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Twitter and Facebook are widely recognized as crucial tools for situational information during disasters. Given that the classification of disaster related tweets is computationally challenging due to the high dimension of textual data caused by the redundant and irrelevant features. Hence for optimal feature selection (FS) and classification of disaster tweets, this work utilizes binary salp swarm algorithm (BSSA) and proposed two enhancements over it (PBcSSA). The commensalism phase from symbiotic organisms search (SOS) is integrated with BSSA to enhance its feature space searchability and then its parallel implementation is done using Apache Spark framework to reduce the execution time. The experiments were performed in a cross-disaster setting on nine groups of datasets including biological, earthquake, flood, hurricane, industrial, societal, transportation, wildfire, and environmental. The proposed PBcSSA combined with the Naive Bayes (NB) classifier in wrapper mode and its performance is compared with standard BSSA, binary sine cosine algorithm (BSCA), binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO), and binary whale optimization algorithm (BWOA). The experimental results reveal that the proposed PBcSSA outperforms other algorithms in disaster tweet classification and achieved highest average F1-score with lowest feature set in a reduced execution time. [ABSTRACT FROM AUTHOR]

Healthcare is crucial to patient care because it provides vital services for maintaining and restoring health. As healthcare technology evolves, cutting-edge tools facilitate faster diagnosis and more effective patient treatment. In the present age of pandemics, the Internet of Things (IoT) offers a potential solution to the problem of patient safety monitoring by creating a massive quantity of data about the patient through the linked devices around them and then analyzing it to estimate the patient’s current status. Utilizing the IoT-based meta-heuristic algorithm allows patients to be remotely monitored, resulting in timely diagnosis and improved care. Meta-heuristic algorithms are successful, resilient, and effective in solving real-world enhancement, clustering, predicting, and grouping. Healthcare organizations need an efficient method for dealing with big data since the prevalence of such data makes it challenging to analyze for diagnosis. The current techniques used in medical diagnostics have limitations due to imbalanced data and the overfitting issue. This study introduces the particle swarm optimization and convolutional neural network to be used as a meta-heuristic optimization method for extensive data analysis in the IoT to monitor patients’ health conditions. Particle Swarm Optimization is used to optimize the data used in the study. Information for a diabetes diagnosis model that includes cardiac risk forecasting is collected. Particle Swarm Optimization and Convolutional Neural Networks (PSO-CNN) results effectively make illness predictions. Support Vector Machine has been used to predict the possibility of a heart attack based on the classification of the collected data into projected abnormal and normal ranges for diabetes. The results of the simulations reveal that the PSO-CNN model used to predict diabetic disease increased in accuracy by 92.6%, precision by 92.5%, recall by 93.2%, F1-score by 94.2%, and quantization error by 4.1%. The suggested approach could be applied to identify cancer cells. [ABSTRACT FROM AUTHOR]

Efforts in cardiovascular disorder detection demand immediate attention as they hold the potential to revolutionize patient outcomes through early detection systems. The exploration of diseases and treatments, coupled with the potential of artifical intelligence to reshape healthcare, highlights a promising avenue for innovation. AI-driven early detection systems offer substantial benefits by improving quality of life and extending longevity through timely interventions for chronic diseases. The evolving landscape of healthcare algorithms presents vast possibilities, particularly in the application of metaheuristics to address complex challenges. An exemplary approach involves employing metaheuristic solutions such as PSO, FA, GA, WOA, and SCA to optimize an RNN for anomaly detection using ECG systems. Despite commendable outcomes in the best and median case scenarios, the study acknowledges limitations, focusing on a narrow comparison of optimization algorithms and exploring RNN capabilities for a specific problem. Computational constraints led to the use of smaller populations and limited rounds, emphasizing the need for future research to transcend these boundaries. Significantly, the introduction of attention layers emerges as a transformative element, enhancing neural network performance. The introduced optimizer proves robust across test scenarios, effectively navigating local minimum traps. Attention layers contribute to a substantial performance boost, reducing the error rate from 0.006837 to an impressive 0.002486, underscoring their role in focusing on pertinent information. This abstract advocates for further research to expand beyond these limitations, exploring novel algorithms and addressing broader medical challenges in the pursuit of refined and advanced solutions. [ABSTRACT FROM AUTHOR]

Non-speech emotion recognition involves identifying emotions conveyed through non-verbal vocalizations such as laughter, crying, and other sound signals, which play a crucial role in emotional expression and transmission. This paper employs a nine-category discrete emotion model encompassing happy, sad, angry, peaceful, fearful, loving, hateful, brave, and neutral. A proprietary non-speech dataset comprising 2337 instances was utilized, with 384-dimensional feature vectors extracted. The traditional Backpropagation Neural Network (BPNN) algorithm achieved a recognition rate of 87.7% on the non-speech dataset. In contrast, the proposed Whale Optimization Algorithm - Backpropagation Neural Network (WOA-BPNN) algorithm, applied to a self-made non-speech dataset, demonstrated a remarkable accuracy of 98.6%. Notably, even without facial emotional cues, non-speech sounds effectively convey dynamic information, and the proposed algorithm excels in their recognition. The study underscores the importance of non-speech emotional signals in communication, especially with the continuous advancement of artificial intelligence technology. The abstract thus encapsulates the paper's focus on leveraging AI algorithms for high-precision non-speech emotion recognition. [ABSTRACT FROM AUTHOR]

In the process of digital transformation and development in various industries, there are more and more large-scale optimization problems. Currently, swarm intelligence optimization algorithms are the best method to solve such problems. However, previous experimental research has found that there is still room for improvement in the performance of using existing swarm intelligence optimization algorithms to solve such problems. To obtain the high-precision optimal value of whale optimization algorithm (WOA) for solving large-scale optimization problems, the optimization problem knowledge model is studied to guide the iterative process of WOA algorithm, and a novel whale optimization algorithm based on knowledge model guidance (KMGWOA) is proposed. First, a population update strategy based on multiple elite individuals is proposed to reduce the impact of the local optimal values, and the knowledge model to guide population update is constructed by combining the proposed population update strategy with the population update strategy based on global optimal individual. Second, a collaborative reverse learning knowledge model with multiple elite and poor individuals in the solution space is proposed to prevent long-term non-ideal region search. The above two knowledge models guide the iterative process of WOA algorithm in solving large-scale optimization problems. The performance of the KMGWOA algorithm guided by the proposed knowledge models is tested through the well-known classical test functions. The results demonstrate that the proposed KMGWOA algorithm not only has good search ability for the theoretical optimal value, but also achieves higher accuracy in obtaining the optimal value when it is difficult to obtain the theoretical optimal value. Moreover, KMGWOA algorithm has fast convergence speed and high effective iteration percentage. [ABSTRACT FROM AUTHOR]

The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively. [ABSTRACT FROM AUTHOR]

Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a novel meta-heuristic approach employing the slender Loris optimization algorithm (SLOA), miming slender Loris behavior. The behavior includes foraging, hunting, migration and communication with each other. The ultimate goal of the devised algorithm is to replicate the food-foraging behaviour of Slender Loris (SL) and the quick movement of SL when threatened (i.e.) their escape from predators and also mathematically modelled the special communication techniques of SL using their urine scent smell. SLOA modelled SL's slow food foraging behaviour as the exploitation phase, and moving between the tree and escaping from a predator is modelled as the exploration phase. The Eyesight of slender Loris plays a vital role in food foraging during nighttime in dim light. The operator's Eyesight is modelled based on the angle of inclination of SL. The urine scent intensity is used here to be instrumental in preventing already exploited territory activities, which improves algorithm performance. The suggested algorithm is assessed and tested against nineteen benchmark test operations and evaluated for effectiveness with standard widely recognized meta-heuristics algorithms. The result shows SLOA performing better and achieving near-optimal solutions and dominance in exploration–exploitation balance in most cases than the existing state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Competitiveness in the global market is getting more intense. Due to resource and budget constraints, firms need to achieve their expected goals and satisfy all investment constraints under uncertainty. Selecting the set of projects among other candidates to get the most efficient portfolio requires a lot of attention from the Decision Makers (DMs) as this consideration no longer relies purely on the financial term. This problem becomes a multi-objective problem under uncertainty where the financial return and risk from uncertainty are required into the trading off consideration. Due to the financial uncertainty, the chance-constrained programming has been employed in this study for defuzzifying and solving uncertain optimization problems at a specified confidence level that is defined by the DMs. Then, various kinds of investment or financial risk measures, Lower-Semi Variance Index (LSVI), the absolute deviation with the expected FNPV, and the absolute mean-Conditional Value at Risk (CVaR) gap are provided in the selection of such risk measures to show their differences in characteristics and performances in the obtained results. Since, such problems can consist of many project candidates and complex constraints, which may grow beyond the application of the exact optimization approach, a meta-heuristic method, Genetic Algorithm (GA), is introduced to optimize this problem through designing and constructing a decision support tool for the investment portfolio selection and optimization. The applicability of the proposed comparative approach and the constructed tool are illustrated through examples. [ABSTRACT FROM AUTHOR]

In this paper, an improved constant current step based on the grey wolf optimization (CCS-GWO) algorithm for photovoltaic systems is investigated. The development of grey wolf optimization has been widely spread over photovoltaic applications. This method is one of the metaheuristic swarm optimization algorithm groups inspired by an optimum means of chasing prey by grey wolves. The proposed technique applies constant current steps to the pack of wolves (alpha, beta, and omega) by monitoring the average of the internal current step and external current step in order to target the leader alpha wolf position. Moreover, the proposed technique solves the convergence process issues, low convergence speed, and premature local optima problems of the traditional GWO algorithm. This CCS-GWO algorithm accurately tracks the maximum power point from the photovoltaic systems for load charging in different partial shading conditions (PSCs). A number of standard benchmark functions are presented with low average cost functions and their corresponding standard deviation values. The simulation results revealed that the proposed CCS-GWO approach outperforms the existing GWO and GA algorithms in terms of efficiency (98.55%) and tracking time (0.3 s). [ABSTRACT FROM AUTHOR]

Generally, Wireless Body Area Networks (WBANs) are regarded as the collection of small sensor devices that are effectively implanted or embedded into the human body. Moreover, the nodes included in the WBAN have large resource constraints. Hence, reliable and energy-efficient data transmission plays a significant role in the implementation and in constructing of most of the merging applications. Regarded to complicated channel environment, limited power supply, as well as varying link connectivity has made the construction of WBANs routing protocol become difficult. In order to provide the routing protocol in a high energy-efficient manner, a new approach is suggested using hybrid meta-heuristic development. Initially, all the sensor nodes in WBAN are considered for experimentation. In general, the WBAN is comprised of mobile nodes as well as fixed sensor nodes. Since the existing models are ineffective to achieve high energy efficiency, the new routing protocol is developed by proposing the Hybrid Tunicate-Whale Swarm Optimization (HT-WSO) algorithm. Subsequently, the proposed work considers the multiple constraints for deriving the objective function. The network efficiency is analyzed using the objective function that is formulated by distance, hop count, energy, path loss, and load and packet loss ratio. To attain the optimum value, the HT-WSO derived from Tunicate Swarm Algorithm (TSA) and Whale Optimization Algorithm (WOA) is employed. In the end, the ability of the working model is estimated by diverse parameters and compared with existing traditional approaches. The simulation outcome of the designed method achieves 13.3%, 23.5%, 25.7%, and 27.7% improved performance than DHOA, Jaya, TSA, and WOA. Thus, the results illustrate that the recommended protocol attains better energy efficiency over WBANs. [ABSTRACT FROM AUTHOR]

Sentiment analysis is the most basic and imperative work in mining the preference of user interest. In this work, a deep model with optimization, named "Chimp Whale Optimization Algorithm-based Random Multimodal Deep Learning" is devised for sentiment rating prediction. The process of tokenization, which divides the entire document into small units using Bidirectional Encoder Representations from Transformers (BERT) for better processing, is where the input review data is initially given. Aspects from review data and aspect term extraction are completed for mining. Additionally, Random Multimodal Deep Learning is used to forecast the sentiment rating. The ChWOA is used in this case to combine the Chimp Optimization Algorithm (ChOA) and the Whale Optimization Algorithm (WOA). With a precision of 93.1%, recall of 94.4%, and F-measure of 93.8%, the ChWOA-based RMDL demonstrated better efficiency. [ABSTRACT FROM AUTHOR]

BACKGROUND: This study explores the dynamics of a mathematical model, utilizing ordinary differential equations (ODE), to depict the interplay between cancer cells and effector cells under chemotherapy. The stability of the equilibrium points in the model is analysed using the Jacobian matrix and eigenvalues. Additionally, bifurcation analysis is conducted to determine the optimal values for the control parameters. OBJECTIVE: To evaluate the performance of the model and control strategies, benchmarking simulations are performed using the PlatEMO platform. METHODS: The Pure Multi-objective Optimal Control Problem (PMOCP) and the Hybrid Multi-objective Optimal Control Problem (HMOCP) are two different forms of optimal control problems that are solved using revolutionary metaheuristic optimisation algorithms. The utilization of the Hypervolume (HV) performance indicator allows for the comparison of various metaheuristic optimization algorithms in their efficacy for solving the PMOCP and HMOCP. RESULTS: Results indicate that the MOPSO algorithm excels in solving the HMOCP, with M-MOPSO outperforming for PMOCP in HV analysis. CONCLUSION: Despite not directly addressing immediate clinical concerns, these findings indicates that the stability shifts at critical thresholds may impact treatment efficacy. [ABSTRACT FROM AUTHOR]

Massive MIMO (M-MIMO) devices are the key tool to meet the performance stards established for 5G-wireless communication. However, more Radio Frequency (RF) chains needed in base station (BS) with a huge count of transmitting antennas, involve expensive hardware and computing complexities. In order to decrease the RF chains needed in BS, this work intended to use the optimal transmit antenna selection (TAS) strategy. This strategy is gaining a lot of interest since the optimization algorithm aids in the ability to enhance the system performance considerably the efficiency and secrecy rate. This work proposes a novel Coati Adopted Pelican Optimization (CA-PO) for choosing the optimal TA by considering efficiency as well as secrecy rate. In addition, the CA-PO algorithm makes the decision on which antenna to be elected. At last, the supremacy of CA-PO-based TAS is proven from the analysis regarding secrecy rate and EE analysis. Accordingly, the proposed CA-PO method for MF for set up 2 has attained a higher EE of 0.976; whereas, the DMOA, COA, MRFO, POA and BEA techniques have got a relatively lower EE of 0.968. [ABSTRACT FROM AUTHOR]

BACKGROUND: The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). OBJECTIVE: A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented. METHODS: The lung tissues are segmented using Otsu's thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier's accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features. RESULTS: Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier's results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall's Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered. CONCLUSION: The MLP classifier's accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%. [ABSTRACT FROM AUTHOR]

Metaheuristics are commonly employed as a means of solving many distinct kinds of optimization problems. Several natural-process-inspired metaheuristic optimizers have been introduced in the recent years. The convergence, computational burden and statistical relevance of metaheuristics should be studied and compared for their potential use in future algorithm design and implementation. In this paper, eight different variants of dragonfly algorithm, i.e. classical dragonfly algorithm (DA), hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder-Mead algorithm and dragonfly algorithm (INMDA), and hybridization of dragonfly algorithm and artificial bee colony (HDA) are applied to solve four industrial chemical process optimization problems. A fuzzy multi-criteria decision making tool in the form of fuzzy-measurement alternatives and ranking according to compromise solution (MARCOS) is adopted to ascertain the relative rankings of the DA variants with respect to computational time, Friedman's rank based on optimal solutions and convergence rate. Based on the comprehensive testing of the algorithms, it is revealed that DADE, QGDA and classical DA are the top three DA variants in solving the industrial chemical process optimization problems under consideration. [ABSTRACT FROM AUTHOR]

Establishing a closed-loop system that could facilitate the reusing, renovation, and recycling of the various garbage products generated by this business could prove significant value to the particular business chains involved. A system of shipping that is mindful of the surroundings and takes accountability regarding all the relevant money, sustainable, and societal concerns. The sustainability Closed-Loop Supply-Chain Networks (CLSCN) architecture and the marketplace are brought together in the present article, which serves as the study's primary part in the body of knowledge. As a result, an optimization with multiple objectives paradigm has been offered to arrive at their choices regarding position, allocations, and stock in relation to the challenge under consideration. The goals of the optimized model, derived from the triple bottom line strategy, are aimed at lowering overall expenditure and emissions of CO2 as much as possible while increasing the number of employment possibilities. In this study, we have proposed Hybrid electromagnetism with a genetic algorithm (HEGA) and compare our proposal with the existing methods. The obtained results show that the proposed model integrated with HEGA gives significant improvements with significant outcomes in terms of sensitivity (97%), specificity (95%), transportation cost (30%), and computational time (5.3s). This knowledge serves as a driving force behind the development of CLSN in the sector to establish a viable and affordable approach. [ABSTRACT FROM AUTHOR]

Rolling bearings are a key component of rotating machinery and their health directly affects the safe operation of mechanical equipment. Therefore, fault diagnose for rolling bearings is very important. The fault diagnosis process of rolling bearings includes three stages: signal decomposition, feature extraction, and pattern recognition. Variational mode decomposition (VMD) can suppress end effects, but improper parameter settings will cause information losses or excessive decomposition. In this work, an improved whale optimization algorithm (IWOA) is applied to parameter settings of VMD. Correspondingly, an IWOA-VMD signal decomposition method is proposed. The decomposed signal is combined with a Laplace score method and classifier to remove the redundancy and noise in the feature set and obtain a low-dimensional sensitive feature subset. Then, aiming at the problem of the parameter settings of a least squares support vector machine (LSSVM) affecting the recognition performance and accuracy, a salp swarm algorithm (SSA) is used to globally optimize the penalty parameter and kernel width in the LSSVM to establish an SSA-LSSVM fault recognition model. This model is applied to the fault diagnosis of rolling bearings. In particular, rolling bearing fault samples at Case Western Reserve University are used to verify the method. The results indicate that the proposed method is effective and improves the speed and accuracy of fault diagnosis. [ABSTRACT FROM AUTHOR]

High-performance concrete (HPC) is a specialized type of concrete designed to meet stringent performance and uniformity standards that are difficult to achieve with conventional materials and standard mixing, placing, and curing methods. The testing process to determine the mechanical properties of HPC specimens is complex and time-consuming, and making improvements can be difficult after the test result does not meet the required properties. Anticipating concrete characteristics is a pivotal facet in the realm of High-Performance Concrete (HPC) manufacturing. Machine learning (ML)-driven models emerge as a promising avenue to tackle this formidable task within this context. This research endeavors to employ a synergy of ML hybrid and ensemble frameworks for the prognostication of the mechanical attributes within HPC, encompassing compressive strength (CS), slump (SL), and flexural strength (FS). The formulation of these hybrid and ensemble constructs was executed through the integration of Support Vector Regression (SVR) with three distinct meta-heuristic algorithms: Prairie Dog Optimization (PDO), Pelican Optimization Algorithm (POA), and Mountain Gazelle Optimizer (MGO). Some criteria evaluators were used in the training, validation, and testing phases to assess the robustness of the established models, and the best model was proposed for practical applications through comparative analysis of the results. As a result, the hybrid and ensemble models were the potential methods to predict concrete properties accurately and efficiently, thereby reducing the need for expensive and time-consuming testing procedures. In general, the ensemble model, i.e., SVPPM, had a more suitable performance with high values of R2 equal to 0.989 (MPa), 0.984 (mm), and 0.992 (MPa) and RMSE = 3.82 (MPa), 9.5 (mm), and 0.30 (MPa) for CS, SL, FS compared to other models, respectively. [ABSTRACT FROM AUTHOR]

Heart disease (HD) is a leading cause of mortality worldwide, emphasizing the need for accurate and efficient detection and classification methods. Recently, Blockchain (BC) provides seamless and secure sharing of heart disease data amongst healthcare providers, specialists, and researchers. This allows collaborative efforts, data exchange, and integration of diverse datasets, leading to a more comprehensive analysis and accurate detection of heart diseases. BC provides a decentralized and tamper-proof platform for storing sensitive patient data related to heart disease. This ensures the integrity and security of the data, reducing the risk of unauthorized access or data manipulation. Therefore, this study presents a new blockchain-assisted heart disease detection and classification model with feature selection with optimal fuzzy logic (BHDDC-FSOFL) technique. The presented BHDDC-FSOFL technique uses BC technology to store healthcare data securely. In addition, the disease detection module encompasses the design of biogeography teaching and learning-based optimization (BTLBO) algorithm for feature selection (FS) procedure. Moreover, an adaptive neuro-fuzzy inference system (ANFIS) classifier can be exploited for HD detection and classification. Furthermore, the ebola search optimization (ESO) algorithm is used for the parameter tuning of the ANFIS classifier. The integration of ANFIS classifier enables the modeling of uncertainty and imprecision in HD data, while metaheuristic algorithms aid in optimizing the classification process. Additionally, the utilization of BC technology ensures secure and transparent storage and sharing of healthcare data. To demonstrate the enhanced HD classification results of the BHDDC-FSOFL technique, a detailed experimental analysis was made on the HD dataset. The extensive result analysis pointed out the improved performance of the BHDDC-FSOFL technique compared to recent approaches in terms of different measures. Therefore, the proposed model offers a reliable and privacy-enhancing solution for healthcare providers and patients in a BC-assisted healthcare environment. [ABSTRACT FROM AUTHOR]

A novel approach to enhance software testing through intelligent test case selection is proposed in this work. The proposed method combines feature extraction, clustering, and a hybrid optimization algorithm to improve testing effectiveness while reducing resource overhead. It employs a context encoder to extract relevant features from software code, enhancing the accuracy of subsequent testing. Through the use of Fuzzy C-means (FCM) clustering, the test cases are classified into groups, streamlining the testing process by identifying similar cases. To optimize feature selection, a Hybrid Whale Optimized Crow Search Algorithm (HWOCSA), which intelligently combines the strengths of both Whale Optimization Algorithm (WOA) and Crow Search Algorithm (CSA) is introduced. This hybrid approach mitigates limitations while maximizing the selection of pertinent features for testing. The ultimate contribution of this work lies in the proposal of a multi-SVM classifier, which refines the test case selection process. Each classifier learns specific problem domains, generating predictions that guide the selection of test cases with unprecedented precision. Experimental results demonstrate that the proposed method achieves remarkable improvements in testing outcomes, including enhanced performance metrics, reduced computation time, and minimized training data requirements. By significantly streamlining the testing process and accurately selecting relevant test cases, this work paves the way for higher quality software updates at a reduced cost. [ABSTRACT FROM AUTHOR]

Accurately estimating concrete mechanical parameters using artificial intelligence-based methods can save time and energy. Existing nonlinear relationships between concrete components have entered uncertainty in the estimation of hardness properties of the slump and compressive strength as one of the most important parameters in concrete design. Employing regular approaches to use AI models individually in estimating dependent variables has been adopted in many studies. Therefore, the current study has aimed to develop predictive models in two categories of ensemble and hybrid frameworks to predict the hardness properties of high-performance concrete (HPC). In this regard, models based on Support Vector Regression, Decision Tree, and AdaBoost Machine learning were coupled with a metaheuristic optimization algorithm Chaos game optimizer (CGO). Linking three predictive models as well as tuning their internal settings via optimization algorithm could generate various types of hybrid and ensemble models. By assessing the results of the proposed models for compressive strength, the performance of ADA-CGO hybrid models was calculated higher than the ensemble model of SVR-ADA-DT, with 1.22% and 166% percent difference in terms of R2 and RMSE, respectively. Also, for predicting Slump, other hybrid models appeared with weaker performance than the ensemble model, with an average difference of 40.66% in terms of the MAE index. Generally, using advanced types of individual models, including ensemble and hybrid, indicated boosted performance accompanied by low-cost modeling processes. [ABSTRACT FROM AUTHOR]

The model of a probabilistic neural network (PNN) is commonly utilized for classification and pattern recognition issues in data mining. An approach frequently used to enhance its effectiveness is the adjustment of PNN classifier parameters through the outcomes of metaheuristic optimization strategies. Since PNN employs a limited set of instructions, metaheuristic algorithms provide an efficient way to modify its parameters. In this study, we have employed the Aquila optimizer algorithm (AO), a contemporary algorithm, to modify PNN parameters. We have proposed two methods: Aquila optimizer based probabilistic neural network (AO-PNN), which uses both local and global search capabilities of AO, and hybrid Aquila optimizer and simulated annealing based probabilistic neural network (AOS-PNN), which integrates the global search abilities of AO with the local search mechanism of simulated annealing (SA). Our experimental results indicate that both AO-PNN and AOS-PNN perform better than the PNN model in terms of accuracy across all datasets. This suggests that they have the potential to generate more precise results when utilized to improve PNN parameters. Moreover, our hybridization technique, AOS-PNN, is more effective than AO-PNN, as evidenced by classification experiments accuracy, data distribution, convergence speed, and significance. We have also compared our suggested approaches with three different methodologies, namely Coronavirus herd immunity optimizer based probabilistic neural network (CHIO-PNN), African buffalo algorithm based probabilistic neural network (ABO-PNN), and β -hill climbing. We have found that AO-PNN and AOS-PNN have achieved significantly higher classification accuracy rates of 90.68 and 93.95, respectively. [ABSTRACT FROM AUTHOR]

Meta-heuristic optimization algorithms are versatile and efficient techniques for solving complex optimization problems. When applied to clustering algorithms, these algorithms offer numerous advantages over traditional optimization methods, including global search capabilities, iterative refinement processes, robustness to initial conditions, and flexibility in handling diverse clustering objectives and constraints. Employing meta-heuristic optimization in clustering algorithms leads to improved accuracy, scalability, robustness, and flexibility in finding optimal or near-optimal clustering solutions. These algorithms generate new individuals iteratively using nature-inspired operations to obtain high-quality results. However, they often suffer from slower convergence and lack guarantees of finding the best solution for every problem, posing ongoing challenges in algorithm development. This study focuses on addressing the issue of premature convergence in metaheuristic algorithms by introducing an automatic cuckoo search (AuCS) algorithm. The AuCS algorithm aims to strike a balance between exploration and exploitation by dynamically updating the step size in each generation, thereby avoiding premature convergence. To evaluate the effectiveness of the proposed algorithm, experiments were conducted on 13 standard benchmark functions and 14 CEC 2005 benchmark functions. In overall performance, AuCS has the best optimum value in 72.22% of cases. This demonstrates the efficacy of the proposed algorithm in achieving improved clustering accuracy and minimizing intra-cluster distance. The proposed AuCS algorithm was applied to data clustering and compared with four swarm optimization algorithms. Here, AuCS outperforms these well-known algorithms in 5 out of 7 datasets. The experimental evaluations in both benchmark functions and clustering problems confirm the promising results of the proposed algorithm, suggesting that AuCS could be considered as a potential improvement over the cuckoo search algorithm. [ABSTRACT FROM AUTHOR]

In Visual Communication Design (VCD), noise data is easy to appear, which reduces image quality and affects the effect of VCD. The non local mean image denoising algorithm is a good filtering denoising algorithm, but there are still issues of information interference and missing. To improve the performance of noise recognition and image denoising technology, this study proposes a non local mean image denoising algorithm based on machine learning technology. The whale optimization algorithm, as a machine learning technique, has good performance in seeking optimal solutions. Therefore, it is applied to optimize the filtering parameters of non local mean image denoising algorithms to improve the perforGAmance of non local mean image denoising algorithms. To address the shortcomings of the whale optimization algorithm, BP neural network is introduced for optimization. Finally, the experiment uses the improved particle swarm optimization algorithm to optimize the BPNN and applies it to the recognition and classification of noise data. Combining the above contents, the IBINLM image denoising algorithm is constructed experimentally. It is verified that the IPSO-BPNN model's loss value is 0.12; The recognition accuracy of the model for noise pixels is 98.64%; F1 value reaches 96.32%; The fitting degree reaches 0.983. The PSNR of IBINLM algorithm is 35.86 dB; MSE is 0.29; AUC value reaches 0.903. The results show that the IPSO-BPNN model and IBINLM image denoising algorithm have better performance compared to other models, which can improve the quality of visual communication works, playing an essential role in image transmission and storage in visual communication design. [ABSTRACT FROM AUTHOR]

Metaheuristics are widely used in science and industry because it as a high-level heuristic technique can provide robust or advanced solutions compared to classical search algorithms. Flow Regime Algorithm is a novel physics-based optimization approach recently proposed, and it is one of the candidate algorithms for solving complex optimization problems because of its few parameter configurations, simple coding, and good performance. However, the population that initialized randomly may have poor diversity issues, resulting in insufficient global search, and premature convergence to local optimum. To solve this problem, in this paper, a novel enhanced Flow Regime Algorithm based on opposition learning scheme is proposed. The proposed algorithm introduces the opposition-based learning strategy into the generation of some populations to enhance the global search performance while maintaining a fast convergence rate. In order to verify the performance of the proposed algorithm, 23 benchmark numerical optimization functions were studied experimentally in detail and compared with six well-known algorithms. Experimental results show that the proposed algorithm outperforms all other metaheuristic algorithms in all unimodal functions with higher accuracy, and can obtain competitive results on more multimodal cases. A statistical comparison shows that the proposed algorithm has superiority. Finally, that the proposed algorithm can achieve higher quality alignment compared to most other metaheuristic-based systems and OAEI ontology alignment systems. [ABSTRACT FROM AUTHOR]

In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA's convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling. [ABSTRACT FROM AUTHOR]

Multilocus variable number tandem repeat analysis (MLVA) utilizes short DNA repeat polymorphism in genomes, which is termed variable number tandem repeat (VNTR), to differentiate closely related organisms. One research challenge is to find an optimal set of VNTR to distinguish different members accurately. An intuitive method is to use an exhaustive search method. However, this method is not an efficient way to find optimal solutions from a dataset comprising many attributes (loci) due to the curse of dimensionality. In this study, metaheuristic methods are proposed to find an optimal set of loci combination. Basic genetic algorithm (BGA) and modified genetic algorithm (MGA) were proposed in our previous work for this purpose. However, they require prior knowledge from an experienced user to specify the minimum number of loci for achieving good results. To impose no such expertise requirement for parameter setting, a GA with Duplicates (GAD), which allows the inclusion of duplicated loci in a chromosome (potential solution) during the search process, is developed. The study also investigates the search performance of a hybrid metaheuristic method, namely quantum-inspired differential evolution (QDE). Hunter-Gaston Discriminatory Index (HGDI) is used to indicate the discriminatory power of a loci combination. Two Mycobacterium tuberculosis MLVA datasets obtained from a public portal and a local laboratory respectively, are used. The results obtained by using exhaustive search and metaheuristic methods are first compared, followed by a performance comparison among BGA, MGA, GAD, and QDE by a statistical approach. The best-performing GA method (i.e., GAD) and QDE are selected for a performance comparison with several recent metaheuristic methods using both MLVA datasets by a statistical approach. The statistical results show that both GAD and QDE could achieve higher HGDI than the recent methods using a small but informative set of loci combination. [ABSTRACT FROM AUTHOR]

Bandwidth consecutive multicoloring problem is also known as a b-coloring problem. Let G = (V, E) be a graph where V is the set of vertices and E is the set of edges. Let each vertex v of V has a positive integer weight b (v) and each edge (v, w) of E has a non-negative integer weight b (v, w). A bandwidth consecutive multicoloring of a graph is a problem of assigning b (v) consecutive positive integers to each vertex v of V in such a manner that the difference between all the integers of vertex v and all the integers of vertex w is greater than b (v, w). The maximum integer assigned in this coloring is called the span of the coloring. The b-coloring is the problem of minimizing this span. No metaheuristic is proposed for general graphs so far for this problem till date as it is strongly NP-hard. In this paper, we proposed three heuristics for the problem including a greedy randomized adaptive search procedure (GRASP). The efficiency of these algorithms is tested on benchmark graphs and their performance is compared among themselves. Experimental results showed that among all three proposed heuristics, GRASP performed well for the given problem. [ABSTRACT FROM AUTHOR]

Deep learning strategies have been achieved over the historical decades to resolve many computer vision applications. Recently, these deep learning algorithms have been extensively used as a tool in classification problems. Generally, the deep learning algorithms trained with gradient-based optimizers, which has some downsides such as the slow speed of convergence and stuck in local minima. As a solution, the planned work using meta-heuristic based Grey Wolf and Whales optimization algorithms for the automatic plant disease detection model. The planned work has explored the application of automatic plant disease identification through the leaf images with the help of the image processing approach. The planned research has evaluated the deep learning algorithm with Grey Wolf and Whales optimization techniques using the three types of datasets, such as Plant Village, New Plant Disease, and Rice Leaf Disease databases. The simulation consequences illustrate that the computational efficiency of the Grey Wolf and Whales based automatic disease identification process is boosted when coupled with the deep learning method. [ABSTRACT FROM AUTHOR]

Cloud computing has become a crucial paradigm for large-scale data-intensive applications, but it also brings challenges like energy consumption, execution time, heat, and operational costs. Improving workflow scheduling in cloud environments can address these issues and optimize resource utilization, leading to significant ecological and financial benefits. As data centres and networks continue to expand globally, efficient scheduling becomes even more critical for achieving better performance and sustainability in cloud computing. Schedulers mindful of energy and deadlines will assign resources to jobs in a way that consumes the least energy while upholding the task's quality standards. Because this scheduling involves a Non-deterministic Polynomial (NP)-hard problem, the schedulers are able to minimize complexity by utilizing metaheuristic techniques. This work has developed methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for optimizing the scheduler. Local search and exploration are respectably supported by heuristic algorithms. The algorithm's exploration and exploitation features must also be balanced. The primary objective is to optimize computation-intensive workflows in a way that minimizes both energy consumption and execution time while maximizing throughput. This optimization should be achieved without compromising the Quality of Service (QoS) guarantee provided to users. The focus is on striking a balance between energy efficiency and performance to enhance the overall efficiency and cost-effectiveness of cloud computing environments. According to the simulation findings, the suggested ABC has a higher guarantee ratio for 5000 jobs when compared to the GA, PSO, GA with the longest processing time, and GA with the lowest processing time, by 7.14 percent, 4.7 percent, 3.5 percent, and 2.3 percent, respectively. It is observed that the proposed ABC possesses qualities like high flexibility, great robustness, and quick convergence leading to good performance. [ABSTRACT FROM AUTHOR]

Wild Horse Optimizer (WHO) is a population-based metaheuristic algorithm inspired by animal behavior, which mainly imitates the decent behavior, grazing behavior, mating behavior and leadership dominance behavior of wild horses in nature to find the optimal. The initialization of the population by imitating the behavior of wild horses is prone to uneven distribution of population positions, and its position updating method is prone to local optimal problems while improving the efficiency of the search. In order to enhance the population diversity and to break out of the local optimum, an adaptive weighted wild horse optimizer based on backward learning and small-hole imaging strategy is proposed. The backward learning strategy is used to enhance the population diversity and improve the uneven distribution of individuals; The adaptive weight and small-hole imaging strategy are added to the local search strategy to improve the global search ability and jump out of the local optimum. To verify the effectiveness of the proposed algorithm, simulation experiments were conducted by using 23 benchmark test functions to test the search ability and Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Rat Swarm Optimizer (RSO) and Multi-Verse Optimizer (MVO) algorithms are compared in terms of their search performance, and finally four real engineering design problems are solved. The simulation results indicate that the proposed FHPWHO has excellent merit-seeking capability. [ABSTRACT FROM AUTHOR]

In wireless-sensing networks (WSNs), the energy economy has lately emerged as the main problem. Since sensor networks run on batteries, they eventually run out of power. To increase the packet transmission ratio for sensing devices, it becomes more difficult to enhance data loss in an energy-efficient manner. In WSNs, the mobile drain causes high network energy usage and data delay. This paper suggests an Improved Ant Colony Clustering-Based Data Transmission Algorithm (EACODT) that first develops the network nodes' energy density function before allocating sensing nodes with higher residual energy as cluster leaders using the energy density function. The EACODT is thoroughly modeled for different WSN situations with variable numbers of sensing nodes and CHs, and the findings are contrasted with some recently developed meta-heuristic algorithms. As a consequence, it is discovered that EACODT gets 34% of energy usage, 98.8% of network lifespan, 95% of packet delivery ratio, 854 kbps of transmission, and a 98% convergence rate. [ABSTRACT FROM AUTHOR]

This paper proposed a multi-objective-based Generation Expansion Planning (GEP) for the real-word power generation system of Tamil Nadu, an Indian state. GEP aims to solve numerous conflicting problems for constructing new power plants. The proposed approaches are Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) and Circle Search algorithm. The key objectives of the proposed method is to reduce budget, to maximize reliability and to minimize the pollutant discharge. Therefore, the apt formulations are modeled and solved to establish the conflicting facets of GEP problem. This paper implements MOCLPSO algorithm to solve Multi-Objective GEP (MOGEP) problem for 7-year and 14-year planning horizon. By then, the proposed model is implemented at MATLAB/Simulink platform and the implementation is calculated. The proposed method shows better results in all approaches like Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO) and Cuckoo Search algorithm. The outcomes establish the competence of MOCLPSO and Circle Search Algorithm to offer good-ranged Pareto optimal non-dominated solutions. [ABSTRACT FROM AUTHOR]

Accurate modeling of electric power generating unit and its hydraulic turbine regulation systems provides support for the speed controller synthesis and stability analysis. It is however a difficult task due to the presence of many non-linear factors in this system. an approach to estimate the parameters of hydraulic turbine regulatory system models is to derive the physical representation of each component and, through simulation, to compare to compare their models, outputs with real data obtained from a hydroelectric plant located in Brazil. The objective of this paper is to find the best values that will represent the system under study as a whole. This problem can be seen as an optimization problem. To find its feasible and optimal solution, this work proposes a new metaheuristics multi-objective based on the Lion Algorithm (LA), called the Multi-Objective Lion Algorithm (MOLA), and its application in the estimation of parameters of the system under study. In addition, the new metaheuristic proposed is validated by using a set of benchmark cases. The results have demonstrated that MOLA outperforms or at least performs similarly to Multi-objective Grey Wolf Optimizer (MOGWO), Multiple Objective Particle Swarm Optimization (MOPSO), Multi-objective Salp Swarm Algorithm (MSSA), Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Non-dominated Sorting Genetic Algorithm III (NSGA-III) in the optimization of multi-objective benchmark functions. These results, suggest that the proposed MOLA algorithm works efficiently. [ABSTRACT FROM AUTHOR]

Concrete is known as one of the most important materials in the world. Concrete composites consisting of cement, water, aggregates, and some additives are used to improve the properties of concrete. These concrete have a certain compressive strength that can be increased depending on the type of concrete. In line with these ideas, high-performance concrete (HPC) has been produced, which can have high compressive strength by adding materials such as fly ash, silica fume, etc. This type of concrete is used in bridges, dams, and special constructions. However, obtaining the mixture design of HPC is problematic and complex, for this reason, the machine learning methods can make it easy to achieve the output by saving time and energy. This study has used support vector regression (SVR) to predict the compressive strength of HPC. Moreover, this study provided two meta-heuristic algorithms for obtaining suitable and optimized results, which are contained the artificial hummingbird algorithm (AHA) and Sine Cosine Algorithm (SCA). The model by coupling with algorithms created the hybrid method in the framework of SVR-AHA and SVR-SCA. Furthermore, some criteria indicators have been used for determining the most desirable hybrid model, which is included coefficient of correlation (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and weight absolute percentage error (WAPE). As a result, the AHA algorithm could have a more satisfying association model with the SVR model, and the results were RMSE = 2.00 (MPa), R2 = 98.59%, MAE = 0.717 (MPa), MAPE = 1.22 (MPa), and WAPE = 0.114 (MPa). [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively. [ABSTRACT FROM AUTHOR]

Today, the Internet of Things (IoT) has an important role for deploying power and energy management in the smart grids as emerging trend for managing power stability and consumption. In the IoT, smart grids has important role for managing power communication systems with safe data transformation using artificial intelligent approaches such as Machine Learning (ML), evolutionary computation and meta-heuristic algorithms. One of important issues to manage renewable energy consumption is intelligent aggregation of information based on smart metering and detecting the user behaviors for power and electricity consumption in the IoT. To achieve optimal performance for detecting this information, a context-aware prediction system is needed that can apply a resource management effectively for the renewable energy consumption for smart grids in the IoT. Also, prediction results from machine learning methods can be useful to manage optimal solutions for power generation activities, power transformation, smart metering at home and load balancing in smart grid networks. This paper aims to design a new periodical detecting, managing, allocating and analyzing useful information regarding potential renewable power and energy consumptions using a context-aware prediction approach and optimization-based machine learning method to overcome the problem. In the proposed architecture, a decision tree algorithm is provided to predict the grouped information based on important and high-ranked existing features. For evaluating the proposed architecture, some other well-known machine learning methods are compared to the evaluation results. Consequently, after analyzing various components by solving different smart grids datasets, the proposed architecture's capacity and supremacy are well determined among its traditional approaches. [ABSTRACT FROM AUTHOR]

With the advances in video technology, the advent of spherical video (360° video) recorded using an omnidirectional camera offers a limitless field-of-view (FoV) to the viewers. However, they suffer from the fear of missing out (FOMO) because they can only see a particular FoV at a time. Reviewing a long recorded surveillance video i.e., 24 hours a day is a time-consuming process due to temporal and spatial redundancy. A solution to this problem is to compactly represent the video synopsis by shifting the objects along the time domain. Using a multi-camera setup for surveillance creates blind spots. This problem is solved by using a spherical camera. Therefore, in this paper, we focus on creating and visualizing the video synopsis recorded by the spherical camera. The optimization algorithm plays a key role in condensing the recorded video. Hence, a novel spherical video synopsis optimization framework has been introduced to generate compact videos that eliminate FOMO. The synopsis is generated by shifting objects on the temporal axis and displays them simultaneously by optimizing multiple constraints. It minimizes activity loss, virtual collisions, temporal inconsistencies, and synopsis video length by preserving interactions between objects. The proposed multiobjective optimization includes a new constraint to restrict the number of objects displayed per frame due to the limitation of the human visual system. Direction-based visualization methods have been proposed to improve the viewer's experience without FOMO. Comparative performance of the proposed framework using the latest metaheuristic optimization algorithms with existing video synopsis optimization algorithms is performed. It is found that chronological disorder ratio and overall virtual collision are minimized effectively through the recent metaheuristics optimization algorithms compared to the related works on video synopsis. [ABSTRACT FROM AUTHOR]

Early detection and classification of breast cancer can be facilitated to initiate the most effective treatment. As the second leading cause of death among women, early breast cancer screening is essential for reducing mortality rates. In this context, Convolutional neural networks (CNNs) are the ideal candidate for increasing the rate of identification and classification of tumours with efficiency, particularly in medical imaging. This research proposes a hybridised CNN with the Orca Predation Optimization Algorithm (OPOA) as a novel classification model for the effective detection of abnormalities in breast cancer diagnosis. Specifically, the OPOA technique is used to determine the optimal hyperparameter values for the hybrid CNN architecture being deployed. As the pretrained CNN model, the suggested model utilizeds a ResNet50 residual network. It merged OPOA with the ResNet50 residual network to construct the OPOA-ResNet-50 Architecture. The experimental validation of the proposed OPOA-ResNet-50 model utilising the datasets of curated breast imaging subset of DDSM (CBIS-DDSM) shown improved classification accuracy of 99.04%, specificity of 98.56%, and sensitivity of 97.78% in comparison to the baseline techniques. The results also revealed that the proposed under mammographic image analysis society (MIAS) OPOA-ResNet-50 model demonstrated superior classification accuracy of 98.64%, specificity of 98.79%, and sensitivity of 98.82% compared to the benchmarked methods. The adopted OPOA algorithm is determined to achieve more optimal hyperparameter values for the ResNet50 architecture than the comparative algorithms Improved Marine Predator Optimization Algorithm (IMPOA), Whale Optimization Algorithm (WOA), Harris hawk's optimization (HHO), and gravitational search algorithm (GSA). [ABSTRACT FROM AUTHOR]

The reptile search algorithm (RSA) is a dynamic and effective meta-heuristic algorithm inspired by the behavior of crocodiles in nature and the way of hunting prey. Unlike other crawler search algorithms, it uses four novel mechanisms to update the location of the solutions, such as walking at high or on the belly, and hunting in a coordinated or cooperative manner. In this algorithm, the total number of iterations is divided into four intervals, and different position-updating strategies are used to make the algorithm easily fall into the local optimum. Therefore, an improved reptile search algorithm based on a mathematical optimization accelerator (MOA) and elementary functions is proposed to improve its search efficiency and make it not easily fall into local optimum. MOA was used to realize the switching of RSA's four searching modes by introducing random perturbations of six elementary functions (sine function, cosine function, tangent function, arccosine function, hyperbolic secant function and hyperbolic cosecant function), four mechanisms are distinguished by random number instead of the original RSA algorithm's inherent four mechanisms by iteration number, which increases the randomness of the algorithm and avoids falling into local optimum. The random perturbations generated by elementary functions are added to the variation trend of parameter MOA to improve the optimization accuracy of the algorithm. To verify the effectiveness of the proposed algorithm, 30 benchmark functions in CEC2017 were used for carrying out simulation experiments, and the optimization performance was compared with BAT, PSO, ChOA, MRA and SSA. Finally, two practical engineering design problems are optimized. Simulation results show that the proposed sechRSA has strong global optimization ability. [ABSTRACT FROM AUTHOR]

The text clustering model becomes an essential process to sort the unstructured text data in an appropriate format. But, it does not give the pave for extracting the information to facilitate the document representation. In today's date, it becomes crucial to retrieve the relevant text data. Mostly, the data comprises an unstructured text format that it is difficult to categorize the data. The major intention of this work is to implement a new text clustering model of unstructured data using classifier approaches. At first, the unstructured data is taken from standard benchmark datasets focusing on both English and Telugu languages. The collected text data is then given to the pre-processing stage. The pre-processed data is fed into the model of the feature extraction stage 1, in which the GloVe embedding technique is used for extracting text features. Similarly, in the feature extraction stage 2, the pre-processed data is used to extract the deep text features using Text Convolutional Neural Network (Text CNN). Then, the text features from Stage 1 and deep features from Stage 2 are all together and employed for optimal feature selection using the Hybrid Sea Lion Grasshopper Optimization (HSLnGO), where the traditional SLnO is superimposed with GOA. Finally, the text clustering is processed with the help of Deep CNN-assisted hierarchical clustering, where the parameter optimization is done to improve the clustering performance using HSLnGO. Thus, the simulation findings illustrate that the framework yields impressive performance of text classification in contrast with other techniques while implementing the unstructured text data using different quantitative measures. [ABSTRACT FROM AUTHOR]

In general, Wireless Sensor Networks (WSNs) require secure routing approaches for delivering the data packets to their sinks or destinations. Most of the WSNs identify particular events in their explicit platforms. However, several WSNs may examine multiple events using numerous sensors in a similar place. Multi-sink and multi-hop WSNs include the ability to offer network efficiency by securing effective data exchanges. The group of nodes in the multi-sink scenario is described through a distance vector. Though, the efficiency of multi-sink WSNs is considerably impacted by the routing of data packets and sink node placement in the cluster. In addition, many WSNs for diverse reasons existed in the similar geographical region. Hence, in this task, a secured energy-efficient routing technique is designed for a Wireless sensor network with Large-scale and multiple sink nodes. Here, the concept of an improved meta-heuristic algorithm termed Adaptive Squirrel Coyote Search Optimization (ASCSO) is implemented for selecting the accurate selection of cluster head. The fitness function regarding residual distance, security risk, energy, delay, trust, and Quality of Service (QoS) is used for rating the optimal solutions. The consumption of energy can be reduced by measuring the mean length along with the cluster head and multiple sink nodes. The latest two heuristic algorithms such as Coyote Optimization Algorithm (COA) and Squirrel Search Algorithm (SSA) are integrated for suggesting a new hybrid heuristic technique. Finally, the offered work is validated and evaluated by comparing it with several optimization algorithms regarding different evaluation metrics between the sensor and sink node. [ABSTRACT FROM AUTHOR]

Low power wide area networks (LPWANs) are made to survive conditions of extensive installation. Technological innovations, including Global Network Operator, Long Range Wide Area Network (LoRaWAN), Narrowband Internet of Things (NB-IoT), Weightless, Sigfox, etc., have adopted LPWANs. LoRaWAN is currently regarded to be one of the most cutting-edge and intriguing technology for the widespread implementation of the IoT. Although LoRaWAN offers the best features that make it fit with Internet - of - things specifications, there are still certain technical issues to overcome, such as link coordination, resource allocation and reliable transmission. In LoRaWAN, End-devices transmit randomized uplink frames to the gateways using un-slotted random-access protocol. This randomness with the restrictions placed on the gateways is a reason that leads to a considerable decline in network performance, in particular downlink frames. In this paper, we propose a new approach to increase Acknowledgement (ACK) messages throughput. The suggested method takes advantage of both class A and class B features to enhance and assist LoRaWAN's reliability by ensuring that an ACK message is sent for every confirmed uplink while retaining the minimum energy level that is utilized by nodes. [ABSTRACT FROM AUTHOR]

The compressive strength and slump of concrete have highly nonlinear functions relative to given components. The importance of predicting these properties for researchers is greatly diagnosed in developing constructional technologies. Such capacities should be progressed to decrease the cost of expensive experiments and enhance the measurements' accuracy. This study aims to develop a Radial Basis Function Neural Network (RBFNN) to model the hardness features of High-Performance Concrete (HPC) mixtures. In this function, optimizing the predicting process via RBFNN will be aimed to be accurate, as the aim of this research, conducted with metaheuristic approaches of Henry gas solubility optimization (HGSO) and Multiverse Optimizer (MVO). The training phase of models RBHG and RBMV was performed by the dataset of 181 HPC mixtures having fly ash and superplasticizer. Regarding the results of hybrid models, the MVO had more correlation between the predicted and observed compressive strength and slump values than HGSO in the R2 index. The RMSE of RBMV (3.7 mm) was obtained 43.2 percent lower than that of RBHG (5.3 mm) in the appraising slump of HPC samples, while, for compressive strength, RMSE was 3.66 MPa and 5 MPa for RBMV and RBHG respectively. Moreover, to appraise slump flow rates, the R2 correlation rate for RBHG was computed at 96.86 % while 98.25 % for RBMV in the training phase, with a 33.30% difference. Generally, both hybrid models prospered in doing assigned tasks of modeling the hardness properties of HPC samples. [ABSTRACT FROM AUTHOR]

Concerns of security as well as privacy are the chief obstacles which have prevented the public cloud's extensive adoption in Intel IT as well as across the industry. Generally, IT organizations are quite reluctant to store sensitive as well as valuable data in infrastructures which are out of their control. The technique of anonymization is employed by enterprises to raise the security of the public cloud's data whilst facilitating the data's analysis as well as application. The procedure of data anonymization will modify how the data is either employed or published in such a way that it will prevent the key information's identification. The privacy issues are addressed using k-anonymity. However, the issue of selecting the variables for anonymization and suppression of variables without the loss of knowledge is an optimization problem. To address the selection of variables for anonymization and suppression, metaheuristic algorithms are used. Diverse research groups have successfully utilized the River Formation Dynamics (RFD) metaheuristic to handle numerous problems of discrete combinatorial optimization. Even so, this metaheuristic has never been adapted for use in domains of continuous optimization. To mitigate the local minima problem, hybridization of the algorithms is proposed. In this work, a modified K-Anonymity technique's proposal has been given by using the Modified Hill Climbing (MHC) optimization, the RFD-MHC optimization, the RFD-PSO optimization, the RFD-MHC suppression as well as the RFD-PSO suppression. Furthermore, proposal for a suppression technique has also been given in this work. Experiments demonstrated that the RFD-PSO optimization has higher classification accuracy in the range of 6.73% to 8.55% when compared to manual K-anonymization. The work has also given better trade off for security analysis and data utility effectiveness. [ABSTRACT FROM AUTHOR]

Multi-compartment vehicle routing problem (MCVRP) is an extension of the classical capacitated vehicle routing problem where products with different characteristics are transported together in one vehicle with multiple compartments. This paper deals with this problem, whose objective is to minimize the total travel distance while satisfying the capacity and maximum route length constraints. We proposed a hybrid iterated local search metaheuristic (HILS) algorithm to solve it. In the framework of iterated local search, the current solution was improved iteratively by five neighborhood operators. For every obtained neighborhood solution after the local search procedure, a large neighborhood search-based perturbation method was executed to explore larger solution space and get a better neighborhood solution to take part in the next iteration. In addition, the worse solutions found by the algorithm were accepted by the nondeterministic simulated annealing-based acceptance rule to keep the diversification of solutions. Computation experiments were conducted on 28 benchmark instances and the experimental results demonstrate that our presented algorithm finds 17 new best solutions, which significantly outperforms the existing state-of-the-art MCVRP methods. [ABSTRACT FROM AUTHOR]

A novel metaheuristic algorithm has been presented based on the physical significance of palm tree leaves and petioles, which can themselves water and fertilize with their unique architecture. Palm tree leaves collect almost all the raindrops that fall on the tree, which drags the nutrient-rich dropping of crawlers and birds that inhabit it and funnel them back to the palm tree's roots. The proposed Palm Tree Optimization (PTO) algorithm is based on two main stages of rainwater before it reaches the trunk. Stage one is that the rainwater drops search for petioles in the local search space of a particular leaf, and stage two involves that the rainwater drops after reaching the petioles search for trunk to funnel back to the root along with nutrients. The performance of PTO in searching for global optima is tested on 33 Standard Benchmark Functions (SBF), 29 constrained optimization problems from IEEE-CEC2017 and real-world optimization problems from IEEE-CEC2011 competition especially for testing the evolutionary algorithms. Mathematical benchmark functions are classified into six groups as unimodal, multimodal, plate & valley-shaped, steep ridges, hybrid functions and composition functions which are used to check the exploration and exploitation capabilities of the algorithm. The experimental results prove the effectiveness of the proposed algorithm with better search ability over different classes of benchmark functions and real-world applications. [ABSTRACT FROM AUTHOR]

Multipath routing helps to establish various quality of service parameters, which is significant in helping multimedia broadcasting in the Internet of Things (IoT). Traditional multicast routing in IoT mainly concentrates on ad hoc sensor networking environments, which are not approachable and vigorous enough for assisting multimedia applications in an IoT environment. For resolving the challenging issues of multicast routing in IoT, CrowWhale-energy and trust-aware multicast routing (CrowWhale-ETR) have been devised. In this research, the routing performance of CrowWhale-ETR is analyzed by comparing it with optimization-based routing, routing protocols, and objective functions. Here, the optimization-based algorithm, namely the Spider Monkey Optimization algorithm (SMO), Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO) algorithm, Water Wave Optimization (WWO) algorithm, Crow Search Algorithm (CSA), and, routing protocols, like Ad hoc On-Demand Distance Vector (AODV), CTrust-RPL, Energy-Harvesting-Aware Routing Algorithm (EHARA), light-weight trust-based Quality of Service (QoS) routing, and Energy-awareness Load Balancing-Faster Local Repair (ELB-FLR) and the objective functions, such as energy, distance, delay, trust, link lifetime (LLT) and EDDTL (all objectives) are utilized for comparing the performance of CrowWhale-ETR. In addition, the performance of CrowWhale-ETR is analyzed in terms of delay, detection rate, energy, Packet Delivery Ratio (PDR), and throughput, and it achieved better values of 0.539 s, 0.628, 78.42%, 0.871, and 0.759 using EDDTL as fitness. [ABSTRACT FROM AUTHOR]

Assessment of energy needed for crack growth in concrete structures has been an interesting topic since the use of fracture mechanics to concrete. However, experimental procedures need time, cost and efforts. Based on historical data, regression approaches were created using mechanical characteristics and mixed design factors to quantify the concrete preliminary (Gf) and whole (GF) fracture energy. This work combined support vector regression (SVR) analysis with antlion optimization (ALO) and Harris Hawks optimization (HHO) approaches to build a hybridized SVR evaluation to fully comprehend Gf and GF. Evaluation metrics demonstrate that both optimized ALO-SVR and HHO-SVR assessments could perform wonderfully throughout the estimation mechanism. Whenever the superior SVR investigation was contrasted to the literature, it was observed that the uniquely developed ALO-SVR regression also provides a reasonable boost in effectiveness, with benefits across the board. Finally, although the HHO-SVR technique has its particular capabilities in the simulating procedure, the ALO-SVR analysis seems to be highly reliable for determining Gf and GF. [ABSTRACT FROM AUTHOR]

Slime mould algorithm (SMA) is a novel meta-heuristic algorithm with fast convergence speed and high convergence accuracy. However, it still has some drawbacks to be improved. The exploration and exploitation of SMA is difficult to balance, and it easy to fall into local optimum in the late iteration. Aiming at the problems existing in SMA, a multistrategy slime mould algorithm named GCSMA is proposed for global optimization in this paper. First, the Logistic-Tent double chaotic map approach is introduced to improve the quality of the initial population. Second, a dynamic probability threshold based on Gompertz curve is designed to balance exploration and exploitation. Finally, the Cauchy mutation operator based on elite individuals is employed to enhance the global search ability, and avoid it falling into the local optimum. 12 benchmark function experiments show that GCSMA has superior performance in continuous optimization. Compared with the original SMA and other novel algorithms, the proposed GCSMA has better convergence accuracy and faster convergence speed. Then, a special encoding and decoding method is used to apply GCSMA to discrete flexible job-shop scheduling problem (FJSP). The simulation experiment is verified that GCSMA can be effectively applied to FJSP, and the optimization results are satisfactory. [ABSTRACT FROM AUTHOR]