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Sediment phenomenon is very important in hydraulic and water resources issues. The existence of this phenomenon causes many problems in water storage. Sediment simulation in rivers helps in controlling sediment as well as reducing damages. In this study, an attempt was made to estimate the suspended sediment load using the corresponding river flow rate in the Zohreh River, Iran using the newest intelligent simulation methods. This study seeks to couple the nonlinear support vector regression (SVR) with crowd intelligence optimization algorithms. For this purpose, support vector regression was optimized using four new crowd optimization algorithms including the ant colony optimizer (ACO), the ant lion optimizer (ALO), the dragonfly algorithm (DA), and the salp swarm algorithm (SSA). Simulation was done in the two phases of train and test. Due to the integration of the nonlinear support vector regression with the optimization algorithms, the model train phase requires more time than usual situations. Therefore, in the current study, taking into account the number of different iterations including 25, 50, 100 and 200 iterations to perform the optimization of the model and tried to find the best optimizer by considering the calculated error and the run time. It was generally found that the SVR model is accurate in estimating the suspended sediment load. Finally, according to the calculated error as well as the run time, the support vector regression model optimized with the salp swarm algorithm with 25 iterations was chosen as the best model. Also, the values of R2, NSE, and RMSE for the best model in the test phase were calculated as 1, 1, and 10.2 tons per day, respectively, and the algorithm run time was 252 s. [ABSTRACT FROM AUTHOR]

Contrast enhancement aims to amplify the visual quality of images by modifying the contrast level because digital images may get distorted by casual acquisition. The article deals with contrast enhancement as an optimization problem and uses the Dragonfly Algorithm (DA) to find the optimal grey-level intensity values. The DA for contrast enhancement uses five control parameters (entropy, number of edges, total intensities of edges, the variance of the probability of occurrence of each grey value, and the number of grey levels) to generate an objective function. An ablation study is also performed to understand how different controlling parameter combinations contribute to determining the optimal solution. The proposed approach considers 24 grey-scale images from the Kodak dataset and metrics as Peak Signal-to-Noise Ratio (PSNR), Visual Information Fidelity (VIF), and Structural Similarity Index Measure (SSIM) to verify the output's performance. The PSNR, VIF, and SSIM values in the experiments are 30.87, 0.7451, and 0.9523, respectively. The experimental observations reveal that the proposed DA-based image contrast enhancement produces high-quality images from its low-contrast counterparts. Comparisons with state-of-art methods ensure the superiority of the proposed algorithm. The Python implementation of the proposed approach is available in this Github repository.

Abstract Sediment phenomenon is very important in hydraulic and water resources issues. The existence of this phenomenon causes many problems in water storage. Sediment simulation in rivers helps in controlling sediment as well as reducing damages. In this study, an attempt was made to estimate the suspended sediment load using the corresponding river flow rate in the Zohreh River, Iran using the newest intelligent simulation methods. This study seeks to couple the nonlinear support vector regression (SVR) with crowd intelligence optimization algorithms. For this purpose, support vector regression was optimized using four new crowd optimization algorithms including the ant colony optimizer (ACO), the ant lion optimizer (ALO), the dragonfly algorithm (DA), and the salp swarm algorithm (SSA). Simulation was done in the two phases of train and test. Due to the integration of the nonlinear support vector regression with the optimization algorithms, the model train phase requires more time than usual situations. Therefore, in the current study, taking into account the number of different iterations including 25, 50, 100 and 200 iterations to perform the optimization of the model and tried to find the best optimizer by considering the calculated error and the run time. It was generally found that the SVR model is accurate in estimating the suspended sediment load. Finally, according to the calculated error as well as the run time, the support vector regression model optimized with the salp swarm algorithm with 25 iterations was chosen as the best model. Also, the values of R2, NSE, and RMSE for the best model in the test phase were calculated as 1, 1, and 10.2 tons per day, respectively, and the algorithm run time was 252 s.

Wind energy forecasting studies play an important role in the search for sustainable energy solutions. However, wind power generation faces an inherent challenge. It is subject to constant fluctuations caused by meteorological conditions. These fluctuations can lead to inconsistencies in voltage and frequency within power grids, resulting in energy instability. To meet this challenge and ensure a reliable energy supply, measures must be taken to reduce the potential instability caused by changing wind conditions. This includes the development of advanced modeling techniques that take into account time-dependent and non-linear changes in wind speed. This type of modeling is crucial for minimizing energy losses and maintaining grid stability. As a result, the urgent need to meet the increasing energy demand while minimizing the environmental impact has triggered the transition to renewable energy sources. In this study, real short-term wind speed data from Osmaniye region were taken as research object. These data were analyzed in detail and the wind speed was estimated by considering the meteorological conditions. Artificial Neural Network was used in the prediction method, and the artificial intelligence algorithm was hybridized with the Dragonfly Algorithm and the coefficients of the artificial intelligence algorithm were trained with the Dragonfly Algorithm. It was used to compare the performance indexes of the prediction models designed with mean percent error, mean absolute percentage error, root mean square error. The performance analysis of Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, Fuzzy and Dragonfly-Based Artificial Neural Network are 2,2512,2,0698,1,7458 and 1,5212, respectively, based on mean absolute percentage error. Root mean square error values are 9,4857,8,2945,7,3285 and 6,4711. Finally, mean absolute errors are 8,2310, 7,5269, 6,2385 and 5,9486, respectively. [ABSTRACT FROM AUTHOR]

Harmonics is the major power quality problem caused by nonlinear devices, leading to malfunction or operational halt of the system. Low-order harmonics increase vibration and heat generation in motors. Therefore, controlling harmonics in the output waveform is the prime target for industrial applications to avoid economic loss. Selective harmonic elimination pulse width modulation (SHEPWM) is one of the techniques used for eliminating or minimizing selected harmonics in the output voltage waveform. This paper utilizes the Newton-Raphson method and Dragonfly Algorithms to calculate optimum switching angles for a Cascaded H-bridge Multilevel inverter (CHBMLI). The algorithms use non-linear equations to calculate the Switching Angles of MLI. The Dragonfly algorithm requires several iterations to reach an optimum solution. For complex problems, this algorithm becomes computationally expensive and time-consuming. A lookup table addresses the limitation by offline training an Artificial Neural Network (ANN) to generate the optimum switching angle for a given modulation index. Neural Fitting Tool in MATLAB software is used to train the ANN model. The simulation is performed using MATLAB SIMULINK software for both 5-level and 7-level CHBMLI configuration. The Dragonfly algorithm-based ANN achieves THD 8.84% when the modulation index (M) equals 0.8 for a 7-level inverter and THD 14.91% for a 5-level inverter and effectively minimizes third and fifth-order harmonics.

In order to optimize the calibration set for near-infrared modeling of the protein content in wheat flour, the binary dragonfly algorithm (BDA) was used to select representative samples from the primary calibration set divided by the traditional Kennard/Stone (K/S) method. Based on the representative samples, a partial least square regression (PLSR) model for estimating the protein content in wheat flour was established, and the prediction set was employed to evaluate the stability and prediction performance of the model. The results indicated that an optimal calibration set with 30 samples was selected finally by BDA, and the proposed model exhibited a coefficient of determination of prediction (Rp2) of 0.956 4 and a root mean square errors of prediction (RMSEP) of 0.278 1, which increased by 1.87% and decreased by 15.57% compared with those (0.938 8 and 0.329 4) from K/S partition of 100 primary calibration sets, respectively. The average number of calibration sets selected from 10 BDA experiments was 30.2, and the protein content of wheat flour was predicted better by the 10 models developed than that obtained based on the primary calibration set. Therefore, BDA can select a small number of representative calibration set samples based on which a PLSR model with good robustness and high prediction accuracy for the protein content of wheat flour can be established. The proposed method can provide an efficient tool for calibration set selection in near-infrared spectroscopic analysis of the quality of wheat flour.

Pure zinc oxide (ZnO) thin films, along with manganese (Mn) doped counterparts, were produced using rapid thermal evaporation technique on ordinary glass substrates. Postannealing treatments resulted in the formation of hexagonal wurtzite structures in the deposited layers. The Raman results unveiled the presence of A1(LO) and LVM vibration modes in each sample that were doped. Interestingly, the undoped sample lacked the LVM mode while showcasing the emergence of LA + TO combined phonons. Employing a novel approach reliant on the Dragonfly Algorithm, optical parameters were extracted, revealing a drop in the bandgap energy of the films from 3.95 eV to 3.79 eV. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science 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.)

In view of the problems that the dragonfly algorithm has, such as that it easily falls into the local optimal solution and the optimization accuracy is low, an improved Dragonfly Algorithm (IDA) is proposed and applied to Otsu multi-threshold image segmentation. Firstly, an elite-opposition-based learning optimization is utilized to enhance the diversity of the initial population of dragonflies, laying the foundation for subsequent algorithm iterations. Secondly, an enhanced sine cosine strategy is introduced to prevent the algorithm from falling into local optima, thereby improving its ability to escape from local optima. Then, an adaptive t-distribution strategy is incorporated to enhance the balance between global exploration and local search, thereby improving the convergence speed of the algorithm. To evaluate the performance of this algorithm, we use eight international benchmark functions to test the performance of the IDA algorithm and compare it with the sparrow search algorithm (SSA), sine cosine algorithm (SCA) and dragonfly algorithm (DA). The experiments show that the algorithm performs better in terms of convergence speed and accuracy. At the same time, the Otsu method is employed to determine the optimal threshold, a series of experiments are carried out on six images provided by Berkeley University, and the results are compared with the other three algorithms. From the experimental results, the peak signal-to-noise ratio index (PSNR) and structural similarity index (SSIM) based on the IDA algorithm method are better than other optimization algorithms. The experimental results indicate that the application of Otsu multi-threshold segmentation based on the IDA algorithm is potential and meaningful. [ABSTRACT FROM AUTHOR]

Navigation poses a significant challenge for autonomous vehicles, prompting the exploration of various bio-inspired artificial intelligence techniques to address issues related to path generation, obstacle avoidance, and optimal path planning. Numerous studies have delved into bio-inspired approaches to navigate and overcome obstacles. In this paper, we introduce the dragonfly algorithm (DA), a novel bio-inspired meta-heuristic optimization technique to autonomously set goals, detect obstacles, and minimize human intervention. To enhance efficacy in unstructured environments, we propose and analyze the dragonfly–fuzzy hybrid algorithm, leveraging the strengths of both approaches. This hybrid controller amalgamates diverse features from different methods into a unified framework, offering a multifaceted solution. Through a comparative analysis of simulation and experimental results under varied environmental conditions, the hybrid dragonfly–fuzzy controller demonstrates superior performance in terms of time and path optimization compared to individual algorithms and traditional controllers. This research aims to contribute to the advancement of autonomous vehicle navigation through the innovative integration of bio-inspired meta-heuristic optimization techniques. [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]

Navigation poses a significant challenge for autonomous vehicles, prompting the exploration of various bio-inspired artificial intelligence techniques to address issues related to path generation, obstacle avoidance, and optimal path planning. Numerous studies have delved into bio-inspired approaches to navigate and overcome obstacles. In this paper, we introduce the dragonfly algorithm (DA), a novel bio-inspired meta-heuristic optimization technique to autonomously set goals, detect obstacles, and minimize human intervention. To enhance efficacy in unstructured environments, we propose and analyze the dragonfly–fuzzy hybrid algorithm, leveraging the strengths of both approaches. This hybrid controller amalgamates diverse features from different methods into a unified framework, offering a multifaceted solution. Through a comparative analysis of simulation and experimental results under varied environmental conditions, the hybrid dragonfly–fuzzy controller demonstrates superior performance in terms of time and path optimization compared to individual algorithms and traditional controllers. This research aims to contribute to the advancement of autonomous vehicle navigation through the innovative integration of bio-inspired meta-heuristic optimization techniques.

Objective: In order to improve the precision of apple grade judgment model, the method of apple grade judgment was established. Methods: A decision model of apple rank based on multi-information fusion and dragonfly algorithm was proposed. Firstly, the HSV color feature, LBP texture feature and HOG shape feature of apple image were extracted by pre-processing such as data enhancement, normalization, Gauss filter and grayscale. Secondly, the performance of DBN model was affected by the selection of parameters, the network parameters of DBN model were optimized by DA algorithm, and a multi-information fusion and DA-DBN model for determining apple rank wws proposed. Results: Compared with GA-DBN, PSO-DBN, GWO-DBN and DBN, the model based on DA-DBN had the highest precision. Conclusion: The DBN model is optimized by dragonfly algorithm which can effectively improve the accuracy of apple rank determination model, which provides a new method for apple rank determination.

Brain-Computer Interfaces (BCIs) facilitate the translation of brain activity into actionable commands and act as a crucial link between the human brain and the external environment. Electroencephalography (EEG)-based BCIs, which focus on motor imagery, have emerged as an important area of study in this domain. They are used in neurorehabilitation, neuroprosthetics, and gaming, among other applications. Optimal Deep Learning-Based Recognition for EEG Signal Motor Imagery (ODLR-EEGSM) is a novel approach presented in this article that aims to improve the recognition of motor imagery from EEG signals. The proposed method includes several crucial stages to improve the precision and effectiveness of EEG-based motor imagery recognition. The pre-processing phase starts with the Variation Mode Decomposition (VMD) technique, which is used to improve EEG signals. The EEG signals are decomposed into different oscillatory modes by VMD, laying the groundwork for subsequent feature extraction. Feature extraction is a crucial component of the ODLR-EEGSM method. In this study, we use Stacked Sparse Auto Encoder (SSAE) models to identify significant patterns in the pre-processed EEG data. Our approach is based on the classification model using Deep Wavelet Neural Network (DWNN) optimized with Chaotic Dragonfly Algorithm (CDFA). CDFA optimizes the weight and bias values of the DWNN, significantly improving the classification accuracy of motor imagery. To evaluate the efficacy of the ODLR-EEGSM method, we use benchmark datasets to perform rigorous performance validation. The results show that our approach outperforms current methods in the classification of EEG motor imagery, confirming its promising performance. This study has the potential to make brain-computer interface applications in various fields more accurate and efficient, and pave the way for brain-controlled interactions with external systems and devices.

Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which combines efficient operators from the dragonfly algorithm, such as the movement of dragonflies in flower pollination for exploration, with the self-cleaning feature of water on flower leaves known as the lotus effect, for extraction and local search operations. The authors compared this method to other improved versions of the dragonfly algorithm using standard benchmark functions, and it outperformed all other methods according to Fredman's test on 29 benchmark functions. The article also highlights the practical application of LEA in reducing energy consumption in IoT nodes through clustering, resulting in increased packet delivery ratio and network lifetime. Additionally, the performance of the proposed method was tested on real-world problems with multiple constraints, such as the welded beam design optimization problem and the speed-reducer problem applied in a gearbox, and the results showed that LEA performs better than other methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

The development of the information industry is a cross-era impact for any industry, especially under the influence of the database, and the systematic analysis of the data in the industry can improve the accuracy of the output vector and data analysis. With the increase of data volume, for the difficulty of data processing increased significantly, the number of college students growing rapidly in recent years, to the accurate analysis of teachers and students need to take information technology, improve the accuracy of the operation, in the analysis of students and teachers through the system processing education data, the results can be applied to the education management. Based on the above content, on the basis of the dragonfly algorithm, through the improvement of the algorithm, the quality of rehabilitation physiology ideological teaching evaluation, first constructed the course ideological teaching quality evaluation system, then on the basis of determining the evaluation index, the teaching quality evaluation score, in order to through the study of rehabilitation physiology ideological teaching quality to provide reference. [ABSTRACT FROM AUTHOR]

The deterministic directional overcurrent relay (DOCR) coordination approaches find limitation in providing fast and reliable protection to today's distribution networks due to growing integration of distributed generations (DGs). In this paper, an adaptive DOCR protection coordination scheme is proposed using an efficient optimization algorithm called the dragonfly algorithm (DA). The main inspiration of the proposed DA optimization technique originates from static and dynamic swarming behaviours of dragonflies. In this approach, the dominant changes in the network topologies are identified by monitoring the status of the circuit breakers connected at the terminals of the DGs and other main power components. When any dominant changes in the network topologies or operating modes are identified, the fault current for that particular condition is calculated and the new optimal DOCRs settings for the prevailing condition are obtained from the substation central computer in online mode. The comparative results with the existing approaches justify the superiority of the proposed scheme in achieving the minimum overall relay operating time and maintaining the coordination between the primary and backup relay pairs. In the proposed adaptive protection scheme, the average percentage reduction in the overall operating times of DOCRs in the 6-bus and the IEEE 14-bus test systems with different levels DG penetration is found to be 49.7% and 15%, respectively, compared to the existing approaches. [ABSTRACT FROM AUTHOR]

This study introduces novel stochastic distributed cooperative control (SDCC) in the context of island microgrids (MGs). A proportional resonant (PR) controller and virtual impedance droop control in stationary reference frames are employed in cooperation with distributed averaging secondary control optimized by the dragonfly algorithm (DA). The suggested approach demonstrates the capability to achieve mean-square synchronization for the voltage and frequency restoration of distributed generators (DGs) to ensure efficient active power sharing. Therefore, a sparse communication network has been used to avoid data congestion and reduce the need for extensive communication and information exchange. The proposed system offers an instinctive compromise between voltage regulation and reactive power sharing. A conventional centralized secondary control with PR droop control is simulated for performance evaluation and comparison purposes. In this study, empirical evidence is demonstrated to support the MG's ability to confront communication failure and its ability to work reliably during plug-and-play operations. [ABSTRACT FROM AUTHOR]

Concerns surrounding the environment along with inadequate energy supply and high cost of same are responsible for pollution, high energy demand, unpredictable and uneconomical power generation. These have contributed to the widespread agreement that sustainable renewable energy sources (RES) must be developed, particularly in isolated villages where expanding the grid may be challenging and financially unviable for power corporations. As a result, in order to effectively and cheaply utilize the plentiful renewable energy resources, an optimal sizing approach is required. This study is aimed at investigating the economic performance of the hybrid system of a stand-alone Small Hydropower/PV/Diesel generator with battery electricity production. The cost function was minimized using Dragonfly Algorithm (DA) in order to minimize the Cost of Energy (COE) generation. The decision variables are the number of small hydro turbines (NSHP), number of solar panels (NPV), number of batteries (NBATT) and the capacity of Diesel generator (PDG). The developed method is applied to a typical Kiri village in Shelleng Local Government area of Adamawa State. For uniformity, the hourly solar irradiance data were created by converting the monthly average solar irradiance data. A dragonfly optimization technique was utilized to reach an optimum solution for the hybrid system. The result obtained showed that the system components: small Hydropower, solar PV and Diesel generator were able to generate electrical power of 5,783,600 W, 56,259 W and 5.2941e-05 W respectively to meet the energy demand. Results obtained from the developed scheme were compared with those obtained when TORSCHE algorithm was used in optimizing the hybrid system. It was observed that a total energy cost of $5,224,500 was obtained for the developed technique while $5,839,600 was obtained as the total cost for the TORSCHE model. This showed that the developed scheme outperformed the system output from the TORSCHE algorithm in terms of cost of energy by 89.46%. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of an extensive number of low-power sensor nodes to gather information from their environment and monitor physical activities. This makes node localization a crucial aspect in most WSN applications since measurement data is worthless unless the location from where the data is acquired is known precisely. The majority of localization solutions rely on anchor nodes for estimating the node locations with different localization accuracy, complexity, and hence different applicability. But, the cost and complexity in the localization of large-scale WSNs are not significantly reduced. In this paper, a novel Hybridized Dragonfly and Jaya Optimization technique (HyDAJ) is introduced for improving localization accuracy and performance of mobile WSNs. The proposed hybrid technique combines the advantages of Dragonfly algorithm and Jaya algorithm to localize the sensor nodes in a more efficient way and overcomes the limitations of the original algorithm. The hybrid algorithm verifies that all target nodes are precisely localized with higher accuracy. Simulation results reveal that HyDAJ outperforms existing methods under multiple metrics including localization efficiency, mean localization error, computation time, and energy consumption. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) providing additional on-demand communication and computing services have become a promising technology. However, the limited energy supply of UAVs, which constrains their service duration, has emerged as an obstacle in UAV-enabled networks. In this context, a novel task offloading framework is proposed in UAV-enabled mobile edge computing (MEC) networks. Specifically, heterogeneous UAVs with different communication and computing capabilities are considered and the energy consumption of UAVs is minimized via jointly optimizing user association and UAV deployment. The optimal transport theory is introduced to analyze the user association sub-problem, and the UAV deployment for each sub-region is determined by a dragonfly algorithm (DA). Simulation results show that the energy consumption performance is significantly improved by the proposed algorithm. [ABSTRACT FROM AUTHOR]

The predictability of the adsorption capacity of the multicomponent adsorption system was modelled using Support Vector Machine (SVM). Two SVM models were built and compared. In the first model, the SVM method was used with an already built-in optimisation algorithm. However, in the second model, the SVM method was used by means of a very recent and efficient optimisation algorithm: the Dragonfly Algorithm (DA). The models’ accuracy was evaluated by three well-established statistical metrics (root mean squared error RMSE, determination coefficient R2, and correlation coefficient R). The used data were collected from previous experimental papers published in literature containing all kinds of pollutants, such as heavy metal ions, dyes, and organic compounds, and different natural/synthetic adsorbents. The dataset contained five important variables with 1023 points; the variables were divided into four inputs (molecular weight, equilibrium concentrations of adsorbate, specific area of adsorbent, and temperature), and one output (adsorption capacity at equilibrium). The data were divided using the holdout function into two subsets (80 % for training set, and 20 % for test set). The programming stage was carried out using MATLAB software. The results showed that the optimised DA-SVM model with RBF-Gaussian kernel function had good ability for global search combined with high prediction accuracy, with R2 = 0.997, R = 0.998, and RMSE = 2.539. The obtained model can be used to predict the efficiency of the adsorption system, and provides a tool for process optimisation responding to changes in operating conditions. A new graphical user interface (GUI) was developed with MATLAB GUI to estimate accurately the desired responses by using the best DA-SVM model.

In view of the problems that the dragonfly algorithm has, such as that it easily falls into the local optimal solution and the optimization accuracy is low, an improved Dragonfly Algorithm (IDA) is proposed and applied to Otsu multi-threshold image segmentation. Firstly, an elite-opposition-based learning optimization is utilized to enhance the diversity of the initial population of dragonflies, laying the foundation for subsequent algorithm iterations. Secondly, an enhanced sine cosine strategy is introduced to prevent the algorithm from falling into local optima, thereby improving its ability to escape from local optima. Then, an adaptive t-distribution strategy is incorporated to enhance the balance between global exploration and local search, thereby improving the convergence speed of the algorithm. To evaluate the performance of this algorithm, we use eight international benchmark functions to test the performance of the IDA algorithm and compare it with the sparrow search algorithm (SSA), sine cosine algorithm (SCA) and dragonfly algorithm (DA). The experiments show that the algorithm performs better in terms of convergence speed and accuracy. At the same time, the Otsu method is employed to determine the optimal threshold, a series of experiments are carried out on six images provided by Berkeley University, and the results are compared with the other three algorithms. From the experimental results, the peak signal-to-noise ratio index (PSNR) and structural similarity index (SSIM) based on the IDA algorithm method are better than other optimization algorithms. The experimental results indicate that the application of Otsu multi-threshold segmentation based on the IDA algorithm is potential and meaningful.

In this article, a method for reconfiguring the distribution network with the presence of production sources to minimize energy losses is presented. Also, besides the reconfiguration issue, the Super distribution tap transformers and the power factor of resources in the network will have their optimal values based on the amount of energy consumption as factors affecting the number of losses. According to the stated parameters, the optimization problem in this article is a non-linear problem with a large search space, for which the dragonfly optimization algorithm was used. To make the results realistic, all types of loads, including commercial, residential and office, have been considered in 24 h and two seasons of the year, cold and hot. The program was prepared with the help of MATLAB software on a sample network of 16 buses, and its applications and results have been evaluated. The obtained results show that determining the optimal reconfiguration is very effective in reducing network losses because, with this method, the amount of losses and unsupplied energy of network subscribers has been reduced significantly. Also, by coordinating the reconfiguration with tap transformers and the power factor of distributed generation resources, the loss reduction rate can be made more effective. The results obtained are compared with the genetic algorithm and the results show the superiority of the dragonfly optimization method.

As primary components of solar power applications, photovoltaic cells have promising development prospects. Due to the characteristics of PV cells, the identification of parameters for circuit models has become a research focus. Among the various methods of parameter estimations, metaheuristic algorithms have attracted significant interest. In this paper, a hybrid-strategy-improved dragonfly algorithm (HIDA) is proposed to meet the demand for high parameter-identification accuracy. Tent chaotic mapping generates the initial position of individual dragonflies and aids in increasing the population diversity. Individual dragonflies can adapt their updated positions to various scenarios using the adjacent position decision approach. The whale optimization algorithm fusion strategy incorporates the spiral bubble-net attack mechanism into the dragonfly algorithm to improve the optimization-seeking precision. Moreover, the optimal position perturbation strategy reduces the frequency of the HIDA falling into local optima from the perspective of an optimal solution. The effectiveness of the HIDA was evaluated using function test experiments and engineering application experiments. Seven unimodal and five multimodal benchmark test functions in 50, 120, and 200 dimensions were used for the function test experiments, while five CEC2013 functions and seven CEC2014 functions were also selected for the experiments. In the engineering application experiments, the HIDA was applied to the single-diode model (SDM), engineering model, double-diode model (DDM), triple-diode model (TDM), and STM-40/36 parameter identification, as well as to the solution of seven classical engineering problems. The experimental results all verify the good performance of the HIDA with high stability, a wide application range, and high accuracy. [ABSTRACT FROM AUTHOR]

With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office 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.)

Grey Wolf optimizer (GWO) has been used in several fields of research. The main advantages of this algorithm are its simplicity, little controlling parameter, and adaptive exploratory behavior. However, similar to other metaheuristic algorithms, the GWO algorithm has several limitations. The main drawback of the GWO algorithm is its low capability to handle a multimodal search landscape. This drawback occurs because the alpha, beta, and gamma wolves tend to converge to the same solution. This paper presents HDGM – a novel hybrid optimization model of dragonfly algorithm and grey wolf optimizer, aiming to overcome the disadvantages of GWO algorithm. Dragonfly algorithm (DA) is combined with GWO in this study because DA has superior exploration ability, which allows it to search in promising areas in the search space. To verify the solution quality and performance of the HDGM algorithm, we used twenty-three test functions to compare the proposed model's performance with that of the GWO, DA, particle swam optimization (PSO) and ant lion optimization (ALO). The results show that the hybrid algorithm provides more competitive results than the other variants in terms of solution quality, stability, and capacity to discover the global optimum. [ABSTRACT FROM AUTHOR]

Bayesian network structure learning is one of the current research hotspots in fields such as statistics and machine learning. Although it has great potential and application prospects, when there are too many variables, this type of algorithm will not be able to accurately and efficiently provide the optimal solution. In response to this issue, this study improved the dragonfly swarm optimization algorithm and solved the problem of variable type conflicts through binary discretization, applying it to the Bayesian network structure learning algorithm. According to the algorithm testing results, when the sample size is 1000 and the missing rate is 30%, the Bayesian Information Criterion (BIC) of the proposed algorithm is -7896. Under the same missing rate, when the sample size is 2000, the proposed algorithm BIC is -15114. Their BIC scores are superior to the greedy search algorithm and the sine cosine algorithm used for comparison. Overall, the proposed algorithm has better convergence ability and BIC rating. But its disadvantage is that the running time has not been optimized, and it has no advantages compared to traditional algorithms. The proposed algorithm provides a promising development direction for the field of Bayesian network structure learning.

A combined ultra-short-term wind power prediction strategy with high robustness based on least squares support vector machine (LSSVM) has been proposed, in order to solve the wind abandonment caused by wind power randomness and realize efficient hydrogen production under wide power fluctuation. Firstly, the original wind power data is decomposed into sub-modes with different bandwidth by variational modal decomposition (VMD), which reduces the influence of random noise and mode mixing significantly. Then dragonfly algorithm (DA) is introduced to optimize LSSVM kernel function and the combined ultra-short-term wind power prediction strategy which meets the time resolution and accuracy requirements of electrolytic cell control has been established finally. This model is validated by a wind power hydrogen production demonstration project output in the middle east of China. The superior prediction accuracy for high volatility wind power data is verified and the algorithm provides theoretical basis to improve the control of wind power hydrogen production system.

The Spiking Neural Network (SNN) model is a third-generation neural network model that uses spike pulse trains to detect and classify electrical problems in the electrical equipment under consideration. To diagnose the thermal issue in the early stages, it is required to evaluate and monitor the electrical components. The detection method in Infrared Thermography (IRT) is hybrid optimization, which is a Dragonfly Algorithm (DA)–Ant Colony Optimization (ACO) technique that produces a higher exploration and exploitation rate while moving toward an optimal solution with a higher convergence rate. The innovative hybrid DA–ACO method used in this study aims to find the best weights for the SNN model while also extracting the most useful characteristics for defect detection in electrical equipment. To improve classification accuracy, the optimum features picked using the created hybrid DA–ACO are provided to the SNN model. Based on conventional and other optimization methods, the proposed method provided superior results from the execution results. [ABSTRACT FROM AUTHOR]

The predictability of the adsorption capacity of the multicomponent adsorption system was modelled using Support Vector Machine (SVM). Two SVM models were built and compared. In the first model, the SVM method was used with an already built-in optimisation algorithm. However, in the second model, the SVM method was used by means of a very recent and efficient optimisation algorithm: the Dragonfly Algorithm (DA). The models’ accuracy was evaluated by three well-established statistical metrics (root mean squared error RMSE, determination coefficient R², and correlation coefficient R). The used data were collected from previous experimental papers published in literature containing all kinds of pollutants, such as heavy metal ions, dyes, and organic compounds, and different natural/ synthetic adsorbents. The dataset contained five important variables with 1023 points; the variables were divided into four inputs (molecular weight, equilibrium concentrations of adsorbate, specific area of adsorbent, and temperature), and one output (adsorption capacity at equilibrium). The data were divided using the holdout function into two subsets (80 % for training set, and 20 % for test set). The programming stage was carried out using MATLAB software. The results showed that the optimised DA-SVM model with RBF-Gaussian kernel function had good ability for global search combined with high prediction accuracy, with R² = 0.997, R = 0.998, and RMSE = 2.539. The obtained model can be used to predict the efficiency of the adsorption system, and provides a tool for process optimisation responding to changes in operating conditions. A new graphical user interface (GUI) was developed with MATLAB GUI to estimate accurately the desired responses by using the best DA-SVM model. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) system is composed of several numbers of sensor nodes and systems, which are wirelessly interlinked to the internet. Generally, big data is the storage of a huge amount of information, which causes the classification process to be very challenging. Numerous big data classification approaches are implemented, but the computational time and secure handling of information are the major problems. The aim of the study is the development of big data approach in Internet of Things (IoT) healthcare application. Hence, this paper presents the proposed Dragonfly Rider Competitive Swarm Optimization-based Deep Residual Network (DRCSO-based DRN) for big data classification in IoT. First, the IoT nodes are simulated, and the heart disease patient data are collected through sensors. The routing is done using the Multi-objective Fractional Gravitational Search Algorithm (Multi-objective FGSA). In the Base Station (BS), the big data classification is done. Here, the classification is done using MapReduce (MR) framework, which includes two phases, like mapper and the reducer phase. The input data is initially fed to the mapper phase in the map-reduce (MR) framework. In the mapper phase, feature selection is carried out based on Dragonfly Rider Optimization Algorithm (DROA) in order to select the appropriate features for further processing. The DROA is modeled through merging Dragonfly Algorithm (DA) and Rider Optimization Algorithm (ROA). In the reducer phase, the classification is performed using DRN, which is trained by the developed DRCSO algorithm. The DRCSO is modeled by incorporating DA, ROA and Competitive Swarm Optimization (CSO). In addition, the performance of the developed method is outperformed than the existing approaches such as Linguistic Fuzzy Rules with Canopy Mapreduce (LFR-CM) + Fuzzy classifier, Machine learning-dependent k-nearest neighbors (FML-KNN), MapReduce-Fuzzy Integral-dependent Ensemble Learning Model+Single hidden layer feedforward neural network (MR-FI-ELM + SLFN) and DROA-recurrent neural network (RNN) based on the accuracy, average residual energy and throughput with the value of 0.929, 0.086 J and 86.585. The proposed method is used to manage and derive meaningful information from the patient's medical records, medical examinations results and hospital records. [ABSTRACT FROM AUTHOR]

A number of swarm intelligence algorithms have been proposed during the last years. Most of them are suitable for the solution of continuous optimization problems. One of them is the Dragonfly Algorithm that has been proved very efficient in the problems in which it has been applied. However, few of the newly proposed algorithms have been used for the solution of a routing problem. In this paper, a new hybridized version of the Dragonfly Algorithm with the Combinatorial Expanding Neighborhood Topology is proposed and analyzed in details. The proposed Combinatorial Expanding Neighborhood Topology Dragonfly Algorithm is an algorithm that combines a very powerful swarm intelligence algorithm, the Dragonfly algorithm, with a very effective procedure, the Combinatorial Expanding Neighborhood Topology. This algorithm was used for solving a well known routing problem, the Vehicle Routing Problem with Stochastic Demands. The algorithm was tested in 40 benchmark instances from the literature and gave, in some of them new, best solutions. It was, also, compared with 10 other swarm intelligence algorithms from the literature proving its effectiveness, as it was ranked in the first place among all the algorithms.

With the traffic congestion problem deteriorating, people increasingly choose urban rail transit (URT) to travel. Although URT alleviates traffic congestion, the long-term operation of a large number of trains leads to huge energy consumption. In order to adapt the major social development concept of "Low carbon", a multi-train energy-saving control collaborative optimization method is proposed in this paper. First, the composition of single train operating conditions is determined by the conversion of operating conditions between stations and the force changes under the premise of ensuring safe and on-time train operation. A single-train energy consumption calculation combinatorial optimization model with the dual control objectives of reducing passengers' average waiting time as well as train traction energy consumption is established. The energy saving control strategy of a single train is investigated by ARMA-Radial Basis Function Neural Network (ARMA-RBFNN) and Genetic Algorithm (GA). Next, the queuing theory is introduced to analyze the variation in passenger waiting time for multiple trains at different arrival intervals. A Deep Reinforcement Learning (DRL) algorithm is designed to obtain the correlation among passenger waiting time, arrival interval and train stopping time. The optimization objective is to minimize the multi-train traction energy consumption and the average passenger waiting time while considering conditions such as train operating safety interval, speed limit, multiple operating state and single train energy-saving models, etc. Then, a multi-train cooperative energy-saving control model is proposed based on the Dragonfly Algorithm (DA). Finally, a case study of Beijing Metro Line 4 is conducted to illustrate the effectiveness of the proposed method. The results demonstrate that the total traction energy consumption and passenger waiting time are reduced by 3.1% and 5 s, respectively, compared with the method of independently optimizing the single-train control strategy. The findings can aid in the development of energy-saving strategies and also provide a basis for energy-saving operation control of multiple trains. [ABSTRACT FROM AUTHOR]

Population growth, industrial development as well as limited fossil fuel resources have motivated the study of other energies, especially nuclear energy. Small modular reactors have been introduced as an efficient energy source due to their greater safety, easy transport, electricity generation and water desalination, even in remote areas. Optimization in the nuclear fuel management to improve performance and save energy leads to cost-effective design with higher efficiency and better safety. In this research, core loading pattern optimization of system-integrated modular advanced reactor (SMART) has been considered using the new dragonfly algorithm. In addition to the selected algorithm, the efficiency of optimizing loading pattern also depends on the definition of the objective function. Two-objective functions including flattening the power distribution and maximizing the effective multiplication factor are considered. Simulations of the reactor fuel assemblies and reactor core were performed by DRAGON lattice calculation and PARCS core calculation codes, respectively. According to the final results, the cycle length and effective multiplication factor are increased for 185 days and 582 pcm, respectively. Also the fitness function is decreased from 0.905931 to 0.194527.

This study introduces novel stochastic distributed cooperative control (SDCC) in the context of island microgrids (MGs). A proportional resonant (PR) controller and virtual impedance droop control in stationary reference frames are employed in cooperation with distributed averaging secondary control optimized by the dragonfly algorithm (DA). The suggested approach demonstrates the capability to achieve mean-square synchronization for the voltage and frequency restoration of distributed generators (DGs) to ensure efficient active power sharing. Therefore, a sparse communication network has been used to avoid data congestion and reduce the need for extensive communication and information exchange. The proposed system offers an instinctive compromise between voltage regulation and reactive power sharing. A conventional centralized secondary control with PR droop control is simulated for performance evaluation and comparison purposes. In this study, empirical evidence is demonstrated to support the MG’s ability to confront communication failure and its ability to work reliably during plug-and-play operations.

Optimization problems are usually solved using heuristic algorithms such as swarm intelligence algorithms owing to their ability of providing near-optimal solutions in a feasible amount of time. An example of an optimization problem is the training of artificial neural networks to obtain the most optimal connection weights. Artificial Neural Network (ANN), being the most prominent machine learning algorithm, has a multitude of applications in a myriad of areas. Recently, the use of ANNs has risen exponentially owing to its effective ability of making conclusions based on certain inputs. This ability is primarily achieved during the training phase of the ANN, which is a vital process prior to being able to use the ANN. Gradient descent-based algorithms, which are usually used for the training process, often encounter the problem of local optima, thus being unable to obtain the optimal connection weights of the ANN. Metaheuristic algorithms, including swarm intelligence algorithms, have been found to be a better alternative to train ANNs. The Dragonfly Algorithm (DA) is a swarm intelligence algorithm that has been found to be more effective than multiple swarm intelligence algorithms. However, despite having a good performance, it still suffers from low exploitation. In this paper, we propose to further improve the performance of DA by using hill climbing as a local search technique so as to enhance its low exploitation. The optimized DA algorithm is then used for training artificial neural networks which are employed for classification problems. Based on the experimental results, the optimized DA algorithm has higher effectiveness than the original DA and some other swarm intelligence algorithms as the ANNs trained by the optimized DA have a lower root mean squared error and a higher classification accuracy.

The aim of this work was to model the deep bed and cake filtration process using an alternative approach based on a fractional calculus (FC). The considered experimental data in this study were extracted from published studies. The data used in FC models contained two inputs - initial concentration and time, and volume of filtrate as an output. The FC kinetic constants were tuned by fitting the experimental and predicted data through Dragonfly algorithm implemented in MATLAB programming software. The performance of the developed models was assessed using different metrics by comparing the experimental against the predicted data. The developed fractional model with nth order presented very acceptable metrics in comparison to the pseudo-nth order and displayed strong potential for estimating the volume of filtrate.

This work aimed to model the kinetics of cumulative drug release from formulations based on encapsulation by biodegradable polycaprolactone and polyvinylpyrrolidone polymers. Different ratios of the polymerswere prepared by a solvent evaporation method using Span 20 and Span 80 as surfactants. The cumulative drug release was estimated depending on the formulation component and time. Four models: hybrid model of support vector machine and dragonfly algorithm (DA-SVM), partial least squares (PLS) model, multiple linear regression (MLR) model, and ordinary least squared (OLS) model, were developed and compared. The statistical analysis proved there were no issues in variable inputs. The results showed that the DA-SVM model gave a better result where a determination coefficient was close to one and RMSE error close to zero. A graphical interface was built to calculate the cumulative drug release.

Software testing is important for ensuring the reliability of software systems. In software testing, effective test case generation is essential as an alternative to exhaustive testing. For improving the software testing technology, the t-way testing technique combined with metaheuristic algorithm has been great to analyze a large number of combinations for getting optimal solutions. However, most of the existing t-way strategies consider test case weights while generating test suites. Priority of test cases hasn’t been fully considered in previous works, but in practice, it’s frequently necessary to distinguish between high-priority and low-priority test cases. Therefore, the significance of test case prioritization is quite high. For this reason, this paper has proposed a t-way strategy that implements an adaptive Dragonfly Algorithm (DA) to construct prioritized t-way test suites. Both test case weight and test case priority have equal significance during test suite generation in this strategy. We have designed and implemented a Bi-objective Dragonfly Algorithm (BDA) for prioritized t-way test suite generation, and the two objectives are test case weight and test case priority. The test results demonstrate that BDA performs competitively against existing t-way strategies in terms of test suite size, and in addition, BDA generates prioritized test suites.

Optimization problems are prevalent in almost all areas and hence optimization algorithms are crucial for a myriad of real-world applications. Deterministic optimization algorithms tend to be computationally costly and time-consuming. Hence, heuristic and metaheuristic algorithms are more favoured as they provide near-optimal solutions in an acceptable amount of time. Swarm intelligence algorithms are being increasingly used for optimization problems owing to their simplicity and good performance. The Dragonfly Algorithm (DA) is one which is inspired by the swarming behaviours of dragonflies, and it has been proven to have a superior performance than other algorithms in multiple applications. Hence, it is worth considering its application to the traveling salesman problem which is a predominant discrete optimization problem. The original DA is only suitable for solving continuous optimization problems and, although there is a binary version of the algorithm, it is not easily adapted for solving discrete optimization problems like TSP. We have previously proposed a discrete adapted DA algorithm suitable for TSP. However, it has low effectiveness, and it has not been used for large TSP problems. In this paper, we propose an optimized discrete adapted DA by using the steepest ascent hill climbing algorithm as a local search. The algorithm is applied to a TSP problem modelling a package delivery system in the Kuala Lumpur area and to benchmark TSP problems, and it is found to have a higher effectiveness than the discrete adapted DA and some other swarm intelligence algorithms. It also has a higher efficiency than the discrete adapted DA. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) are the integral part of Internet of Things (IoT) applications. They consist of many sensor nodes energized by batteries. These batteries turn out to be dead after the usage of specific duration. The lifespan of the sensor devices depends on its battery life. So it is inevitable that the battery life is to be maintained optimally. Intensifying the data spread in a well-regulated mode helps to increase the lifespan of the sensor devices. Clustering is one of the techniques which help in enhancing the network lifetime of WSN. The optimal selection of cluster heads is a vital parameter. In view of this, a novel fusion cluster head selection algorithm is offered by combining the advantages of LEACH protocol and dragonfly algorithm. The swarm intelligent behavior of dragonfly is deployed to group the sensors and to choose the optimal route to reach the base station. The new algorithm is tested on diverse scenarios of WSNs, by varying the maximum runs and the total sum of sensor nodes. The performance appraisal is made with reference to number of live nodes, dead nodes, and packet transmission ratio. The proposed energy efficient, novel bio-inspired algorithm (NBA) is compared with other existing state-of-the-art algorithms which demonstrate that the new method is effective in broadening network life expectancy satisfactorily. [ABSTRACT FROM AUTHOR]

With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings.

This paper introduces an economic disposal and lot-sizing problem (EDLSP) for perishable inventories, it is a new version of an economic lot-sizing problem (ELSP). The classic ELSP aims to determine the production-inventory strategy such that the sum of holding, production, and setup costs is minimized. In the proposed EDLSP, the optimal disposal strategy, as well as the optimal production-inventory strategy, is determined for perishable inventories. Disposal strategy determines how many and when corrupt items should be removed from the stock. In the new model, the unit holding cost depends on the volume of the corrupt inventories that exist in the stock. To closely reflect reality, the model considers fixed and variable disposal costs that represent the costs of removing the corrupt inventories from the warehouse. As a generalized form of the classic lot-sizing problem with the setup cost, the EDLSP is an NP-complete problem. Therefore, two meta-heuristic algorithms, namely Binary Dragonfly Algorithm (BDA) and Genetic Algorithm (GA), are proposed to solve the model. The Taguchi method is also applied to calibrate the meta-heuristics. Then, the performance of these algorithms is evaluated on a set of test problems. The obtained results indicate that both meta-heuristics have a good performance in solving small-sized problems. However, the GA has a better performance than the BDA for medium- and large-sized problems. Furthermore, a sensitivity analysis of key parameters is done to determine the model specifications and to present some managerial insights. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) solar energy is a very promising renewable energy technology, as solar PV systems are less efficient because of climate conditions, temperature, and irradiance change. So, to resolve this problem, two PV topologies are used, i.e., centralized and distributed PV systems. The centralized technique is quicker than the distributed technique in terms of convergence speed and a faster power tracking approach. In the event of uniform irradiance, the centralized system also has the benefit of supplying superior energy, but in PS scenarios, a huge amount of energy is lost. However, the distributed approach requires current and voltage measurements at each panel, resulting in a massive data set. Nevertheless, in the event of shading circumstances, the distributed technique is highly effective because a modular level power electronics (MLPE) converter is used. While in a centralized PV system, there is only a single DC-DC converter for the whole PV system. In this research work, a DFO-based DC-DC converter is designed for modular level, with an ability to perform a rapid shutdown of the module under fire hazard conditions, troubleshooting, and monitoring of a module in a very efficient way. The robustness of the proposed MPPT DFO algorithm is tested with different techniques such as Cuckoo Search (CS), Fruit Fly Optimization (FFO), Particle swarm optimization (PSO), Incremental conductance (InC), and Perturb and observe(P&O) techniques. The proposed technique shows better results in terms of MPPT efficiency, dynamic responsiveness, and harmonics. Furthermore, the result of MLPE and the centralized system is verified by using the Helioscope with different inverter companies like SMA, Tigo, Enphase, Solar edge, and Huawei. The results prove that MLPE is a better option in the case of shading region for attaining the maximum power point. [ABSTRACT FROM AUTHOR]

The objective of this research was to build a mathematical model based on a Support Vector Machine (SVM) capable of predicting the amount adsorbed at equilibrium (qe). Activated carbon fibres (ACF) were used for the adsorption of certain phenols (phenol, 2-chlorophenol, 4-chlorophenol, 2,4,6-trichlorophenol, 4-nitrophenol, and 2,4-dinitrophenol). An experimental dataset of 129 points was collected from previously published papers. The inputs considered for modelling were temperature (T), concentration at equilibrium (ce), and two descriptors (boiling point (BP) and density (d)) to differentiate between the pollutants studied. The data used were pre-processed by the statistical analysis to ensure that they were adequate for modelling. The results showed a superiority of the Gaussian kernel function DA-SVM model demonstrated by its determination coefficient (R2 = 0.997) and root mean squared error (RMSE = 0.027 mmol l−1).

Induction motors tend to have better efficiency on rated conditions, but at partial load conditions, when these motors operate on rated flux, they exhibit lower efficiency. In such conditions, when these motors operate for a long duration, a lot of electricity gets consumed by the motors, due to which the computational cost as well as the total running cost of industrial plant increases. Squirrel-cage induction motors are widely used in industries due to their low cost, robustness, easy maintenance, and good power/mass relation all through their life cycle. A significant amount of electrical energy is consumed due to the large count of operational units worldwide; hence, even an enhancement in minute efficiency can direct considerable contributions within revenue saving, global electricity consumption, and other environmental facts. In order to improve the efficiency of induction motors, this research paper presents a novel contribution to maximizing the efficiency of induction motors. As such, a model of induction motor drive is taken, in which the proportional integral (PI) controller is tuned. The optimal tuning of gains of a PI controller such as proportional gain and integral gain is conducted. The tuning procedure in the controller is performed in such a condition that the efficiency of the induction motor should be maximum. Moreover, the optimization concept relies on the development of a new hybrid algorithm, the so-called Scrounger Strikes Levy-based dragonfly algorithm (SL-DA), that hybridizes the concept of dragonfly algorithm (DA) and group search optimization (GSO). The proposed algorithm is compared with particle swarm optimization (PSO) for verification. The analysis of efficiency, speed, torque, energy savings, and output power is validated, which confirms the superior performance of the suggested method over the comparative algorithms employed. [ABSTRACT FROM AUTHOR]

Space target feature extraction and space infrared target recognition are important components of space situational awareness (SSA). However, owing to far imaging distance between the space target and infrared detector, the infrared signal of the target received by the detector is dim and easily contaminated by noise. To effectively improve the accuracy of feature extraction and recognition, it is essential to suppress the noise of the infrared signal. Hence, a novel denoising and extracting feature method combinating optimal variational mode decomposition (VMD) and dual-band thermometry (DBT) is proposed. It takes the mean weighted fuzzy-distribution entropy (FuzzDistEn) of the band-limited intrinsic mode functions (BLIMFs) as the optimization index of dragonfly algorithm (DA) to obtain the optimal parameters (K, α) of VMD. Then the VMD is utilized to decompose the noisy signal to obtain a series of BLIMFs and the Pearson correlation coefficient (PCC) is proposed to determine the effective modes to reconstructe the denoising signal. Finally, based on the denoising signal, the feature of temperature and emissivity-area product are calculated using the DBT. The simulation and experiment results show that the proposed method has better noise reduction performance compared with the other denoising methods, and the accuracy of feature extraction is improved at different noise equivalent irradiance. This provides more accurate feature of temerpature and emissivity-area product for space infrared dim target recognition. [ABSTRACT FROM AUTHOR]

To solve the access selection problem of heterogeneous wireless network, an access selection algorithm based on dragonfly algorithm (DA) optimized fuzzy neural network (FNN) was proposed, considering the user’s business type and network state.In view of the low convergence speed of the fuzzy neural network, the dragonfly algorithm was used to optimize the membership function parameters of the second and fifth layers of the fuzzy neural network, so as to obtain the initial value of membership function parameters of the fuzzy neural network.The most suitable network was selected for the users according to their preference to the network and the output score of the network under different business types.The experimental results show that dragonfly algorithm optimization can improve the convergence speed of fuzzy neural network, improve system throughput, reduce blocking rate, and reduce switching times to some extent.