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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the essential data pre-processing methods for enhancing the performance of machine learning (ML) models is feature selection. Because they choose the most optimal features for ML problems, metaheuristic feature selection algorithms have gained popularity recently. The Gravitational Search Optimization Algorithm (GSOA), Emperor Penguin Optimization (EPO), and an integrated (hGSEPO) algorithm that combines GSOA and EPO are three metaheuristic feature selection algorithms that are presented in this paper. GSOA performs the global search in hGSEPO, and Emperor Penguin Optimizer (EPO) performs a more thorough local search. In order to find influential features while eliminating irrelevant features and reducing complexity, this article introduces a pioneering hybrid approach that combines the two distinct algorithms GSOA and EPO. While the baseline algorithms have been employed for feature selection in a few ML tasks, the hybrid of these two has been used for the first time for breast cancer (BC) classification. The reason for selecting BC as a case of investigation is due to its recognition as the second leading cause of death in women. According to earlier research, the feature selection (FS) stage is crucial when processing large datasets with the goal of forecasting medical conditions like BC. Based on the selection of the most important features necessary to achieve enhanced accuracy, this intelligent classification system divides the data from the benchmark BC Wisconsin Diagnostic Breast Cancer (WDBC) feature set into two classes. Additionally, the intention of the research is to ascertain the minimum quantity of features necessary to attain a higher level of accuracy. The experimental results show that the proposed approach works auspiciously and categorizes with astounding results, with the highest accuracy of 97.66%, 0.9687 sensitivity, 1.000 specificity, 1.000 precision, 0.9516 F1-score, and 0.9980 area under the curve (AUC). [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have great role in solving problems related to optimization. Meta-heuristic method cannot solve problems related to optimization due to No Free Lunch theory. Hence different optimization methods are proposed by various researchers each year in order to solve optimization problems. Forest Optimization Algorithm (FOA) is an evolutionary optimization algorithm that is appropriate for continuous nonlinear optimization problems. The algorithm drawbacks include entrapment in local optimum and failure in achieving global optimum. The paper proposes hybrid algorithm called FOAGSA, in which the Gravitational Search Algorithm (GSA) is employed to improve the FOA performance in order to solve nonlinear continuous problems. The FOAGSA was evaluated through 39 benchmark optimization functions and two engineering problems. The experimental results proved that the FOAGSA exhibited acceptable results compared to state-of-art and well-known Meta-heuristic algorithms. Friedman ranking algorithm was utilized to compare FOAGSA with existing methods. The FOAGSA was ranked first on that basis. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid algorithm that combines the Butterfly Optimization Algorithm (BOA) and Quantum-behavior Particle Swarm Optimization (QPSO) algorithms, leveraging [Math Processing Error] g b e s t to establish an algorithm communication channel for cooperation. Initially, the population is split into two equal subgroups optimized by BOA and QPSO respectively, with the latter incorporating the Lévy flight for enhanced performance. Subsequently, a hybrid mechanism comprising a weight hybrid mechanism, a elite strategy, and a diversification mechanism is introduced to blend the two algorithms. Experimental evaluation on 12 benchmark test functions and the Muskin model demonstrates that the synergy between BOA and QPSO significantly enhances algorithm performance. The hybrid mechanism further boosts algorithm performance, positioning the new algorithm as a high-performance method. In the Muskingum model experiment, the algorithm proposed in this article can give the best sum of the square of deviation (SSQ) and is superior in the comparison of other indicators. Overall, through benchmark test function experiments and Muskin model evaluations, it is evident that the algorithm proposed in this paper exhibits strong optimization capabilities and is effective in addressing practical problems. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is facing more challenges in solving high-dimensional global optimization problems. In order to overcome this difficulty, this paper proposes a novel PSO variant of the hybrid Sine Cosine Algorithm (SCA) strategy, named Velocity Four Sine Cosine Particle Swarm Optimization (VFSCPSO). The introduction of the SCA strategy in the velocity formulation ensures that the global optimal solution is found accurately. It increases the flexibility of PSO. A series of experiments are conducted on the CEC2005 test suite with compositional algorithms, algorithmic variants, and good intelligent algorithms. The experimental results show that the algorithm effectively improves the overall performance of compositional algorithms; the Friedman test proves that the algorithm has good competitiveness. The algorithm also performs better in PID parameter tuning. Therefore, the VFSCPSO is able to solve the high-dimensional global optimization problems in a better way. [ABSTRACT FROM AUTHOR]

The optimal unmanned aerial vehicle (UAV) path planning using bioinspired algorithms requires high computation and low convergence in a complex 3D environment. To solve this problem, a hybrid A*-FPA algorithm was proposed that combines the A* algorithm with a flower pollination algorithm (FPA). The main idea of this algorithm is to balance the high speed of the A* exploration ability with the FPA exploitation ability to find an optimal 3D UAV path. At first, the algorithm starts by finding the locally optimal path based on a grid map, and the result is a set of path nodes. The algorithm will select three discovered nodes and set the FPA's initial population. Finally, the FPA is applied to obtain the optimal path. The proposed algorithm's performance was compared with the A*, FPA, genetic algorithm (GA), and partical swarm optimization (PSO) algorithms, where the comparison is done based on four factors: the best path, mean path, standard deviation, and worst path length. The simulation results showed that the proposed algorithm outperformed all previously mentioned algorithms in finding the optimal path in all scenarios, significantly improving the best path length and mean path length of 79.3% and 147.8%, respectively. [ABSTRACT FROM AUTHOR]

Solving many-objective optimization problems has become one of the most popular research areas in recent years due to its ever-increasing applications in industries and other fields. In this paper, a novel two-phase hybrid feeder (TPHF) is proposed to provide elite solutions for selection mechanism of many-objective optimization algorithms (MaOAs) to improve their performance. The proposed TPHF framework generates solutions using a novel particle swarm optimization (PSO) operator along with a genetic algorithm operator during a two-phase calculated search based on the average velocity of the PSO particles. TPHF focuses on the worst solutions of the population to find a better place for them. Therefore, it frequently resets the PSO particles to the worst solutions. The new PSO operator uses the novel idea of dynamic inertia and learning factors and a novel velocity update equation. The classic global bests set of the classic PSO operator is replaced by a PSO feeder which uses the novel idea of using groups of the best/worst solutions to feed the new PSO operator based on the phase of the search. The proposed TPHF is applied to some of the most famous and state-of-the-art MaOAs with their corresponding default parameters. The result of the comparison between these MaOAs with their corresponding TPHF versions on MaF test suite shows a significant improvement in the performance of the TPHF versions in 62.2% of the cases. [ABSTRACT FROM AUTHOR]

In this study, an improved version of aquila optimizer (AO) known as EHAOMPA has been developed by using the marine predators algorithm (MPA). MPA is a recent and well-behaved optimizer with a unique memory saving and fish aggregating devices mechanism. At the same time, it suffers from various defects such as inadequate global search, sluggish convergence, and stagnation of local optima. However, AO has contented robust global exploration capability, fast convergence speed, and high search efficiency. Thus, the proposed EHAOMPA aims to complement the shortcomings of AO and MPA while bringing new features. Specifically, the representative-based hunting technique is incorporated into the exploration stage to enhance population diversity. At the same time, random opposition-based learning is introduced into the exploitation stage to prevent the optimizer from sticking to local optima. This study tests the performance of EHAOMPA's on 23 standard mathematical benchmark functions, 29 complex test functions from the CEC2017 test suite, six constrained industrial engineering design problems, and a convolutional neural network hyperparameter (CNN-hyperparameter) optimization for Corona Virus Disease 19 (COVID-19) computed tomography-image detection problem. EHAOMPA is compared with four existing optimization algorithm types, achieving the best performance on both numerical and practical issues. Compared with other methods, the test function results demonstrate that EHAOMPA exhibits a more potent global search capability, a higher convergence rate, increased accuracy, and an improved ability to avoid local optima. The excellent experimental results in practical problems indicate that the developed EHAOMPA has great potential in solving real-world optimization problems. The combination of multiple strategies can effectively improve the performance of the algorithm. The source code of the EHAOMPA is publicly available at https://github.com/WangShuang92/EHAOMPA. [ABSTRACT FROM AUTHOR]

The stock market is dynamic and highly chaotic due to its complicated nature. The prediction of future stock prices has gained the interest of investors and researchers. Numerous conventional and hybrid approaches were put forth. The projections performed poorly and strangely did not get any better. This study aimed to predict Philippine Stock Exchange closing prices using a proposed hybrid method. This proposed study aimed to improve the Artificial Neural Network (ANN) performance using the Kepler Optimization Algorithm, a metaheuristic algorithm, in conjunction with the Artificial Neural Network (ANN-KOA). ANN is a widely accepted technique in predicting. Still, the Kepler Optimization Algorithm can provide random initial inputs for the method through effective feature selection. It can identify superior subsets of input variables to integrate into ANN, enabling more accurate prediction. In this study, the ups and downs were grouped, and 12 technical indicators with varying lengths of days 3, 5, 10, 15, and 20 were used to decompose the original historical data, which are regular and smoother than the original data. The historical data was normalized into [-1, 1], so the predicted result would be 0 (decrease) or 1 (increase) otherwise. Finally, to obtain the train and test data, it was fused into five groups and tested using the ANN and ANN-KOA. The experiment's outcome of 0 (a declining stock price) indicates two alternative courses of action based on the two scenarios: holding if investors have already purchased it or purchasing if they haven't made a decision. In this proposed study, ANN-KOA resulted in higher accuracy than ANN. However, regarding the number of elapsed times, KOA-ANN provided a slower time than ANN. On the other hand, in terms of variations of lengths of the days, 3-5-10-KOA-ANN outperformed the variations of lengths of days: 3-5-10-ANN, 3-5-10-15-KOA-ANN, 3-5-10-15-20-KOA, and 3-5-10-15-20-KOA-ANN. In conclusion, this study suggested that it be utilized for other problems, such as predicting academic performance, diseases, floods, etc.

Total nitrogen concentration (CTN) enrichment is the primary cause of natural water eutrophication. Accurately estimating CTN and its spatiotemporal dynamics is crucial for formulating monitoring and control measures to alleviate lake eutrophication. A hybrid model was proposed for estimating CTN in optically complex inland waters by incorporating the relationship between CTN and water optical active components for Zhuhai-1 Orbita hyperspectral (OHS) imagery. Compared with other semi-analytical algorithms, the re-adjusted reference wavelength QAA716 shows the best performance in aph(λ) retrieval. The hybrid model for CTN estimation achieves an root mean square deviation (RMSD) of 0.20 mg/L, a mean absolute percentage deviation (MAPD) of 6.96 %, and a unbiased mean absolute percentage deviation (UMAPD) of 6.96 %, with aph(569) accuracy exerting the greatest influence. Ground-satellite synchronous validation demonstrates robust performance, with an RMSD of 0.28 mg/L, a MAPD of 14.49 %, and a UMAPD of 14.92 %. The hybrid model was applied to OHS observations of Lake Dianchi from April 2019 to September 2021. The analysis revealed a generally decreasing trend in CTN during this timeframe. The above results demonstrate that the robustness and applicability of the proposed CTN hybrid model for inland waters with complex optical properties. Furthermore, satellite-based data products provide valuable information for formulating lake management strategies.

This paper presents a novel hybrid algorithm that combines the Butterfly Optimization Algorithm (BOA) and Quantum-behavior Particle Swarm Optimization (QPSO) algorithms, leveraging $ gbest $ to establish an algorithm communication channel for cooperation. Initially, the population is split into two equal subgroups optimized by BOA and QPSO respectively, with the latter incorporating the Lévy flight for enhanced performance. Subsequently, a hybrid mechanism comprising a weight hybrid mechanism, a elite strategy, and a diversification mechanism is introduced to blend the two algorithms. Experimental evaluation on 12 benchmark test functions and the Muskin model demonstrates that the synergy between BOA and QPSO significantly enhances algorithm performance. The hybrid mechanism further boosts algorithm performance, positioning the new algorithm as a high-performance method. In the Muskingum model experiment, the algorithm proposed in this article can give the best sum of the square of deviation (SSQ) and is superior in the comparison of other indicators. Overall, through benchmark test function experiments and Muskin model evaluations, it is evident that the algorithm proposed in this paper exhibits strong optimization capabilities and is effective in addressing practical problems.

Non-technical losses (NTL) pose multiple challenges across distribution grids. This paper introduces a comprehensive framework combining data-driven methods to detect and categorize fraud due to meter tampering or direct connections while identifying potential culprits. The hybrid methodology utilizes grid and consumer-related data to obtain NTL curves through an energy balance approach, yielding indicators such as magnitude, duration, and other features. A Random Forest classifier trained with real historical cases of NTL achieves a weighted F1 score of 0.859, effectively labelling fraud types. Additionally, an unsupervised detection model, integrating clustering and correlation methods, enables accurate identification of tampered meters. The paper introduces two adjustable parameters enabling utilities to fine-tune meter tampering detection strategies based on economic considerations. The results demonstrate that true positives can be increased at the expense of increasing false positives. Accurate fraud identification is achieved using the Fuzzy C-Means algorithm, with an F1 score of 0.9. The algorithm is tested on grids with distributed generation, with a decrease of 10% on the predictive performance when half of the users are prosumers, demonstrating the methodology’s promising performance in real-world scenarios.

This paper deals with the problem of selecting a set of clients that will receive an offer of one or more products during a promotional campaign. Such campaigns are essential marketing tools to improve the economic profit of an enterprise, either by acquiring new customers or generating additional revenue from existing customers. In this research, a well-known mathematical model for the problem is used and extended with the cannibalism constraint which avoids some products being offered simultaneously to simulate competing products cannibalizing each other’s market. To solve this problem, a hybrid heuristic is proposed, which uses a genetic algorithm (GA) as long-term memory for a tabu search (TS). The main idea is not to use GA exclusively as an optimization procedure but also as a diversification strategy. In particular, GA elite solutions replace the TS’s current solutions exploring in this way new areas in the search space. GA also receives the best TS solutions to maintain its population with high-quality solutions. Extensive computational experiments are performed on a set of existing benchmark test problems integrated with the restriction of cannibalism. A new set of instances with a high degree of difficulty is generated and are available to the research community through GitHub. The proposed method is compared with state-of-the-art methods demonstrating better overall performance (sometimes more than 10 percentage points) and statistical significance.

Dynamic flexible job-shop scheduling is traditionally a challenge in real-world manufacturing systems, especially considering the constraints of transportation resources and transportation time. To address the dynamic optimisation problem in flexible manufacturing systems, this paper proposes a novel proactive-reactive methodology to adapt to the dynamic changes in working environments and addresses the joint scheduling problem for machine tools and transportation resources. The joint optimisation model is first formulated as a mixed-integer programming model considering production efficiency and transportation constraints. The flowchart of the dynamic scheduling system is then designed for dynamic decision-making, and a novel particle swarm optimisation algorithm integrated with genetic operators is developed to respond to dynamic events and generate the reschedule plan in time. Finally, several numerical experiments and case studies in reality are applied to verify the efficiency of the developed methodology. Common dispatching rules and heuristic methods are also applied to test and evaluate the efficiency of the developed algorithm. Computational results demonstrate that the developed methods and decision models are efficient for dynamic job-shop scheduling problems in flexible manufacturing systems, which can acquire rather a good effect in practical production. [ABSTRACT FROM AUTHOR]

Efficient scheduling and load balancing are essential for optimizing performance in multiprocessor systems. This study proposes a novel hybrid algorithm that integrates beam search and differential evaluation techniques within the domain of artificial intelligence (AI) to address these challenges. Our objective is to minimize the operational completion time (OCT), a critical metric for evaluating system performance. Beam search is utilized to explore the solution space effectively, enabling the algorithm to identify promising solutions. Moreover, we employ a differential evaluation approach to assess the quality of candidate solutions and guide the search toward optimal or near-optimal scheduling and load-balancing configurations. By combining these techniques, our hybrid algorithm aims to minimize OCT, thereby enhancing system throughput and resource utilization. Experimental evaluations demonstrate the effectiveness of our approach in achieving improved performance compared to traditional methods. This research contributes to advancing the field of AI in multiprocessor systems optimization, providing practical solutions for real-world deployment in high-performance computing environments.

This research aims to forecast, using various criteria, the flow of soil erosion that will occur at a particular geographical location. As for the training dataset, 80% of the dataset from the sample sites, four hybrid algorithms, namely heap-based optimizer (HBO), political optimizer (PO), teaching-learning based optimization (TLBO), and backtracking search algorithm (BSA) combined with artificial neural network (ANN) was used to create an erosion susceptibility model and establishes a unique and original approach. After it was confirmed to be successful, the algorithms were applied to create a susceptibility map for this area, demonstrating the integrity of the results. The AUC values were computed for every optimisation algorithm used in this study. The optimal estimated accuracy indices for populations of 450 were determined to be 0.9846 using the BSA-MLP training databases. The maximum AUC value for the HBO-MLP training databases with different swarm sizes was 0.9736. A swarm size of 350–300 is considered optimal for forecasting erosion susceptibility mapping in hybrid models. With the same swarm size constraints, the AUC value for training in the TLBO-MLP scenario was 0.996. After 150 swarm size conditions were used to train the PO-MLP model, the AUC values were 0.9845. According to these findings, the TLBO-MLP and PO-MLP algorithms worked best with populations of 50 and 150, respectively.

The most significant threat that networks established in IoT may encounter is cyber attacks. The most commonly encountered attacks among these threats are DDoS attacks. After attacks, the communication traffic of the network can be disrupted, and the energy of sensor nodes can quickly deplete. Therefore, the detection of occurring attacks is of great importance. Considering numerous sensor nodes in the established network, analyzing the network traffic data through traditional methods can become impossible. Analyzing this network traffic in a big data environment is necessary. This study aims to analyze the obtained network traffic dataset in a big data environment and detect attacks in the network using a deep learning algorithm. This study is conducted using PySpark with Apache Spark in the Google Colaboratory (Colab) environment. Keras and Scikit-Learn libraries are utilized in the study. 'CICIoT2023' and 'TON_IoT' datasets are used for training and testing the model. The features in the datasets are reduced using the correlation method, ensuring the inclusion of significant features in the tests. A hybrid deep learning algorithm is designed using one-dimensional CNN and LSTM. The developed method was compared with ten machine learning and deep learning algorithms. The model's performance was evaluated using accuracy, precision, recall, and F1 parameters. Following the study, an accuracy rate of 99.995% for binary classification and 99.96% for multiclassification is achieved in the 'CICIoT2023' dataset. In the 'TON_IoT' dataset, a binary classification success rate of 98.75% is reached. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance; however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm(AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms. [ABSTRACT FROM AUTHOR]

Feature selection is an important component of the machine learning domain, which selects the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a given number of features, there are 2n possible feature subsets, making it challenging to select the optimal set of features from a dataset via conventional feature selection approaches. We opted to investigate glaucoma infection since the number of individuals with this disease is rising quickly around the world. The goal of this study is to use the feature set (features derived from fundus images of benchmark datasets) to classify images into two classes (infected and normal) and to select the fewest features (feature selection) to achieve the best performance on various efficiency measuring metrics. In light of this, the paper implements and recommends a metaheuristics-based technique for feature selection based on emperor penguin optimization, bacterial foraging optimization, and proposes their hybrid algorithm. From the retinal fundus benchmark images, a total of 36 features were extracted. The proposed technique for selecting features minimizes the number of features while improving classification accuracy. Six machine learning classifiers classify on the basis of a smaller subset of features provided by these three optimization techniques. In addition to the execution time, eight statistically based performance metrics are calculated. The hybrid optimization technique combined with random forest achieves the highest accuracy, up to 0.95410. Because the proposed medical decision support system is effective and ensures trustworthy decision-making for glaucoma screening, it might be utilized by medical practitioners as a second opinion tool, as well as assist overworked expert ophthalmologists and prevent individuals from losing their eyesight. [ABSTRACT FROM AUTHOR]

Stamping is a very important manufacturing process. To optimize the process parameters, a hybrid surrogate model based on the back-propagation neural network and sparse auto-encoder is proposed and compared with classical surrogate models to verify its reliability. Furthermore, the hybrid improved particle swarm optimization–genetic algorithm, based on chaos theory, is proposed and compared with other algorithms. A double-C part is used as an engineering example to verify the proposed method. The Latin hypercube sampling method is used for sampling and the response value is obtained by AutoForm simulation software. On this basis, the hybrid surrogate model is used to establish the mapping relationship between the forming quality of the double-C part and the stamping process parameters. The optimal stamping process parameters are obtained through the improved hybrid algorithm. The results demonstrate that the wrinkling of the optimized double-C part is significantly reduced and the forming quality is improved. [ABSTRACT FROM AUTHOR]

This paper introduces the Right Truncated Inverse Gompertz Distribution (RTIGD) with two parameters δ and ϑ with some of its properties as; (Survival Function, Hazard-Function, cumulative distribution function, probability density function, r-th moment, mean, variance, Moment Generating Function, Median, and Mode. In addition, we propose a new hybrid algorithm, namely the firefly Algorithm with Cuckoo Search Algorithm (FA_CSA) to estimate the parameters based on the survival-functions of RTIGD. Simulation is utilized to compare the proposed algorithm with traditional methods, for example, the Maximum Likelihood Estimator and moment method and the standard algorithms Firefly Algorithm and Cuckoo Search. In most cases, the results demonstrate that the proposed algorithm (FA_CSA) provides an accurate estimation for the survival function since it has less mean squared error than the other estimation methods. [ABSTRACT FROM AUTHOR]

A cloud is made up of many data centers, with its own set of data and resources. The reasons for employing several cloud sites to operate a workflow are that the data is already dispersed, the required resources surpass the constraints of a single site. This paper presents a hybrid multi-objective optimization algorithm denoted as HSOS-SOA, achieved by combining the Symbiotic Organisms Search and Seagull Optimization Algorithm. The HSOS-SOA uses chaotic maps to generate random numbers and performs a good trade-off between exploration and exploitation, resulting in a higher convergence rate. HSOS-SOA is used to solve scientific workflow scheduling problems in multisite cloud computing by taking into consideration elements such as makespan, cost, and reliability. A solution is chosen from the Pareto front using the knee-point approach in this approach. Extensive analyses are performed out in Microsoft Azure multisite cloud and the results exhibited that the HSOS-SOA can outperform other algorithms in terms of metrics such as IGD, Coverage Ratio, and so on. Experimental results of experiments reveal that the results in makespan improvement in the range of 5.72–28.61%, cost in the range of 5.16–45.16%, and reliability in the range of 3.11–25% over well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

To address the challenges posed by composite targets composed of an anisotropic medium and metal in electromagnetic (EM) scattering calculations, this paper introduces an innovative hybrid algorithm tailored for simulating the EM scattering characteristics of such complex targets. Utilizing impedance boundary condition (IBC), the method employs surface impedance vectors to precisely depict the EM properties of the medium. By harnessing the distinct advantages of the Method of Moments (MoMs) at low frequency and Physical Optics (POs) at high frequency, the algorithm ensures both accuracy and efficiency in the EM simulation of composite targets. By transforming the EM scattering problem of targets coated with a thin-layered medium into an equivalent radiation problem of EM currents on impedance surfaces, this research has achieved rapid and high-precision calculations of the Radar Cross Section (RCS) for complex targets with anisotropic medium coatings. To assess the performance of the algorithm, three target models—square plates, simplified aircraft, and complex satellites—are selected as test cases. The dual metrics of RCS and surface current distribution are utilized as evaluation benchmarks, and comparisons are made against the Method of Moments–Finite Element Method (MoM-FEM) hybrid numerical method. The comparative results demonstrate that the proposed method meets the engineering standards in terms of both the root mean square error (RMSE) of RCS and the relative error in surface current distribution, while also achieving a significant improvement of over 50% in computational efficiency, thereby validating its superior accuracy and practical utility. [ABSTRACT FROM AUTHOR]

Since photovoltaic ( PV) arrays installationin in harsh outdoor environments, PV faults frequently occur during their operation. To accurately identify the fault types of PV arrays, a sand cat swarm optimization support vector machine (SCSO-SVM) was proposed for PV module fault identification. In addition, the SCSO-SVM, support vector machine ( SVM), particle swarm optimized support vector machine (PSO-SVM), genetic optimized support vector machine (GA-SVM), sparrow optimized support vector machine (SSA-SVM), gray wolf optimized support vector machine (GWO-SVM) and whale optimized support vector machine (WOA-SVM) algorithms were compared. First of all, all six SVM hybrid algorithms overcome the disadvantage that SVM diagnosis results were easily affected by the initial values of parameters, and the recognition accuracy was improved compared with traditional SVM algorithms, but the recognition time was increased for all of them. Secondly, SCSO-SVM recognition was the best among the seven algorithms, which overcomed the vulnerability of SVM to the initial values of parameters and improved the recognition accuracy by about 9. 459 4% compared to SVM. Because it is more effective in finding the SVM penalty factors and kernel function parameters. Then, for the same algorithm, the recognition accuracy of the algorithm decreased with decreasing input features because the fewer the input features, the less effective it was to characterize the output properties of the PV modules under different fault types. However, the recognition time of the algorithm was not brief with the decrease of the input features. Therefore, the appropriate input features were selected to balance the fault recognition accuracy and efficiency of the algorithm. Finally, it was found that the recognition effect of the seven algorithms depends on the effect of the dataset. The reason may be that there are differences in generalizability due to excessive selection of parameters for each algorithm and dependence on the initial value selection of parameters. [ABSTRACT FROM AUTHOR]

Production scheduling is a critical factor to enhancing productivity in manufacturing engineering and combinatorial optimization research. The complexity and dynamic nature of production systems necessitates innovative solutions. The Job Shop Flexible Programming Problem (FJSP) provides a realistic environment for production, where processing times are variable and uncertain, and multiple objectives need optimization. To solve the Multi-Objective Flexible Fuzzy Job Shop problem with partial flexibility (P-MOFfJSP), this paper proposes a hybrid metaheuristic approach that combines the Teaching–Learning-based Optimization (TLBO) algorithm with a Genetic Algorithm. The proposed algorithm of Adaptive TLBO (TLBO-A) uses two genetic operators (mutation and crossover) with an adaptive population reconfiguration strategy, ensuring solution space exploration and preventing premature convergence. We have evaluated the TLBO-A algorithm's performance on benchmark instances commonly used in programming problems with fuzzy variables. The experimental analysis indicates significant results, demonstrating that the adaptive strategy improves the search for suitable solutions. The proposed algorithm (TLBO-A) exhibits low variations (around 11%) compared to the best mono-objective heuristic for the fuzzy makespan problem, indicating its robustness. Moreover, compared with other heuristics like traditional TLBO, the variations decrease to around 1%. However, TLBO-A stands out as it aims to solve a multi-objective problem, improving the fuzzy makespan, and identifying good results on the Pareto frontier for the fuzzy average flow time, all within this low variation margin. Our contribution addresses the challenges of production scheduling in fuzzy time environments and proposes a practical hybrid metaheuristic approach. The TLBO-A algorithm shows promising results in solving the P-MOFfJSP, highlighting the potential of our proposed methodology for solving real-world production scheduling problems. [ABSTRACT FROM AUTHOR]

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

This paper introduces Right Truncated Inverse Generalized Rayleigh distribution (RTIGRD) with two parameters and with some of its properties as; (Survival-Function, Probability Density Function, Hazard-Function, Cumulative Distribution Function, R-Th Moment, Mean, Variance, Median, Moment Generating Function, and Mode. In addition, we propose a new hybrid algorithm (Artificial Bee Colony Algorithm with Firefly Algorithm (ABC_FA)) to estimate Survival functions based on the parameters (,) of (RTIGRD). Simulation is utilized to compare the proposed algorithm with traditional methods (Maximum Likelihood Estimator and moment method) and standard algorithms (Artificial Bee Colony Algorithm and Firefly Algorithm). The results show proposed approach (ABC_FA) provides a 100% accurate estimate of the survival function for the cases selected in this research, as it has a less mean square error than other estimation methods. [ABSTRACT FROM AUTHOR]

Efficient scheduling and load balancing are essential for optimizing performance in multiprocessor systems. This study proposes a novel hybrid algorithm that integrates beam search and differential evaluation techniques within the domain of artificial intelligence (AI) to address these challenges. Our objective is to minimize the operational completion time (OCT), a critical metric for evaluating system performance. Beam search is utilized to explore the solution space effectively, enabling the algorithm to identify promising solutions. Moreover, we employ a differential evaluation approach to assess the quality of candidate solutions and guide the search toward optimal or near-optimal scheduling and load-balancing configurations. By combining these techniques, our hybrid algorithm aims to minimize OCT, thereby enhancing system throughput and resource utilization. Experimental evaluations demonstrate the effectiveness of our approach in achieving improved performance compared to traditional methods. This research contributes to advancing the field of AI in multiprocessor systems optimization, providing practical solutions for real-world deployment in high-performance computing environments. [ABSTRACT FROM AUTHOR]

The simple parallel computing or single heterogeneous platform can no longer meet the requirements of signal processing and task scheduling with large computation and high complexity, so heterogeneous multi platform system has become the development trend of signal processing and task scheduling. In view of improving the throughput of the platform, utilization rate of the processor and perception of the task, this study investigates the signal processing model of heterogeneous multi-platform, and the scheduling tasks and hardware and software resources are modeled by directed acyclic graph. Based on the proposed scheduling algorithms, the task scheduling is summarized and compared. It is found that the hybrid scheduling algorithm based on task perception can meet the platform scheduling requirements well. It is a trend of future research to use mixed scheduling algorithm based on task perception to solve task scheduling in signal processing. [ABSTRACT FROM AUTHOR]

In recent years, big data analysis has been one of the hot research topics which attracts more researchers to work on it. Big data is comprised of highly sensitive data which is needed to be secured and monitored effectively. However, problems related to privacy and security have been provoked due to ineffectiveness in storing the data and relying on third parties. To overcome the issues of storing the data in a cloud environment securely, this research introduced a hybrid cryptography algorithm that is comprised with advanced encryption standard (AES), triple data encryption standard (TDES), and blow fish algorithm (BFA). Initially, the data from the user are acquired and uploaded to the cloud storage space, then a key is generated which helps in encrypting and decrypting the user's data. Process of encryption and decryption takes place using the proposed hybrid cryptography algorithm which effectively securely stores the data and helps the user based on their needs. Moreover, the rivest, shamir, adleman (RSA) algorithm is used in the process of key generation which helps to encrypt and decrypt the messages. The efficiency of the proposed approach is compared with the existing triple data encryption standard (TDES), blow fish algorithm - spotted hyena optimization (BFA-SHO) algorithm and secure data deletion and verification (SDVC) scheme on based on ciphertext policy-attribute based encryption (CP-ABE). The results obtained through analysis exhibits that suggested methodology took 183450 ms for executing the file size of 20 MB whereas the existing BFA-SHO took 248107 ms for executing the same file size of 20 MB. [ABSTRACT FROM AUTHOR]

The reliable operation of industrial equipment is imperative for ensuring both safety and enhanced production efficiency. Machine learning technology, particularly the Light Gradient Boosting Machine (LightGBM), has emerged as a valuable tool for achieving effective fault warning in industrial settings. Despite its success, the practical application of LightGBM encounters challenges in diverse scenarios, primarily stemming from the multitude of parameters that are intricate and challenging to ascertain, thus constraining computational efficiency and accuracy. In response to these challenges, we propose a novel innovative hybrid algorithm that integrates an Arithmetic Optimization Algorithm (AOA), Simulated Annealing (SA), and new search strategies. This amalgamation is designed to optimize LightGBM hyperparameters more effectively. Subsequently, we seamlessly integrate this hybrid algorithm with LightGBM to formulate a sophisticated fault warning system. Validation through industrial case studies demonstrates that our proposed algorithm consistently outperforms advanced methods in both prediction accuracy and generalization ability. In a real-world water pump application, the algorithm we proposed achieved a fault warning accuracy rate of 90%. Compared to three advanced algorithms, namely, Improved Social Engineering Optimizer-Backpropagation Network (ISEO-BP), Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), and Grey Wolf Optimizer-Light Gradient Boosting Machine (GWO-LightGBM), its Root Mean Square Error (RMSE) decreased by 7.14%, 17.84%, and 13.16%, respectively. At the same time, its R-Squared value increased by 2.15%, 7.02%, and 3.73%, respectively. Lastly, the method we proposed also holds a leading position in the success rate of a water pump fault warning. This accomplishment provides robust support for the timely detection of issues, thereby mitigating the risk of production interruptions. [ABSTRACT FROM AUTHOR]

The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid-ML classes, was decreased to −25.3%, −29.6% and −57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet-ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has −204.9 value for AIC which has grown by 5.23% (−194.7) compared to the state without any pre-processing method (ANN_Relu_25).

In order to solve the problems of casualties and property losses caused by the delay transprotation of emergency materials after the earthquake disaster, a hybrid algorithm considering time division was proposed. Firstly, according to the location of the epicenter, the disaster point and its material needs were determined; Secondly, according to the constraints of road damage in the disaster area, the distribution mode of the demand in the disaster area were determined; Thirdly, according to the road damage and recovery, the "golden 72 hours" after the earthquake was divided into multiple times and multiple distribution methods; Finally, the "Luding 9·5 earthquake in Sichuan Province" was selected as the background for simulation. The results show that compared with the traditional genetic algorithm, the total distribution cost of hybrid algorithm is reduced by 9.53%, and the total distribution time is reduced by 7.03%. In the multi-time scene, the total delivery time of the overall scheme is reduced by 6.33% and the total delivery cost is reduced by 18 .9% compared with that without considering the time scene. The method of joint distribution by dividing time periods after the earthquake can effectively solve the problem of emergency demand in the disaster area and improve the efficiency of emergency materials distribution.

Titanium alloys are broadly used in the medical and aerospace sectors. However, they are categorized within the hard-to-machine alloys ascribed to their higher chemical reactivity and lower thermal conductivity. This aim of this research was to study the impact of the dry-end-milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. This research aims to study the impact of the dry-end milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. Also, it seeks to develop a new hybrid neural model based on the training back propagation neural network (BPNN) with swarm optimization-gravitation search hybrid algorithms (PSO-GSA). Full-factorial design of the experiment with L27 orthogonal array was applied, and three end-milling parameters (cutting speed, feed rate, and axial depth of cut) with three levels were selected (50, 77.5, and 105 m/min; 0.1, 0.15, and 0.2 mm/tooth; and 1, 1.5, and 2 mm) and investigated to show their influence on the obtained surface roughness. The results revealed that the surface roughness is significantly affected by the feed rate followed by the axial depth. A 0.49 µm was produced as a minimum surface roughness at the optimized parameters of 105 m/min, 0.1 mm/tooth, and 1 mm. On the other hand, a neural network having a single hidden layer with 1–20 hidden neurons, 3 input neurons, and 1 output neuron was trained with both PSO and PSO–GSA algorithms. The hybrid BPNN–PSO–GSA model showed its superiority over the BPNN–PSO model in terms of the minimum mean square error (MSE) that was calculated during the testing stage. The best BPNN–PSO–GSA hybrid model was the 3–18–1 structure, which reached the best testing MSE of 3.8 × 10−11 against 2.42 × 10−5 of the 3–8–1 BPNN–PSO hybrid model.

Renewable radiation is a natural, natural resource that is simultaneously permanent and unrenewable. The two key elements that influence the performance of a PV system are irradiation and temperature. Partial shade, on the other hand, causes a nonlinear maximum power point tracking (MPPT) issue in PV systems. The PV array's P-V characteristics have more than one MPP when exposed to non-uniform solar irradiation. This situation makes tracking MPP more difficult and reduces the PV system's efficiency. This article proposes a hybrid optimization of Aquila optimization (AO) and Bird Swarm Algorithm (BSA) assisted MPPT controller to monitor peak energy in various weather conditions for the MPPT of PV System under partial shading condition for Indoor energy harvesting in smart homes. Finally, the proposed Bird Swarm Fostered Aquila Optimization (BSFAO) assisted MPPT control is compared to that of the standard methods like perturb and observe (P&O), Harris Hawks Optimization (HHO), Bird Swarm Algorithm (BSA), Aquila Optimization (AO). The proposed BSFAO method displays higher MPPT performance and a rapid convergence at the global maxima. Thus, the suggested hybrid (BSFAO) formed MPPT approach provides faster MPPT. The results proved that the suggested BSFAO model has achieved enhanced power tracking efficiency of 99.61% for every shading design when compared to the existing methodology and attained a reduced computational burden (3717.63) approximately, for all shading conditions respectively. [ABSTRACT FROM AUTHOR]

Under the retailing environment, shelf space-based competition between items with different brands has drawn much attention from practitioners and researchers, the decision for low-temperature stored items on limited shelf spaces are compounding this challenge. This paper discusses the shelf display space partition assignment decision for low-temperature dairy items with two-phase demand in remaining shelf-life to maximize the profit of the shelf. Two-phase demand is assumed for the first time based on the knowledge that consumers' perception on the quality of the dairy item is unequal to its true shelf-life. Moreover, considering shelf partitions in different heights carry different sale gaining potentials, three profit weights are given to the partitions. Herein, a new research model named as SPSPA of shelf profit space partition assignment for the low-temperature stored dairy items is constructed. To effectively solve the proposed SPSPA, a hybrid firefly particle swarm optimization (HFPSO) is developed combing the superiority of particle swarm optimization (PSO) in coarse search and firefly algorithm (FA) in fine search. Numerical experiments have been performed on small- and large-scale cases to verify the performance of the proposed HFPSO algorithm by comparing with genetic algorithm, differential evolution (DE), PSO, and FA based on which management insights are obtained for the shelf space assignment decision. [ABSTRACT FROM AUTHOR]

A new implementation of an adiabatically-trained ensemble model is derived that shows significant improvements over classical methods. In particular, empirical results of this new algorithm show that it offers not just higher performance, but also more stability with less classifiers, an attribute that is critically important in areas like explainability and speed-of-inference. In all, the empirical analysis displays that the algorithm can provide an increase in performance on unseen data by strengthening stability of the statistical model through further minimizing and balancing variance and bias, while decreasing the time to convergence over its predecessors. [ABSTRACT FROM AUTHOR]

Healthcare cost is an issue of concern right now. While many complex machine learning algorithms have been proposed to analyze healthcare cost and address the shortcomings of linear regression and reliance on expert analyses, these algorithms do not take into account whether each characteristic variable contained in the healthcare data has a positive effect on predicting healthcare cost. This paper uses hybrid machine learning algorithms to predict healthcare cost. First, network structure learning algorithms (a score-based algorithm, constraint-based algorithm, and hybrid algorithm) for a Conditional Gaussian Bayesian Network (CGBN) are used to learn the isolated characteristic variables in healthcare data without changing the data properties (i.e., discrete or continuous). Then, the isolated characteristic variables are removed from the original data and the remaining data used to train regression algorithms. Two public healthcare datasets are used to test the performance of the proposed hybrid machine learning algorithm model. Experiments show that when compared to popular single machine learning algorithms (Long Short Term Memory, Random Forest, etc.) the proposed scheme can obtain similar or higher prediction accuracy with a reduced amount of data. [ABSTRACT FROM AUTHOR]

In the scheduling literature, the studies that consider unavailable periods (UPs) have generally ignored the resources. However, when the resources to be used in unavailable periods are limited and these resources are needed for more than one machine at the same time, the problem of when the resource should be allocated to which machine arises. This decision is important as it can greatly affect the effectiveness of the machine schedule. For this reason, it is necessary to consider not only the UPs, but also the resources used by the UPs. In this study, flow shop scheduling problem with unavailable periods, flexible in a time window, and additional resources is discussed. In the considered problem, since additional resources are required during the unavailable periods and they can serve just one machine at a time, they cannot overlap. A MIP model and a hybrid algorithm that genetic algorithm and modified subgradient algorithm works together, have been developed for the considered problem. The performance of the hybrid algorithm is compared with pure genetic algorithm and Cplex solver of GAMS by using randomly generated test problems. Test results showed that while hybrid algorithm has solution quality advantage, genetic algorithm has solution time advantage. In addition, with the developed hybrid algorithm, GAMS results were improved up to 88%. [ABSTRACT FROM AUTHOR]

The proposed work presents a novel composite polymer filament made by blending High-Density Polyethylene (HDPE) and Polycarbonate (PC) with Acrylonitrile Butadiene Styrene (ABS), resulting in improved properties such as strength-to-weight ratio and impact resistance, expanding the applicability of ABS in healthcare, automotive, and engineering industries. The composite polymer filament was fabricated using a single screw extruder at different parameters like barrel temperature, rotational speed of the screw and different thermoplastic polymer compositions. The tensile strength was measured using a Universal Testing Machine (UTM). The input/output data were optimized using hybrid tools, including genetic algorithm artificial neural networks (GA-ANN) and response surface methodology (GA-RSM). The results obtained from GA-ANN were validated experimentally, demonstrating a maximum tensile strength of 39.8 MPa at 280 °C, 10.001 RPM, 16.783%wt of HDPE, and 8.839%wt of PC. It is observed that the proposed work with GA-ANN model effectively improved the tensile strength by 7.2% in comparison with GA-RSM. This research highlights the potential of the proposed composite material in various applications, emphasizing its cost-effectiveness and improved mechanical properties. [ABSTRACT FROM AUTHOR]

Based on the Cuckoo Search Algorithm (CSA) and the Quantum-Behavior Particle Swarm Optimization (QPSO), this paper propose a hybrid cuckoo quantum-behavior particle swarm optimization (C-QPSO). At first, the QPSO algorithm is modified by the weighted mean best position and the rapid decreasing contraction-expansion coefficient. After that, elite cooperative mechanism, selection mechanism and the mechanism for preventing premature puberty are designed in C-QPSO. To test the performance of the proposed hybrid algorithm, 12 benchmark functions with different dimensions are solved. It is shown from experiments that the algorithm has strong global optimization ability. Furthermore, our presented C-QPSO algorithm is applied to estimate the parameters of a nonlinear Muskingum model. Finally, some numerical results are given to illustrate the effectiveness of C-QPSO algorithm. [ABSTRACT FROM AUTHOR]