dataset. The proposed methods are evaluated using ROC criteria to build a prediction model to determine the anaemia type of anaemic patients. As a result of the study on the dataset taken from the Kaggle database, it is observed that the six proposed hybrid methods outperformed other studies using the same dataset and similar studies in the literature. [ABSTRACT FROM AUTHOR]

Binary combinatorial optimization plays a crucial role in various scientific and engineering fields. While deterministic approaches have traditionally been used to solve these problems, stochastic methods, particularly metaheuristics, have gained popularity in recent years for efficiently handling large problem instances. Ant Colony Optimization (ACO) is among the most successful metaheuristics and is frequently employed in non‐binary combinatorial problems due to its adaptability. Although for binary combinatorial problems ACO can suffer from issues such as rapid convergence to local minima, its eminently parallel structure means that it can be exploited to solve large and complex problems also in this field. In order to provide a versatile ACO implementation that achieves competitive results across a wide range of binary combinatorial optimization problems, we introduce a parallel multicolony strategy with an improved cooperation scheme to maintain search diversity. We evaluate our proposal (Binary Parallel Cooperative ACO, BiPCACO) using a comprehensive benchmark framework, showcasing its performance and, most importantly, its flexibility as a successful all‐purpose solver for binary combinatorial problems. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have gained considerable popularity, driven in part by an increased concern for the impact of automobile emissions on climate change. Electric vehicles (EVs) cover more than just conventional cars and trucks. They also include electric motorcycles, such as those produced by Gogoro, which serve as the primary mode of transportation for food and package delivery services in Taiwan. Consequently, the Electric Vehicle Routing Problem (EVRP) has emerged as an important variation of the Capacitated Vehicle Routing Problem (CVRP). In addition to the CVRP's constraints, the EVRP requires vehicles to visit a charging station before the battery level is insufficient to continue service. EV battery consumption is linearly correlated to their weight. These additional constraints make the EVRP more challenging than the conventional CVRP. This study proposes an improved Harmony Search Algorithm (HSA), with performance validated by testing 24 available benchmark instances in the EVRP. This study also proposes a novel update mechanism in the improvement stage and a strategy to improve the routes with charging stations. The results show that in small and large instances, the proposed HSA improved the number of trips to the charging stations by 24% and 4.5%, respectively. These results were also verified using the Wilcoxon signed-rank significant test. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive comparative analysis of optimization techniques for achieving the optimal design of cellular beams. The incorporation of gaps within cellular beams reduces the weight of the beam and increases section height, resulting in the production of lighter and stronger sections. The Taguchi method is employed to fine-tune the parameters of the Grey Wolf Optimizer, enabling the achievement of a robust design. The performance of each algorithm is evaluated through three design examples, facilitating comprehensive comparisons among the seven algorithms. Moreover, the study encompasses the modeling and analysis of optimally designed cellular beams using finite element software. [ABSTRACT FROM AUTHOR]

Reliable estimation of reference evapotranspiration (ETo), an essential component of optimal irrigation management, is challenging in many regions due to its complex dependence on meteorological factors. Alternative empirical models, often used to estimate ETo considering data limitations, provide highly unreliable estimates for Iraq. This study aimed to formulate simpler empirical models for accurate ETo estimation with fewer variables in different climate regions of Iraq. The metaheuristic Whale Optimization Algorithm (WOA) was used to finetune the coefficients of the nonlinear least square fitting regression (NLLSF) model during development. Two simpler models were developed based on (1) only mean air temperature (T) (NLLSF-T) and (2) solar radiation and T (NLLSF-R) as inputs. The performance of the models was validated using historical ground observations (2012–2021), and the ETo was estimated using the Penman–Monteith method from the reanalyzed (ERA5) datasets (1959–2021). The models' spatial, seasonal, and temporal performance in estimating daily ETo was rigorously evaluated using multiple statistical metrics and visual presentations. The Kling-Gupta Efficiency (KGE) and normalized root mean square error (NRMSE) of the NLLSF-T model were 0.95 and 0.30, respectively, compared to 0.75 and 0.40 for Kharrufa, the best-performing temperature-based models in Iraq. Similarly, NLLSF-R improved the KGE from 0.78 to 0.97 in KGE and NRMSE from 0.44 to 0.22 compared to Caprio, the best-performing radiation-based model in Iraq. The spatial assessment revealed both the models' excellent performance over most of Iraq, except in the far north, indicating their suitability in estimating ETo in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a significant dimensionality reduction technique in machine learning and data mining that is adept at managing high-dimensional data efficiently and enhancing model performance. Metaheuristic algorithms have become one of the most promising solutions in FS owing to their powerful search capabilities as well as their performance. In this paper, the novel improved binary walrus optimizer (WO) algorithm utilizing the golden sine strategy, elite opposition-based learning (EOBL), and population regeneration mechanism (BGEPWO) is proposed for FS. First, the population is initialized using an iterative chaotic map with infinite collapses (ICMIC) chaotic map to improve the diversity. Second, a safe signal is obtained by introducing an adaptive operator to enhance the stability of the WO and optimize the trade-off between exploration and exploitation of the algorithm. Third, BGEPWO innovatively designs a population regeneration mechanism to continuously eliminate hopeless individuals and generate new promising ones, which keeps the population moving toward the optimal solution and accelerates the convergence process. Fourth, EOBL is used to guide the escape behavior of the walrus to expand the search range. Finally, the golden sine strategy is utilized for perturbing the population in the late iteration to improve the algorithm's capacity to evade local optima. The BGEPWO algorithm underwent evaluation on 21 datasets of different sizes and was compared with the BWO algorithm and 10 other representative optimization algorithms. The experimental results demonstrate that BGEPWO outperforms these competing algorithms in terms of fitness value, number of selected features, and F1-score in most datasets. The proposed algorithm achieves higher accuracy, better feature reduction ability, and stronger convergence by increasing population diversity, continuously balancing exploration and exploitation processes and effectively escaping local optimal traps. [ABSTRACT FROM AUTHOR]

Based on a meta-heuristic secretary bird optimization algorithm (SBOA), this paper develops a multi-strategy improvement secretary bird optimization algorithm (MISBOA) to further enhance the solving accuracy and convergence speed for engineering optimization problems. Firstly, a feedback regulation mechanism based on incremental PID control is used to update the whole population according to the output value. Then, in the hunting stage, a golden sinusoidal guidance strategy is employed to enhance the success rate of capture. Meanwhile, to keep the population diverse, a cooperative camouflage strategy and an update strategy based on cosine similarity are introduced into the escaping stage. Analyzing the results in solving the CEC2022 test suite, the MISBOA both get the best comprehensive performance when the dimensions are set as 10 and 20. Especially when the dimension is increased, the advantage of MISBOA is further expanded, which ranks first on 10 test functions, accounting for 83.33% of the total. It illustrates the introduction of improvement strategies that effectively enhance the searching accuracy and stability of MISBOA for various problems. For five real-world optimization problems, the MISBOA also has the best performance on the fitness values, indicating a stronger searching ability with higher accuracy and stability. Finally, when it is used to solve the shape optimization problem of the combined quartic generalized Ball interpolation (CQGBI) curve, the shape can be designed to be smoother according to the obtained parameters based on MISBOA to improve power generation efficiency. [ABSTRACT FROM AUTHOR]

Inspired by classical experiments that uncovered the inherent properties of light waves, Young's Double-Slit Experiment (YDSE) optimization algorithm represents a physics-driven meta-heuristic method. Its unique search mechanism and scalability have attracted much attention. However, when facing complex or high-dimensional problems, the YDSE optimizer, although striking a good balance between global and local searches, does not converge as fast as it should and is prone to fall into local optimums, thus limiting its application scope. A fractional-order boosted hybrid YDSE, called FYDSE, is proposed in this article. FYDSE employs a multi-strategy mechanism to jointly address the YDSE problems and enhance its ability to solve complex problems. First, a fractional-order strategy is introduced into the dark edge position update of FYDSE to ensure more efficient use of the search potential of a single neighborhood space while reducing the possibility of trapping in a local best. Second, piecewise chaotic mapping is constructed at the initial stage of the population to obtain better-distributed initial solutions and increase the convergence rate to the optimal position. Moreover, the low exploration space is extended by using a dynamic opposition strategy, which improves the probability of acquisition of a globally optimal solution. Finally, by introducing the vertical operator, FYDSE can better balance global exploration and local exploitation and explore new unknown areas. The numerical results show that FYDSE outperforms YDSE in 11 (91.6%) of cec2022 sets. In addition, FYDSE performs best in 8 (66.6%) among all algorithms. Compared with the 11 methods, FYDSE obtains the optimal best and average weights for the 20-bar, 24-bar, and 72-bar truss problems, which proves its efficient optimization capability for difficult optimization cases. [ABSTRACT FROM AUTHOR]

In this article, a production–assembly scheduling problem is presented in three steps. In the first step, the production operation is performed by dedicated parallel machines; in the second step, the assembly operation is performed by the same parallel machines, and in the third step, the post-assembly operation is performed by one machine, and these three steps exist in parallel factories. This problem is NP hard and cannot be solved in a reasonable time by mathematical models. Therefore, a mixed integer linear programming algorithm is presented for small-sized problems, and a new metaheuristic algorithm is presented for large-sized problems, by combining quantum-behaved particle swarm optimization (QPSO), shortest processing time (SPT) and dominance rules, which is called the hybrid QPSOSPT dominance rules (HQSD) algorithm. Accurate parameter adjustment was achieved by analysis of variance. The HQSD algorithm was shown to obtain better results than the other algorithms. [ABSTRACT FROM AUTHOR]

The minimum multiplex arrangement problem locates each vertex of a graph on an integer point on a line, with each integer point being assigned at most $ \mathfrak {m} $ m vertices. The case with $ \mathfrak {m}=2 $ m = 2 , known as the minimum duplex arrangement (MinDA) problem, is investigated in this study. MinDA is $ \mathscr {N}\!\!\mathscr {P} $ N P -hard and is related to applications such as manufacturing systems design and VLSI layout. A mixed-integer linear programming formulation of MinDA is presented. The capabilities of the proposed formulation are assessed on 63 problem instances with varying densities and sizes up to 25 vertices. Additionally, an examination of the structure of an optimal MinDA solution is conducted. Based on this analysis, an iterated local search (ILS) algorithm is established, and novel strategies are developed to enhance its effectiveness. In contrast to traditional ILS approaches, where the local search phase invariably reaches a local optimum, the local search phase in the proposed algorithm may converge to a point in the vicinity of a local optimum, thereby reducing the computational burden for large-scale instances. Extensive computational experiments employing the ILS algorithm are conducted on instances with sizes n up to 10,240 vertices. [ABSTRACT FROM AUTHOR]

Precise estimation of daily reference crop evapotranspiration (ET0) is critical for water resource management and agricultural irrigation optimization worldwide. In China, diverse climatic zones pose challenges for accurate ET0 prediction. Here, we evaluate the performance of a support vector machine (SVM) and its hybrid models, PSO-SVM and WOA-SVM, utilizing meteorological data spanning 1960–2020. Our study aims to identify a high-precision, low-input ET0 estimation tool. The findings indicate that the hybrid models, particularly WOA-SVM, demonstrated superior accuracy with R2 values ranging from 0.973 to 0.999 and RMSE values between 0.123 and 0.863 mm/d, outperforming the standalone SVM model with R2 values of 0.955 to 0.989 and RMSE values of 0.168 to 0.982 mm/d. The standalone SVM model showed relatively lower accuracy with R2 values of 0.822 to 0.887 and RMSE values of 0.381 to 1.951 mm/d. Notably, the WOA-SVM model, with R2 values of 0.990 to 0.992 and RMSE values of 0.092 to 0.160 mm/d, emerged as the top performer, showcasing the benefits of the whale optimization algorithm in enhancing SVM's predictive capabilities. The PSO-SVM model also presented improved performance, especially in the temperate continental zone (TCZ), subtropical monsoon region (SMZ), and temperate monsoon zone (TMZ), when using limited meteorological data as the input. The study concludes that the WOA-SVM model is a promising tool for high-precision daily ET0 estimation with fewer meteorological parameters across the different climatic zones of China. [ABSTRACT FROM AUTHOR]

The uniaxial compressive strength (UCS) is an essential parameter to study rock characteristics, determined by direct and indirect methods. However, the direct methods of determining rock UCS are lengthy and arduous, presenting a requirement of developing a robust indirect method. This investigation introduces an optimal computational model based on the artificial intelligence approach. For that aim, the relevance vector machine (RVM) and long short-term memory (LSTM) approaches have employed 24 computational models in estimating rock UCS and introducing an optimal computational model. The 514, 146, and 74 data points have trained, tested, and validated the 24 models compiled from the literature. The model comparison reveals that implementing a secondary kernel function in a single kernel-based RVM model enhances the performance. Moreover, the particle swarm (PSO) optimized dual kernel-based RVM model attains higher performance than genetic (GA) optimized RVM models. Conversely, the Adam-optimized LSTM model achieved higher performance than other LSTM models. The overall comparison introduces model MD21 (PSO-optimized RVM model implemented by dual kernels, i.e., Laplacian + polynomial) as an optimal performance model in predicting rock UCS with a correlation coefficient of 0.9979 (in the testing phase) and 0.9939 (in the validation phase). In addition, the multicollinearity analysis reveals that the database variables consist of weak multicollinearity. It is noted that the optimized RVM and LSTM models attain overfitting because of weak multicollinearity and optimization algorithms. The analysis of variance (ANOVA), Z, and Anderson–Darling tests accept the research hypothesis for the compiled database. In addition, the trend of simulated results illustrates the robustness and correctness of the employed model MD21. Highlights: Implementation of hybrid relevance vector machine and long short-term memory computational models to assess uniaxial compressive strength of rock. Comparison of genetic and particle swarm optimized relevance vector machine model to find the best optimization algorithm. Comparison of Adam, root-mean-square proportion, and stochastic gradient descent with momentum optimized long short-term memory models. Illustration of the effect of multicollinearity on performance and curve fitting of optimized computational models. Particle swarm optimized dual kernel-based relevance vector machine is an optimal computational model for assessing rock strength. [ABSTRACT FROM AUTHOR]

This study discusses a new method for the fractional-order system reduction. It offers an adaptable framework for approximating various fractional-order systems (FOSs), including commensurate and non-commensurate. The fractional-order modeling and control (FOMCON) toolbox in MATLAB and the gaze cues learning-based grey wolf optimizer (GGWO) technique form the basis of the recommended method. The fundamental advantage of the offered method is that it does not need intermediate steps, a mathematical substitution, or an operator-based approximation for the order reduction of a commensurate and non-commensurate FOS. The cost function is set up so that the sum of the integral squared differences in step responses and the root mean squared differences in Bode magnitude plots between the original FOS and the reduced models is as tiny as possible. Two case studies support the suggested method. The simulation results show that the reduced approximations constructed using the methodology under consideration have step and Bode responses more in line with the actual FOS. The effectiveness of the advocated strategy is further shown by contrasting several performance metrics with some of the contemporary approaches disseminated in academic journals. [ABSTRACT FROM AUTHOR]

With the exponential growth of devices, particularly Internet of things (IoT) devices, connecting to wireless networks, existing networks face significant challenges. Spectral efficiency is crucial for uplink, which is the dominant form of asymmetrical network in today's communication landscape, in large-scale connectivity scenarios. In this paper, an uplink transmission scenario is considered and user equipment (UE) aggregation is employed, wherein some users act as cooperative nodes (CNs), and help to forward received data from other users requiring coverage extension, reliability improvement, and data–rate enhancement. Non–orthogonal multiple access (NOMA) technology is introduced to improve spectral efficiency. To reduce the interference impact to guarantee the data rate, one UE can be assisted by multiple CNs, and these CNs and corresponding assisted UEs are clustered into joint transmission pairs (JTPs). Interference-free transmission can be achieved within each JTP by utilizing different successive interference cancellation (SIC) decoding orders. To explore SIC gains and maximize data rates in NOMA–based UE aggregation, we propose a primary user CN–based channel–sorting algorithm for JTP construction and apply a whale optimization algorithm for JTP power allocation. Additionally, a conflict graph is established among feasible JTPs, and a greedy strategy is employed to find the maximum weighted independent set (MWIS) of the conflict graph for subchannel allocation. Simulation results demonstrate that our joint collaborative NOMA (JC–NOMA) design with two–hop multi–path UE aggregation significantly improves spectral efficiency and capacity under limited spectral resources. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel optimization algorithm called the OX optimizer, inspired by oxen animals, which are characterized by their great strength. The OX optimizer is designed to address the challenges posed by complex, high-dimensional optimization problems. The design of the OX optimizer embodies a fundamental symmetry between global and local search processes. This symmetry ensures a balanced and effective exploration of the solution space, highlighting the algorithm's innovative contribution to the field of optimization. The OX optimizer has been evaluated on CEC2022 and CEC2017 IEEE competition benchmark functions. The results demonstrate the OX optimizer's superior performance in terms of convergence speed and solution quality compared to existing state-of-the-art algorithms. The algorithm's robustness and adaptability to various problem landscapes highlight its potential as a powerful tool for solving diverse optimization tasks. Detailed analysis of convergence curves, search history distributions, and sensitivity heatmaps further support these findings. Furthermore, the OX optimizer has been applied to optimize support vector machines (SVMs), emphasizing parameter selection and feature optimization. We tested it on the NSL-KDD dataset to evaluate its efficacy in an intrusion detection system. The results demonstrate that the OX optimizer significantly enhances SVM performance, facilitating effective exploration of the parameter space. [ABSTRACT FROM AUTHOR]

This paper presents a novel, multi-objective scatter search algorithm (MOSS) for a bi-objective, dynamic, multiprocessor open-shop scheduling problem (Bi-DMOSP). The considered objectives are the minimization of the maximum completion time (makespan) and the minimization of the mean weighted flow time. Both are particularly important for improving machines' utilization and customer satisfaction level in maintenance and healthcare diagnostic systems, in which the studied Bi-DMOSP is mostly encountered. Since the studied problem is NP-hard for both objectives, fast algorithms are needed to fulfill the requirements of real-life circumstances. Previous attempts have included the development of an exact algorithm and two metaheuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective gray wolf optimizer (MOGWO). The exact algorithm is limited to small-sized instances; meanwhile, NSGA-II was found to produce better results compared to MOGWO in both small- and large-sized test instances. The proposed MOSS in this paper attempts to provide more efficient non-dominated solutions for the studied Bi-DMOSP. This is achievable via its hybridization with a novel, bi-objective tabu search approach that utilizes a set of efficient neighborhood search functions. Parameter tuning experiments are conducted first using a subset of small-sized benchmark instances for which the optimal Pareto front solutions are known. Then, detailed computational experiments on small- and large-sized instances are conducted. Comparisons with the previously developed NSGA-II metaheuristic demonstrate the superiority of the proposed MOSS approach for small-sized instances. For large-sized instances, it proves its capability of producing competitive results for instances with low and medium density. [ABSTRACT FROM AUTHOR]

This study presents a novel method, termed RBAVO-DE (Relief Binary African Vultures Optimization based on Differential Evolution), aimed at addressing the Gene Selection (GS) challenge in high-dimensional RNA-Seq data, specifically the rnaseqv2 lluminaHiSeq rnaseqv2 un edu Level 3 RSEM genes normalized dataset, which contains over 20,000 genes. RNA Sequencing (RNA-Seq) is a transformative approach that enables the comprehensive quantification and characterization of gene expressions, surpassing the capabilities of micro-array technologies by offering a more detailed view of RNA-Seq gene expression data. Quantitative gene expression analysis can be pivotal in identifying genes that differentiate normal from malignant tissues. However, managing these high-dimensional dense matrix data presents significant challenges. The RBAVO-DE algorithm is designed to meticulously select the most informative genes from a dataset comprising more than 20,000 genes and assess their relevance across twenty-two cancer datasets. To determine the effectiveness of the selected genes, this study employs the Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classifiers. Compared to binary versions of widely recognized meta-heuristic algorithms, RBAVO-DE demonstrates superior performance. According to Wilcoxon's rank-sum test, with a 5% significance level, RBAVO-DE achieves up to 100% classification accuracy and reduces the feature size by up to 98% in most of the twenty-two cancer datasets examined. This advancement underscores the potential of RBAVO-DE to enhance the precision of gene selection for cancer research, thereby facilitating more accurate and efficient identification of key genetic markers. [ABSTRACT FROM AUTHOR]

Flux Balance Analysis (FBA) is a constraint-based method that is commonly used to guide metabolites through restricting pathways that often involve conditions such as anaplerotic cycles like Calvin, reversible or irreversible reactions, and nodes where metabolic pathways branch. The method can identify the best conditions for one course but fails when dealing with the pathways of multiple metabolites of interest. Recent studies on metabolism consider it more natural to optimize several metabolites simultaneously rather than just one; moreover, they point out the use of metaheuristics as an attractive alternative that extends FBA to tackle multiple objectives. However, the literature also warns that the use of such techniques must not be wild. Instead, it must be subject to careful fine-tuning and selection processes to achieve the desired results. This work analyses the impact on the quality of the pathways built using the NSGAII and MOEA/D algorithms and several novel optimization models; it conducts a study on two case studies, the pigment biosynthesis and the node in glutamate metabolism of the microalgae Chlorella vulgaris, under three culture conditions (autotrophic, heterotrophic, and mixotrophic) while optimizing for three metabolic intermediaries as independent objective functions simultaneously. The results show varying performances between NSGAII and MOEA/D, demonstrating that the selection of an optimization model can greatly affect predicted phenotypes. [ABSTRACT FROM AUTHOR]

Delivery apps are experiencing significant growth, requiring efficient algorithms to coordinate transportation and generate profits. One problem that considers the goals of delivery apps is the multi-vehicle profitable pickup and delivery problem (MVPPDP). In this paper, we propose eight new metaheuristics to improve the initial solutions for the MVPPDP based on the well-known swarm intelligence algorithm, Artificial Bee Colony (ABC): K-means-GRASP-ABC(C)S1, K-means-GRASP-ABC(C)S2, Modified K-means-GRASP-ABC(C)S1, Modified K-means-GRASP-ABC(C)S2, ACO-GRASP-ABC(C)S1, ACO-GRASP-ABC(C)S2, ABC(S1), and ABC(S2). All methods achieved superior performance in most instances in terms of processing time. For example, for 250 customers, the average times of the algorithms was 75.9, 72.86, 79.17, 73.85, 76.60, 66.29, 177.07, and 196.09, which were faster than those of the state-of-the-art methods that took 300 s. Moreover, all proposed algorithms performed well on small-size instances in terms of profit by achieving thirteen new best solutions and five equal solutions to the best-known solutions. However, the algorithms slightly lag behind in medium- and large-sized instances due to the greedy randomised strategy and GRASP that have been used in the scout bee phase. Moreover, our algorithms prioritise minimal solutions and iterations for rapid processing time in daily m-commerce apps, while reducing iteration counts and population sizes reduces the likelihood of obtaining good solution quality. [ABSTRACT FROM AUTHOR]

This paper proposes a new meta-heuristic optimization algorithm, the crown growth optimizer (CGO), inspired by the tree crown growth process. CGO innovatively combines global search and local optimization strategies by simulating the growing, sprouting, and pruning mechanisms in tree crown growth. The pruning mechanism balances the exploration and exploitation of the two stages of growing and sprouting, inspired by Ludvig's law and the Fibonacci series. We performed a comprehensive performance evaluation of CGO on the standard testbed CEC2017 and the real-world problem set CEC2020-RW and compared it to a variety of mainstream algorithms such as SMA, SKA, DBO, GWO, MVO, HHO, WOA, EWOA, and AVOA. The best result of CGO after Friedman testing was 1.6333/10, and the significance level of all comparison results under Wilcoxon testing was lower than 0.05. The experimental results show that the mean and standard deviation of repeated CGO experiments are better than those of the comparison algorithm. In addition, CGO also achieved excellent results in specific applications of robot path planning and photovoltaic parameter extraction, further verifying its effectiveness and broad application potential in practical engineering problems. [ABSTRACT FROM AUTHOR]

Recently, light fidelity (Li-Fi) technology becomes important for rapid data transmission in the network. Integration of different communication technologies can be used to address the traffic limitations posed by increasing number of Internet users. Hybrid Wi-Fi/Li-Fi networks (HLWN) make use of rapid Li-Fi data transmission and Wi-Fi coverage So, the combination of them can resolve the limitations of both technologies and enhance the efficiency of the overall network. At the same time, the choice of access point (AP) remains a difficult task as the coverage regions of various networks overlapped with one another. Based on instant channel data, the traditional load balancing schemes allocate every client to a certain AP, offering the optimum solutions at a time instance. But it can results in several handoffs for clients and degrade the throughput performance. Therefore, this article develops a Mobility aware LB using Kho–Kho optimization algorithm (MALB-KKOA) for HLWN. The presented MALB-KKOA technique is investigated in two aspects such as It is permissible to have both several AP associations (MA) and a single AP association (SA) (MA). In the SA phase, every client is aided by an AP either Li-Fi or Wi-Fi where the MALB-KKOA model allocates every client to a particular network over a time duration to reduce handover. On the other hand, in the MA phase, every client is concurrently assisted by Li-Fi as well as Wi-Fi, and no vertical handover takes place. In addition, the MALB-KKOA model is mainly based on the approaches utilized by the players in a tag-team game named Kho–Kho. Moreover, the MALB-KKOA model computes an objective function for the minimization of packet loss and delay. The experimental validation of the MALB-KKOA model is tested and the results signified its promising performance over other recent approaches. [ABSTRACT FROM AUTHOR]

In recent decades, the demand for optimization techniques has grown due to rising complexity in real-world problems. Hence, this work introduces the Hyperbolic Sine Optimizer (HSO), an innovative metaheuristic specifically designed for scientific optimization. Unlike conventional approaches, HSO takes a unique approach by engaging individual members of the population, ensuring a comprehensive exploration of solution spaces. Employing distinctive exploration and exploitation phases, coupled with hyperbolic sinh function convergence, the optimizer enhances speed, simplify parameter adjustment, alleviates slow convergence, and demonstrates efficiency in high-dimensional optimization. This approach is designed to tackle optimization challenges and enhance adaptability in unpredictable real-world scenarios. The evaluation of HSO's performance unfolds through four distinct testing phases. Initially, a set of 65 widely recognized benchmark functions is employed. These functions cover both unimodal and multi-modal varieties across dimensions of 30, 100, 500, and 1000, including fixed-dimensional functions, to comprehensively assess the exploration, exploitation, local optima avoidance, and convergence capabilities of the proposed algorithm. The results of the HSO algorithm are then compared to those of 15 state-of-the-art metaheuristic algorithms and 8 recently published algorithms. Secondly, HSO's performance is assessed in comparison with the benchmark suite from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC). This suite includes 15 benchmark functions for CEC-2015 and an additional 30 benchmark functions for CEC-2017. During the third phase, HSO tackles seven real-world classical engineering design problems by addressing both the constrained and unconstrained optimization challenges of IEEE CEC-2020. Finally, HSO undertakes training for a multilayer perceptron, utilizing four distinct datasets. To qualitatively assess HSO's performance, two statistical analyses—the Friedman and T tests—are employed. The findings of HSO showcase its adaptability and effectiveness as a high-performing optimizer in engineering optimization challenges. Note that the source code of the HSO algorithm are publicly accessible via https://github.com/Shivankur07/Hyperbolic-Sine-Optimizer.git. [ABSTRACT FROM AUTHOR]

Anomaly detection (AD) in the industrial internet of things (IIoT) platform is said to be the major module of security the consistency, safety, and efficacy of industrial procedures. In IIoT, several sensors and devices always gather massive data counts from tools, infrastructure, and equipment. Ensuring the safety of IIoT AD systems is vital for protecting against cyber-attacks. AD and Intrusion detection are required to work in tandem to maintain industrial procedures. Machine learning (ML) and deep learning (DL) approaches generally employed for AD in IIoT. These methods allow automated AD and pattern detection. With this motivation, this study develops an enhanced pelican optimization model with an ensemble voting-based anomaly detection (EPOA-EVAD) approach. Primarily, the SMOTE approach is used for class imbalance data handling. The presented EPOA-EVAD technique integrates the EPOA-based feature selection and ensemble voting classifier to enhance the accuracy and efficiency of AD in IIoT environments. In addition, the group teaching optimization algorithm with SMOTE approach is used for the purpose of class imbalance data handling. EPOA optimally chooses the related features from the complex and high-dimensional IIoT data. For AD, the EPOA-EVAD technique involves an ensemble learning process comprising gated recurrent unit (GRU), bi-directional long short-term memory (BiLSTM), and stacked autoencoder (SAE). Lastly, the seagull optimization (SGO) algorithm fine-tunes the parameters compared with the DL approaches. The simulation values exhibited that the proposed system outperforms standard approaches, offering robust and adaptive AD capabilities in dynamic IIoT settings. The proposed EPOA-EVAD technique contributes to the advancement of AD techniques crucial for maintaining the integrity and efficiency of industrial processes in the era of IIoT. [ABSTRACT FROM AUTHOR]

Sleep Apnea (SA) is a breathing disorder that many people experience during sleep. Polysomnography is the best way to diagnose SA, but it requires significant time, cost, and effort. A practical and efficient method of diagnosing SA is using a wearable sensor to record Electrocardiography (ECG) signals. Machine learning algorithms can be used to classify SA by extracting features from ECG signals. Recently, deep learning techniques such as Convolutional Neural Network (CNN) have been used to identify features from ECG data automatically. However, the large number of hyperparameters in CNN makes it challenging to perform this task manually. Metaheuristic algorithms such as Honey Badger Algorithm (HBA) have been successfully applied to tune CNN hyperparameters, but it still has issues with premature convergence. To address these issues, the Bare-Bones Honey Badger Algorithm (BBHBA) is proposed as an improved version of HBA. It improves the exploitation potential of solutions, reduces diversity spillover, and maintains solution diversity. The method generates new candidate solutions using Gaussian search equations and an inverse hyperbolic cosine control mechanism. The greedy selection strategy is used to improve the searcher's capabilities effectively. To validate the proposed deep learning model, the PhysioNet Apnea-ECG database is used. The model achieves an accuracy of 90.92%, a sensitivity of 91.24%, a specificity of 90.36%, and an F1 score of 92.76% on the validation dataset. Overall, the proposed method provides a practical and efficient way to diagnose SA using wearable sensors and deep learning techniques. The BBHBA algorithm improves the performance of CNN by effectively tuning hyperparameters, providing more accurate results in SA diagnosis. [ABSTRACT FROM AUTHOR]

Over the past few years, global use of Online Social Networks (OSNs) has increased. The rising use of OSN makes protecting users' privacy from OSN attacks difficult. Finally, it affects the basic commitment to protect OSN users from such invasions. The lack of a distributed, dynamic, and artificial intelligence (AI)-based privacy-preserving strategy for performance trade-offs is a research challenge. We propose the Distributed Privacy Preservation (DPP) for OSN using Artificial Intelligence (DPP-OSN-AI) to reduce Information Loss (IL) and improve privacy preservation from different OSN threats. DPP-OSN-AI uses AI to design privacy notions in distributed OSNs. DPP-OSN-AI consists of AI-based clustering, l-diversity, and t-closeness phases to achieve the DPP for OSN. The AI-based clustering is proposed for dynamic and optimal clustering of OSN users to ensure personalized k-anonymization to protect from AI-based threats. First, the optimal number of clusters is discovered dynamically with simple computations, and then the Whale Optimization Algorithm is designed to optimally place the OSN users across the clusters such that it helps to protect them from AI-based threats. Because k-anonymized OSN clusters are insufficient to handle all privacy concerns in a distributed OSN environment, we systematically applied the l-diversity privacy idea followed by the t-closeness to it, resulting in higher DPP and lower IL. The DPP-OSN-AI model is assessed for IL Efficiency (ILE), Degree of Anonymization (DoA,) and computational complexity using publically accessible OSN datasets. Compared to state-of-the-art, DPP-OSN-AI model DoA is 15.57% higher, ILE is 17.85% higher, and computational complexity is 3.61% lower. [ABSTRACT FROM AUTHOR]

The continual increase in the amount of generated data by social media, IoT devices, and monitoring systems have motivated the use of Distributed Data Stream Processing (DSP) systems to harness data in a real-time manner. The scheduling of processing tasks in DSP systems across the machines in a cluster or cloud environment is an NP-Hard problem. Different scheduling schemes have been proposed to address the scheduling problem, but most fail to take into account the runtime adaptation and workload changes after initial scheduling. In this paper, we propose a new scheduler (GT-Scheduler) that leverages a heuristic and rule-based algorithm to schedule tasks at near-optimal performance alongside using a meta-heuristic algorithm to make runtime adaptation. Firstly, K-way graph partitioning divides the tasks in an application graph according to the communication patterns. It places tasks with the highest amount of communication near each other to limit an increase in the topology response time. Secondly, instead of assigning tasks to the worker nodes, a partition of tasks is assigned to the nodes by adopting a greedy strategy. If the capacity of nodes is insufficient to host a specific partition of tasks, this partition is iteratively divided by the k-partitioning algorithm to assign to a proper node. The idea of runtime adaptation lies in detecting overutilized worker nodes and reassigning their tasks by exploiting a Tabu-Search and a new scoring strategy to find the best solution in a way that no worker node is overutilized. GT-Scheduler is implemented on the standard Apache Storm and using the standard benchmarks, it is shown that GT-Scheduler outperforms the R-Storm and the Online-Scheduler by at least 35% in reducing the topology response time. [ABSTRACT FROM AUTHOR]

Maintenance planning aims to improve the reliability of assets, prevent the occurrence of asset failures, and reduce maintenance costs associated with downtime of assets and maintenance resources (such as spare parts and workforce). Thus, effective maintenance planning is instrumental in ensuring high asset availability with the minimum cost. Nevertheless, to find such optimal planning is a nontrivial task due to the (i) complex and usually nonlinear inter-relationship between different planning decisions (e.g., inventory level and workforce capacity), and (ii) stochastic nature of the system (e.g., random failures of parts installed in assets). To alleviate these challenges, we study a joint maintenance planning problem by considering several decisions simultaneously, including workforce planning, workforce training, and spare parts inventory management. We develop a hybrid solution algorithm (DRLSA ) that is a combination of Double Deep Q-Network based Deep Reinforcement Learning (DRL) and Simulated Annealing (SA) algorithms. In each episode of the proposed algorithm, the best solution found by DRL is delivered to SA to be used as an initial solution, and the best solution of SA is delivered to DRL to be used as the initial state. Different from the traditional SA algorithms where neighborhood structures are selected only randomly, the DRL part of DRLSA learns to choose the best neighborhood structure to use based on experience gained from previous episodes. We compare the performance of the proposed solution algorithm with several well-known meta-heuristic algorithms, including Simulated Annealing, Genetic Algorithm (GA), and Variable Neighborhood Search (VNS). Further, we also develop a Machine Learning (ML) algorithm (i.e., K-Median) as another benchmark in which different properties of spare parts (e.g., failure rates, holding costs, and repair rates) are used as clustering features for the ML algorithm. Our study reveals that the DRLSA finds the optimal solutions for relatively small-size instances, and it has the potential to outperform traditional meta-heuristic and ML algorithms. [ABSTRACT FROM AUTHOR]

Considering the detrimental impact of thermal phenomena on the geometric precision of machine tools, a machine tool ball screw's omni-directional error model is created using the LSTM neural network algorithm. Subsequently, the machine tool ball screw's omni-directional error compensation module is devised by combining the core functions of the Huazhong numerical control system with the visual programming environment of QT and the numerical computation capability of Matlab. To enhance the practicality and accuracy of the compensation model, this study has employed the Whale Optimization Algorithm (WOA) to optimize the parameters of the LSTM model. This has resulted in an improvement in the model's generalization ability and prediction performance, making it more effective. During the experimental validation phase, the Z-axis error of the machine tool was practically operated and analyzed using the compensation method. Results manifestly show that, after employing the compensation method, the peak amplitude of the Z-axis error fluctuations have been notably curtailed to ±0.006 mm -- a considerable reduction compared to the initial error bandwidth of ±0.0145 mm. These empirical findings substantiate the efficacy of the proposed compensation strategy in substantially boosting the machining precision of products, thus furnishing a substantial and instructive benchmark for future inquiries into CNC machine tool error compensation technologies. [ABSTRACT FROM AUTHOR]

In this study, an advanced cooling method for microelectronic modules in a magnetic field is examined through a numerical and artificial intelligence approach. The research utilized a serpentine microchannel with a rectangular cross section and a laminar and steady flow, assessing various flow rates ranging from 0.01 to 0.1 mL s−1. A hybrid nanofluid made of soybean oil with MgO and Al2O3 nanoparticles served as the coolant, tested at different mixing ratios and volume fractions of 1–4%. The impact of several parameters, including forced convection, magnetic field intensity (0–1 Tesla), module heating capacity (3–7 W), and aspect ratio of the microchannel section (AR) (0.5, 1, and 2), was explored using computational fluid dynamics. The research findings revealed that a 1 Tesla magnetic field notably increased module temperatures, with the maximum cooling efficiency observed with the application of the hybrid nanofluid, optimized convection, an AR of 0.5, and the inclusion of a magnetic insulator. This optimal setup resulted in a significant temperature reduction ranging from 5.98 to 112.88 K compared to the use of pure soybean oil without forced convection and a magnetic field strength of 1 Tesla. Furthermore, the study integrated an artificial neural network (ANN) model, which demonstrated superior accuracy and reliability in predicting the system's performance compared to genetic algorithm models. The effectiveness of ANN as a predictive tool suggests a reduction in dependency on elaborate physical experiments for evaluating the cooling performance of microelectronic modules under magnetic influence. This research proposes a potent and systematic approach for managing the thermal challenges faced by microelectronic devices in magnetic fields and deems further exploration into hybrid ANN models with meta-heuristic algorithms beneficial for enhancing solar collector modeling. [ABSTRACT FROM AUTHOR]

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]

A novel figure of merit based on the concept of signature curve for cold formed steel (CFS) cross sections is used to improve the structural member's overall behavior regardless of length and boundary conditions. The objective is defined as the area under the signature curve, plus a penalty function that ensures improved performance over standard sections at specified lengths. Charged system search (CSS), a meta-heuristic optimization algorithm, is used to search the design space. End-use and other geometrical constraints suggested by previous studies are considered to arrive at practical cross sections. This includes limiting the fold angles to minimize sharp corners in the optimized cross sections, which might result in residual stresses that diminish axial capacity. Such nonlinear constraints are also taken into account using penalty functions to facilitate integration with the heuristic optimization process. The proposed strategy is examined through a couple of illustrative examples and is shown to yield higher axial capacity at all points when combined with the proper penalization. The optimized cross sections are also analyzed in simple-simple and clamped-clamped boundary conditions showing improved axial capacity compared to the standard lipped-channel sections with the same coil width. [ABSTRACT FROM AUTHOR]

In this paper, a novel Gyro Fireworks Algorithm (GFA) is proposed by simulating the behaviors of gyro fireworks during the display process, which adopts a framework of multi-stage and multiple search strategies. At the beginning of the iteration, the gyro fireworks are full of gunpowder; they move via Lévy flight and spiral rotation, and the sprayed sparks are widely distributed and more balanced, which is an effective global exploration method. In the later iteration stages, due to the consumption of gunpowder, the gyro fireworks gradually undergo aggregation and contraction of spiral rotation, which is conducive to the search group to exploit the local area near the global optimal position. The GFA divides the iterative process into four phases, and each phase adopts a different search strategy, in order to enhance the diversity of the search of the population and to balance the exploration capability of the gyro fireworks search group in the global space and the exploitation of the local space. In order to verify the performance of the GFA, it is compared with the latest algorithms, such as the dandelion optimizer, Harris Hawks Optimization (HHO) algorithm, gray wolf optimizer, slime mold algorithm, whale optimization algorithm, artificial rabbits optimization, in 33 test functions. The experimental results show that the GFA obtains the optimal solution for all algorithms on 76% of the functions, while the second-placed HHO algorithm obtains the optimal solution for all algorithms on only 21% of the functions. Meanwhile, the GFA has an average ranking of 1.8 on the CEC2014 benchmark set and 1.4 on the CEC2019 benchmark set. It verifies that the GFA proposed in this paper has better convergence performance and better robustness than the competing algorithms. Moreover, experiments on challenging engineering optimization problems confirm the superior performance of the GFA over alternative algorithms. [ABSTRACT FROM AUTHOR]

The implementation of flexible plate structures in industrial settings presents a multitude of benefits, including enhanced efficiency and greater flexibility in comparison with rigid structures. Notwithstanding these advantages, malleable plate structures are more prone to vibrations on account of their inadequate rigidity to endure significant levels of vibrational events. To address this issue, it is imperative to develop an accurate model and an efficient control system. This study aims to develop a model and active vibration control-based advanced firefly algorithm (AFA) for a flexible horizontal plate structure. Initially, input and output vibration data are collected based on the experimental rig designed with a flexible horizontal plate structure. The obtained data are later used to model the system using the system identification method utilizing a metaheuristic algorithm with linear auto-regressive with an exogenous model structure. This study introduces a novel AFA optimization, known as the variant of the firefly algorithm (FA), for the model identification of the flexible horizontal plate structure. Results indicated that the model of the flexible horizontal plate structure developed using AFA is better compared to FA. The smallest mean square error training is achieved from the best model by AFA at 1.1090 × 10−5 compared to FA. Subsequently, the developed model is implemented in a control scheme for vibration attenuation of the flexible horizontal plate structure. The proposed proportional–integral–derivative (PID) controller tuned by AFA works well to stop unwanted vibrations. It works better than the PID-FA controller and other control algorithms, reducing vibrations by up to 98.9%. [ABSTRACT FROM AUTHOR]

Forecasting of long-term annual electricity demand is studied utilizing historical data for electrical energy consumption and socio-economic indicators—gross domestic product, population, import and export values for the case of Turkey between 1975 and 2020. A quadratic model for electrical energy consumption was applied to define the relation between the historical and predicted data. This model used metaheuristic algorithms; genetic algorithms (GA), differential evolution (DE), particle swarm optimization (PSO), artificial intelligence (AI) approaches; neural networks (NN), and adaptive network fuzzy inference systems (ANFIS), and machine learning (ML) applications; all models undergo testing, but the top four models—stepwise linear regression (SLR), NN, Gaussian process regression (GPR) with exponential, and GPR with squared exponential—are selected for additional research to determine the best forecasting model based on their forecasting performance. Comparing the finalized models SLR produced the best forecasting model with a mean absolute percentage error (MAPE) value of 2.36%, followed by GA with 2.97%. Turkey's yearly electrical energy consumption is projected under three possible scenarios through 2030. Finding the most appropriate forecasting model among the models studied for long-term electrical energy forecasting is ultimately the primary goal of this research. Simulations are done on the MATLAB™ platform. [ABSTRACT FROM AUTHOR]

The traditional energy market is being transformed with modern communication technology, with an interactive generation-load communication topology for energy management in a domestic utility connected over a micro smart grid (MSG). In the traditional energy market, the consumer's role is limited to power consumption. Modern microgrids allow the user to interact with the grid and have increased the energy efficiency of domestic utilities. It is very much necessary to study the role of distributed generation in demand-side management for a proper energy management system in MSG. This paper highlights the interactive areas for the user with the grid to minimize the losses and improve the grid efficiency, thereby minimizing the cost for the user. An agent-based system for the identification of priority and non-priority loads is discussed in this paper, along with an Adaptive Whale Optimization Algorithm (AWOA) for load switching based on priority. Simulation is conducted in MATLAB to validate the proposed technique. The effectiveness of the proposed framework is assessed by comparing it to the Whale optimization Algorithm (WOA) and cases without scheduling. By implementing AWOA, the following improvements were observed: the total electricity cost decreased by an average of 1.33% for 6 residential buildings. In the similar manner, the lad deviation of 1.36% is achieved by implementing AWOA compared to AWO Algorithm. [ABSTRACT FROM AUTHOR]

Electricity network across the globe has witnessed an increase in penetration of distributed energy resources (DER) to the grid. A maiden attempt to integrate 50% loaded combined cycle gas turbine (CCGT) plant and doubly fed induction generator (DFIG) plant with DER is carried out for investigations to study frequency excursions in the hybrid system along with temperature excursions in the CCGT plant. A novel attempt is done to introduce modified beluga whale optimizer for tuning the controller gains and system parameters of the hybrid isolated system. This article emphasizes on the system behaviour of an isolated power system consisting of a CCGT plant and a DFIG plant integrated with distributed energy resources comprising of diesel engine generator, wind turbine generator and battery energy storage system. Among the classical secondary controllers, comparison of system responses is carried out to select the best among proportional, proportional–integral, and proportional–integral–derivative controllers. A modified beluga whale optimization algorithm is utilized to tune the controllers. Various investigations conducted with different turbo generators confirm the control strategy to the frequency of excursions. The hybrid CCGT—DFIG—DER system is also evaluated under random load demand, change in generation rate constraints and DFIG power output. [ABSTRACT FROM AUTHOR]

The electrical power system (EPS) has been heavily stressed due to high load demand. It operates close to the total capacity limits, resulting in voltage instability that can lead to voltage collapse. In this regard, incorporating flexible alternating current transmission system (FACTS) devices and renewable energy sources (RESs) to obtain the optimum values of the generator voltage, reactive compensation, and transformer tab in optimal reactive power dispatch (ORPD) is essential in increasing the reliability and safety of the system. ORPD involves discrete and continuous variables, which are nonlinear, noncontinuous, non-convex, and complex problems. The objective functions of ORPD are reduction in active power loss (Ploss), voltage deviation, and voltage profile enhancement. This paper presents a recent advancement of the ORPD problem, mathematical formulation of the objectives function, and a summary of various metaheuristic optimization methods (single and hybrid) used to solve the ORPD problems. The hybrid method combines two or more methods to improve the demerits of one method to obtain a quality solution to a problem. This review covered incorporating FACTS devices and RESs used in solving the ORPD problem to reduce the active Ploss and improve the voltage profile in the EPS. The benefits of FACTS devices and RESs are also discussed. Also, various metaheuristic algorithms (single, modified, and hybrid) employed to solve the ORPD problem were discussed. The future direction for researchers in this field was provided to give insight into the applicability and performance. Overall, this research explores different techniques used in solving ORPD problems from the optimization point of view to incorporating RESs and FACTS devices to obtain quality solutions. Some existing methods do not guarantee an optimum solution, but incorporating RESs and FACTS devices will help attain the best solution to the problem for better power system operation to improve system reliability and voltage profile. Based on the review journal, it can be concluded that hybrid techniques offer efficient quality solutions to the ORPD problem. [ABSTRACT FROM AUTHOR]

The optimal reactive power dispatch (ORPD) problem is a critical factor in maintaining the safe and efficient operation of electric networks. Due to its mixed-variable nonlinear nature, addressing this problem requires an appropriate optimization algorithm. In this study, a novel approach based on the combination of artificial rabbits optimization (ARO) and gradient-based optimization (GBO) algorithms was proposed to solve the ORPD problem in electric networks. The performance of the proposed AROGBO algorithm were verified on 7 numerical optimization test functions. To evaluate the effectiveness of this hybrid AROGBO technique, standard IEEE-30, IEEE-57 bus, and IEEE-118 bus test systems were employed, with two objective functions tested for each system: minimum total power loss and minimum total voltage deviations. The AROGBO, ARO, and GBO algorithms were utilized in each case to determine the optimal values of the generator voltage, transformer tap changer positions, and reactive power compensation values. A comprehensive comparison was made between the results obtained from the hybrid AROGBO algorithm, standard ARO and GBO algorithms, and other metaheuristic optimization techniques. The simulation attainments verify the accuracy and stability of the proposed AROGBO methodology in solving the ORPD problem. [ABSTRACT FROM AUTHOR]

A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm (AM-SSA), called AMSSAElman-AdaBoost, is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground. The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration. In AM-SSA, firstly, the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow, enhancing the global search ability. Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered, expanding the local search ability. Finally, the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal. In addition, it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA. To demonstrate the effectiveness and reliability of AM-SSA, 23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions (CEC2005), are employed as the numerical examples and investigated in comparison with some wellknown optimization algorithms. The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces. By utilizing the AdaBoost algorithm, multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output. Additionally, the on-site monitoring data acquired from a deep excavation project in Ningbo, China, were selected as the training and testing sample. Meanwhile, the predictive outcomes are compared with those of other different optimization and machine learning techniques. In the end, the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model, illustrating its power and superiority in terms of computational efficiency, accuracy, stability, and robustness. More critically, by observing data in real time on daily basis, the structural safety associated with metro tunnels could be supervised, which enables decision-makers to take concrete control and protection measures. [ABSTRACT FROM AUTHOR]

Agricultural diesel engines can be operated continuously under complex and variable conditions for extended periods. The higher requirements are posed on the Diesel Particulate Filter (DPF) regeneration and reliability. This study aims to enhance the safe and efficient performance of DPF regeneration, emissions, and fuel economy of agricultural diesel engines. An intelligent multi-objective optimization was also proposed to enhance multiple cyclic training. Taking the agricultural engines as the research object, the sample data of the bench test was obtained after experimental design. A prediction model was then constructed in the conditions of DPF regeneration using a Back Propagation neural network (BPNN). According to the Seagull Optimization Algorithm (SOA), the Adaptive Memory Seagull Optimization (AMSO) was proposed to optimize the structure parameters of the BPNN model, in order to meet the requirements of precision. The targets of optimization included T4, T5, O2 concentration, Brake Specific Fuel Consumption (BSFC), NOx, and smoke opacity. In the specific conditions of agricultural diesel engines, multi-objective optimization of injection and intake control parameters was realized under regeneration mode using the Non-dominated Sorting Genetic Algorithm (NSGA)-III. The AMSO-BP prediction model was used to evaluate the fitness of the Pareto optimal solutions, in order to validate the NSGA-III optimal dataset. The optimized MAP values were written into the Engine Control Unit (ECU) after bench tests. Steady-state and World Harmonized Transient Cycle (WHTC) experiments were carried out to verify the multi-objective optimization of the model. The results indicate that the AMSO significantly outperformed the SOA in the optimization of the BPNN prediction model. The AMSO-BPNN prediction model more accurately utilized experimental data to predict the T4, T5, O2 concentration, BSFC, NOx, and smoke opacity, with the R² values of 0.97, 0.99, 0.95, 0.99, 0.98, and 0.95, respectively, on the validation set. Furthermore, the Mean Absolute Percentage Errors (MAPE) on the validation set were reduced by 0.1%, 0.33%, 0.8%, 0.04%, 0.08%, and 4.2%, respectively, compared with the SOA-BP model. The steady-state tests before and after optimization showed that T4 and T5 increased by an average of 3.14%, where the concentration of exhaust oxygen increased by 2.07% and 10.79%, respectively. Simultaneously, the average reductions in the NOx, smoke opacity, and BSFC were 8.68%, 12.07%, and 1.03%, respectively. Efficient and safe DPF regeneration was achieved to significantly reduce the emissions of diesel engines. There was particularly notable optimization under the operation of agricultural diesel engines, which were often working at low speeds and high loads, fully meeting the usage requirements of these engines. The WHTC transient test also showed that the effectiveness of the optimization was achieved in the T4, T5, and O2 concentrations, which increased by 31%, 2.6%, and 0.5%, respectively. Additionally, NOx and soot emissions decreased by 10.4% and 0.8%, and BSFC was reduced by 3.5%. These findings demonstrate that better performance was obtained under the complex and variable conditions of agricultural diesel engines. In conclusion, the safe and efficient DPF regeneration can be expected to reduce the emissions and operational costs for agricultural diesel engines. The specific requirements of agricultural diesel engines can also be fully met to enhance the system reliability in the engineering application. This research can offer practical guidance to optimize the control parameters under the regeneration mode of diesel engines, particularly for more sustainable and efficient agricultural machinery. [ABSTRACT FROM AUTHOR]

The precise evaluation of predicting software development effort is a serious process in software engineering. The underestimates require more time, which seems to be the negotiation of complete efficient design and complete software testing. Thus, this paper devises a novel model for inspecting the effort taken for the design of software. The Mayfly Lion Optimization (MFLion) algorithm is devised, which ensembles the Mayfly Algorithm (MA) with the Lion Optimization Algorithm (LOA) for precise estimation. The deep maxout network (DMN) is adapted wherein the different weights are produced out of which the most suitable is infused considering the MFLion during model training. The optimal features are selected using recursive feature elimination (RFE) wherein the best features are selected and the remaining irrelevant features are eliminated from the list. The proposed MFLion obtained better performance with the smallest mean magnitude of relative error (MMRE) of 5.909 and the smallest root mean square error (RMSE) of 75.505, respectively. Each technique is produced using a separate database generated using the Promise software engineering repository. The outcomes produced from the assessment of the accuracies of models suggested that the MFLion‐DMN is a substitute to forecast software effort, which is extensively devised in engineering platforms. [ABSTRACT FROM AUTHOR]

This paper presents a recent bio-inspired optimization algorithm for the critical topic of load frequency control (LFC) in an isolated multi-source power generating system. A specific model that emerges from the scientific literature and consists of reheated thermal, hydro and gas-turbine power sources is examined. The main goal is to tune the controller of each generating source i.e. find their optimal gains through bio-inspired optimization algorithms. Namely, the algorithms applied are the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and the newly proposed Harris Hawks Optimization (HHO). It was observed that all optimization algorithms succeeded in obtaining controllers' gain values which ensure stable system operation. Also, the simulation results indicated the superiority of the proposed HHO algorithm over the other algorithms for all the considered scenarios. In this article, for the first time to the authors' knowledge extend an attempt has been made to optimize an isolated multi-source system using the bio-inspired HHO algorithm. The present study considered the LFC problem with a variety of different scenarios, taking into account both sub-cases of nonlinearities (e.g., generation rate constraint, boiler dynamics, etc.) and their combinations, as well as combinations of different controllers and load disturbances, which are not found in the literature. Last but not least, the robustness of the selected controller was further evaluated. The obtained results clearly demonstrated that the controller's gains established in normal conditions do not require retuning when critical system parameters undergo a significant variation. [ABSTRACT FROM AUTHOR]

For improving the energy efficacy and control performance, integration of swarm optimization with controller design could successfully reach this objective. In this study, a comparative analysis has been conducted between two decentralized control structures based on optimized Proportional-Integral-Derivative (PID) and PID-Proportional (PID-P) controllers for optimal controlling of heating system in multi-zone building. Based on the energy balance equation, the mathematical dynamics model of the heating system is established in the building. In order to enhance and optimize the performances of both controllers, their design parameters are tuned based on Whale Optimization Algorithm (WOA). Two objectives have been considered in the optimization process of heating system. The first objective is to minimize the error in temperature, between the desired and real temperatures, based on IAE (Integral of Absolute Error) index, while the second objective is the minimization of the heat energy consumption. The normalization method has been used to adjust between the two differently-scaled objectives. Simulation results based on MATLAB reveal that the PID-P controller achieved better performance in terms of providing comfort indoor temperature with energy savings as compared to the PID controller. [ABSTRACT FROM AUTHOR]

Heart disease is one of the foremost reasons for death globally. Machine learning (ML) can be used to predict heart diseases early, which can help improve patient outcomes. This research proposes a novel machine learning method for predicting heart disease using a combination of Grey Wolf Optimization (GWO) and stacked ensemble techniques. GWO is a metaheuristic algorithm that can be used to optimize the parameters of machine-learning models. The stacked ensemble technique is a combination of multiple machine learning models to improve the overall accuracy of the prediction. The model proposed was evaluated using a dataset of heart patients. The results showed that the model achieved a 93% accuracy, which was significantly higher compared to traditional machine learning methods. The proposed method also had a higher precision of 91%, sensitivity of 95.3%, F1 score of 92.9%, and Matthew coefficient of 0.83, less in Log_Loss 2.87 than the traditional methods. The results of this research suggest that the proposed model is a promising new approach for predicting heart diseases. This method is more accurate and reliable than traditional methods and has the potential to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Advanced brain cancer is the deadliest type with just a few months survival rate. Existing technologies hinder the objective of forecasting cancer. This work aims to fulfil the pressing requirement for timely and precise identification of advanced-stage brain tumours, which are notorious for their markedly reduced life expectancy. It presents an innovative hybrid approach for predicting brain tumours and improves diagnostic capabilities. The Multiple Kernel K-Means Cluster Algorithm (MKKCA) is used to segment brain MRI images effectively, differentiating healthy and tumorous tissues. After segmentation, a hybrid approach with 3D-Convolutional Neural Network (CNN) and U-Net has been utilized for classification. The objective is to effectively and accurately distinguish normal and pathological brain images. To enhance the efficiency, we include the Improved Whale Optimization Algorithm (IWOA), which guarantees accurate and dependable performance via location updates. The methodology demonstrates outstanding precision with 98.5% accuracy rate, 98.56% specificity, 91% sensitivity, 87.45% precision and a recall rate of 96% with the F-Measure at 96.02%. These findings, obtained using MATLAB, demonstrate a substantial performance improvement compared to current approaches. This development not only represents a significant addition to diagnostic imaging but also a crucial role in the prediction and treatment of brain cancers. [ABSTRACT FROM AUTHOR]