Journal articles: 'Dragonfly algorithm'

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Liu, Fang. "Path Recognition of the Regional Education Expansion Based on Improved Dragonfly Algorithm." Mathematical Problems in Engineering 2021 (September 7, 2021): 1–5. http://dx.doi.org/10.1155/2021/9928020.

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To solve the problems of low recognition rate, high misrecognition rate, and long recognition time, the path recognition method of the regional education scale expansion based on the improved dragonfly algorithm is proposed. Through a variety of different behaviors utilized in the optimization process, the dragonfly algorithm model has been constructed. The step size and the position vector are introduced to update the dragonfly’s location. The dragonfly’s foraging behaviors are accurately simulated. Afterward, the dragonfly algorithm is combined with the flower authorization algorithm. The conversion probability is added, and the dragonfly’s global development ability is adjusted in real-time. Then, the dragonfly algorithm is improved. The improved dragonfly algorithm is employed to extract the features of the expansion path of the regional education scale. The improved support vector machine is utilized as a classifier to realize the recognition of the regional education scale expansion path. The experimental results denote that the proposed method has a high recognition rate of the regional education scale expansion path and can effectively reduce the misrecognition rate and shorten the recognition time.

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Rahman, Chnoor M., and Tarik A. Rashid. "Dragonfly Algorithm and Its Applications in Applied Science Survey." Computational Intelligence and Neuroscience 2019 (December 6, 2019): 1–21. http://dx.doi.org/10.1155/2019/9293617.

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One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm’s weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.

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Santoso, Ong, Hansel, Hartarto Junaedi, and Joan Santoso. "Dragonfly Algorithm for Crowd NPC Movement Simulation in Metaverse." Bulletin of Social Informatics Theory and Application 6, no. 1 (2023): 76–83. http://dx.doi.org/10.31763/businta.v6i1.551.

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During The Pandemic Period The Development Of Virtual Reality (Vr) In The Field Of Social Media (Metaverse) Is Very Fast To Give New Experiences. To Provide A New Experience, The Development Of A Supporting Virtual World As A Gathering Place Is Needed, To Support The Presence Of Others That Become A Factor Of Social Virtual Presence (Svr) Npc Is Required. Npc Crowds Will Be Tested In Job Fair Case Study By Compared Dragonfly And Particle Swarm Optimization Algorithms. Algorithm Testing Will Be Adjustable With The Same Parameters And Profiles For Individuals And Objectives. After Experiment And Evaluation, Dragonfly Algorith Was More Optimal And Provided Better SVR.

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Baiche, Karim, Yassine Meraihi, Manolo Dulva Hina, Amar Ramdane-Cherif, and Mohammed Mahseur. "Solving Graph Coloring Problem Using an Enhanced Binary Dragonfly Algorithm." International Journal of Swarm Intelligence Research 10, no. 3 (2019): 23–45. http://dx.doi.org/10.4018/ijsir.2019070102.

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The graph coloring problem (GCP) is one of the most interesting classical combinatorial optimization problems in graph theory. It is known to be an NP-Hard problem, so many heuristic algorithms have been employed to solve this problem. In this article, the authors propose a new enhanced binary dragonfly algorithm to solve the graph coloring problem. The binary dragonfly algorithm has been enhanced by introducing two modifications. First, the authors use the Gaussian distribution random selection method for choosing the right value of the inertia weight w used to update the step vector (∆X). Second, the authors adopt chaotic maps to determine the random parameters s, a, c, f, and e. The aim of these modifications is to improve the performance and the efficiency of the binary dragonfly algorithm and ensure the diversity of solutions. The authors consider the well-known DIMACS benchmark graph coloring instances to evaluate the performance of their algorithm. The simulation results reveal the effectiveness and the successfulness of the proposed algorithm in comparison with some well-known algorithms in the literature.

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Yuan, Li, Fangjun Kuang, Siyang Zhang, and Huiling Chen. "The Gaussian Mutational Barebone Dragonfly Algorithm: From Design to Analysis." Symmetry 14, no. 2 (2022): 331. http://dx.doi.org/10.3390/sym14020331.

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The dragonfly algorithm is a swarm intelligence optimization algorithm based on simulating the swarming behavior of dragonfly individuals. An efficient algorithm must have a symmetry of information between the participating entities. An improved dragonfly algorithm is proposed in this paper to further improve the global searching ability and the convergence speed of DA. The improved DA is named GGBDA, which adds Gaussian mutation and Gaussian barebone on the basis of DA. Gaussian mutation can randomly update the individual positions to avoid the algorithm falling into a local optimal solution. Gaussian barebone can quicken the convergent speed and strengthen local exploitation capacities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of engineering design. To verify the superiorities of GGBDA, this paper sets 30 benchmark functions, which are taken from CEC2014 and 4 engineering design problems to compare GGBDA with other algorithms. The experimental result show that the Gaussian mutation and Gaussian barebone can effectively improve the performance of DA. The proposed GGBDA, similar to the DA, presents improvements in global optimization competence, search accuracy, and convergence performance.

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Chen, Yilin, Bo Gao, Tao Lu, et al. "A Hybrid Binary Dragonfly Algorithm with an Adaptive Directed Differential Operator for Feature Selection." Remote Sensing 15, no. 16 (2023): 3980. http://dx.doi.org/10.3390/rs15163980.

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Feature selection is a typical multiobjective problem including two conflicting objectives. In classification, feature selection aims to improve or maintain classification accuracy while reducing the number of selected features. In practical applications, feature selection is one of the most important tasks in remote sensing image classification. In recent years, many metaheuristic algorithms have attempted to explore feature selection, such as the dragonfly algorithm (DA). Dragonfly algorithms have a powerful search capability that achieves good results, but there are still some shortcomings, specifically that the algorithm’s ability to explore will be weakened in the late phase, the diversity of the populations is not sufficient, and the convergence speed is slow. To overcome these shortcomings, we propose an improved dragonfly algorithm combined with a directed differential operator, called BDA-DDO. First, to enhance the exploration capability of DA in the later stages, we present an adaptive step-updating mechanism where the dragonfly step size decreases with iteration. Second, to speed up the convergence of the DA algorithm, we designed a new differential operator. We constructed a directed differential operator that can provide a promising direction for the search, then sped up the convergence. Third, we also designed an adaptive paradigm to update the directed differential operator to improve the diversity of the populations. The proposed method was tested on 14 mainstream public UCI datasets. The experimental results were compared with seven representative feature selection methods, including the DA variant algorithms, and the results show that the proposed algorithm outperformed the other representative and state-of-the-art DA variant algorithms in terms of both convergence speed and solution quality.

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Dong, Yuxue, Mengxia Li, and Mengxiang Zhou. "Multi-Threshold Image Segmentation Based on the Improved Dragonfly Algorithm." Mathematics 12, no. 6 (2024): 854. http://dx.doi.org/10.3390/math12060854.

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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.

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Zhang, Yue, Hongping Pu, and Wei Chen. "Positioning algorithm based on improved dragonfly optimization." International Journal of Computer Science and Information Technology 1, no. 1 (2023): 54–59. http://dx.doi.org/10.62051/ijcsit.v1n1.08.

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Aiming at solving the nonlinear equation of indoor arrival time difference positioning, a multi-strategy improved dragonfly optimization algorithm is proposed. The initial population is improved by chaotic mapping, and then nonlinear factors and Cauchy mutation operators are introduced to rapidly converge the balanced global search and local search. At the same time, simulation and comparison experiments with other algorithms show that the algorithm has a higher positioning effect.

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Khaleel, Layth Riyadh, and Ban Ahmed Mitras. "A Novel Hybrid Dragonfly Algorithm with Modified Conjugate Gradient Method." International Journal of Computer Networks and Communications Security 8, no. 2 (2020): 17–25. http://dx.doi.org/10.47277/ijcncs/8(2)2.

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Dragonfly Algorithm (DA) is a meta-heuristic algorithm, It is a new algorithm proposed by Mirjalili in (2015) and it simulate the behavior of dragonflies in their search for food and migration. In this paper, a modified conjugate gradient algorithm is proposed by deriving new conjugate coefficient. The sufficient descent and the global convergence properties for the proposed algorithm are proved. Novel hybrid algorithm of the dragonfly (DA) was proposed with modified conjugate gradient Algorithm which develops the elementary society that is randomly generated as the primary society for the dragonfly optimization algorithm using the characteristics of the modified conjugate gradient algorithm. The efficiency of the hybrid algorithm was measured by applying it to (10) of the optimization functions of high measurement with different dimensions and the results of the hybrid algorithm were very good in comparison with the original algorithm

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Neelima, P., and A. Rama Mohan Reddy. "An Efficient Novel Load Balancing Algorithm to Improve the Performance of the System in Cloud Environment." Asian Journal of Computer Science and Technology 8, S3 (2019): 105–8. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2074.

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Distribution of workload in a balanced manner is a main challenge in cloud computing system. It distributes workload among multiple nodes, hence resources are properly utilized. This is an optimization problem and a good load balancer should be involved for this strategy to the types of tasks and dynamic environment. To overcome load balancing problem here a Novel Load balancing Algorithm is develop i.e. Dragonfly Algorithm is design and developed, to execute the entire task with shortest completion time and load balanced. Our algorithm will be presented with efficient solution representation, derivation of efficient fitness function (or multi-objective function) along with the usual Dragonfly operators. The performance of the algorithm will be analyzed based on the different evaluation measures. The algorithms like particle swarm optimization (PSO) and Genetic algorithm (GA) will be taken for the comparative analysis.

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Chatterjee, Bitanu, Sayan Acharya, Trinav Bhattacharyya, Seyedali Mirjalili, and Ram Sarkar. "Stock market prediction using Altruistic Dragonfly Algorithm." PLOS ONE 18, no. 4 (2023): e0282002. http://dx.doi.org/10.1371/journal.pone.0282002.

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Stock market prediction is the process of determining the value of a company’s shares and other financial assets in the future. This paper proposes a new model where Altruistic Dragonfly Algorithm (ADA) is combined with Least Squares Support Vector Machine (LS-SVM) for stock market prediction. ADA is a meta-heuristic algorithm which optimizes the parameters of LS-SVM to avoid local minima and overfitting, resulting in better prediction performance. Experiments have been performed on 12 datasets and the obtained results are compared with other popular meta-heuristic algorithms. The results show that the proposed model provides a better predictive ability and demonstrate the effectiveness of ADA in optimizing the parameters of LS-SVM.

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Patel, Brijesh, Varsha Dubey, Snehlata Barde, and Nidhi Sharma. "Optimum Path Planning Using Dragonfly-Fuzzy Hybrid Controller for Autonomous Vehicle." Eng 5, no. 1 (2024): 246–65. http://dx.doi.org/10.3390/eng5010013.

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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.

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胡, 小平. "Study on Improvement of Dragonfly Algorithm." Computer Science and Application 09, no. 07 (2019): 1377–86. http://dx.doi.org/10.12677/csa.2019.97155.

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Wang, Lin, Ronghua Shi, and Jian Dong. "A Hybridization of Dragonfly Algorithm Optimization and Angle Modulation Mechanism for 0-1 Knapsack Problems." Entropy 23, no. 5 (2021): 598. http://dx.doi.org/10.3390/e23050598.

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The dragonfly algorithm (DA) is a new intelligent algorithm based on the theory of dragonfly foraging and evading predators. DA exhibits excellent performance in solving multimodal continuous functions and engineering problems. To make this algorithm work in the binary space, this paper introduces an angle modulation mechanism on DA (called AMDA) to generate bit strings, that is, to give alternative solutions to binary problems, and uses DA to optimize the coefficients of the trigonometric function. Further, to improve the algorithm stability and convergence speed, an improved AMDA, called IAMDA, is proposed by adding one more coefficient to adjust the vertical displacement of the cosine part of the original generating function. To test the performance of IAMDA and AMDA, 12 zero-one knapsack problems are considered along with 13 classic benchmark functions. Experimental results prove that IAMDA has a superior convergence speed and solution quality as compared to other algorithms.

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Wibowo, Bayu Setyo, Susatyo Handoko, and Hermawan Hermawan. "Optimization Economic and Emissions of Hydro and Thermal Power Plants in 150 kV Systems Using the Dragonfly Algorithm." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 6, no. 1 (2021): 8–13. http://dx.doi.org/10.25139/inform.v6i1.3320.

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Electricity is one of the energies required by daily living since the greater demand for electricity increases greenhouse emissions that create emission gases resulting in global climate change. The main portion of the output cost is fuel's cost to manufacture electrical energy in thermal turbines. The use of electrical energy is currently rising increasingly following the increasing population. The research aims to optimize hydro generation to minimize thermal generation expense and address economic problems and pollution from shipping. With 2016b using Matlab applications and the lambda iteration process, the analysis method uses the Dragonfly Algorithm method. The analysis found that the average cost of fuel consumption provided by the Dragonfly Algorithm method was IDR 151,164,418 per day with an emission of 917.40 tons per day, based on the simulation results the Dragonfly Algorithm in testing by considering the emission of 5 practical steps. Meanwhile, with the emission of 918,044 tonnes per day, the average cost of fuel consumption produced by the Lambda Iteration method is IDR 151,202,209 per day. Test results can enhance the fuel consumption cost of IDR 37,791 and emissions of 0.641 tons with the Dragonfly Algorithm process.

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Xie, Tao, Jun Yao, and Zhiwei Zhou. "DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis." Processes 7, no. 5 (2019): 263. http://dx.doi.org/10.3390/pr7050263.

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As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.

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Vasavi Krishna, D., M. Surya Kalavathi, and B. Ganeshbabu. "A Novel NR-DA-Based ANN for SHEPWM in Cascaded Multilevel Inverters for Renewable Energy Applications." International Transactions on Electrical Engineering and Computer Science 3, no. 3 (2024): 135–43. http://dx.doi.org/10.62760/iteecs.3.3.2024.97.

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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.

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Ezzerrifi Amrani, Ismail, Ahmed Lahjouji El Idrissi, Abdelkhalek BAHRI, and Ahmad El ALLAOUI. "A dragonfly algorithm for solving the Fixed Charge Transportation Problem FCTP." Data and Metadata 3 (February 8, 2024): 218. http://dx.doi.org/10.56294/dm2024218.

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The primary focus of this article is dedicated to a thorough investigation of the Fixed Load Transportation Problem (FCTP) and the proposition of an exceedingly efficient resolution method, with a specific emphasis on the achievement of optimal transportation plans within practical time constraints. The FCTP, recognized for its intricate nature, falls into the NP-complete category, notorious for its exponential growth in solution time as the problem's size escalates. Within the realm of combinatorial optimization, metaheuristic techniques like the Dragonfly algorithm and genetic algorithms have garnered substantial acclaim due to their remarkable capacity to deliver high-quality solutions to the challenging FCTP. These techniques demonstrate substantial potential in accelerating the resolution of this formidable problem. The central goal revolves around the exploration of groundbreaking solutions for the Fixed Load Transportation Problem, all while concurrently minimizing the time investment required to attain these optimal solutions. This undertaking necessitates the adept utilization of the Dragonfly algorithm, an algorithm inspired by natural processes, known for its adaptability and robustness in solving complex problems. The FCTP, functioning as an optimization problem, grapples with the multifaceted task of formulating distribution plans for products originating from multiple sources and destined for various endpoints. The overarching aspiration is to minimize overall transportation costs, a challenge that mandates meticulous considerations, including product availability at source locations and demand projections at destination points. The proposed methodology introduces an innovative approach tailored explicitly for addressing the Fixed Charge Transport Problem (FCTP) by harnessing the inherent capabilities of the Dragonfly algorithm. This adaptation of the algorithm's underlying processes is precisely engineered to handle large-scale FCTP instances, with the ultimate objective of unveiling solutions that have hitherto remained elusive. The numerical results stemming from our rigorous experiments unequivocally underscore the remarkable prowess of the Dragonfly algorithm in discovering novel and exceptionally efficient solutions. This demonstration unequivocally reaffirms its effectiveness in overcoming the inherent challenges posed by substantial FCTP instances. In summary, the research represents a significant leap forward in the domain of FCTP solution methodologies by seamlessly integrating the formidable capabilities of the Dragonfly algorithm into the problem-solving process. The insights and solutions presented in this article hold immense promise for significantly enhancing the efficiency and effectiveness of FCTP resolution, ultimately benefiting a broad spectrum of industries and logistics systems, and promising advancements in the optimization of transportation processes.

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Emambocus, Bibi Aamirah Shafaa, Muhammed Basheer Jasser, Aida Mustapha, and Angela Amphawan. "Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications." Sensors 21, no. 22 (2021): 7542. http://dx.doi.org/10.3390/s21227542.

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Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.

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Chakrabarti, Y. Ravi Kumar, Bhuvan Unhelkar, S. Siva Shankar, et al. "An intelligent framework for credit card fraud detection through data analytics." Journal of Statistics and Management Systems 28, no. 1 (2025): 139–49. https://doi.org/10.47974/jsms-1320.

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An intelligent framework combining Autoencoder Neural Networks and the Dragonfly optimization algorithm, which would be put forth to this research to combat effective credit card fraud by detecting fraudulent transactions quickly through extracting important features and patterns in transactional data with the help of Autoencoder Neural Networks. The Dragonfly optimization algorithm enhances the recital of the archetypal in question by refining the hyperparameters of the autoencoder archetypal. In doing so, the algorithm improves adaptability to emerging fraud patterns and always gives strong generalizations. Critical experiments are conducted that show that the framework has enormous precision, accuracy, F1 score, specificity, as well as recall while trying to detect credit card fraud as accurately as possible, reaching a maximum accuracy of 98%. This framework, therefore, will prove to be a good defence against credit card fraud by finally protecting financial interests, based on drawing the benefits of Autoencoder Neural Networks and Dragonfly optimization.

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Chen, Yuanyuan, and Zhibin Wang. "Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm." Molecules 24, no. 3 (2019): 421. http://dx.doi.org/10.3390/molecules24030421.

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Wavelength selection is an important preprocessing issue in near-infrared (NIR) spectroscopy analysis and modeling. Swarm optimization algorithms (such as genetic algorithm, bat algorithm, etc.) have been successfully applied to select the most effective wavelengths in previous studies. However, these algorithms suffer from the problem of unrobustness, which means that the selected wavelengths of each optimization are different. To solve this problem, this paper proposes a novel wavelength selection method based on the binary dragonfly algorithm (BDA), which includes three typical frameworks: single-BDA, multi-BDA, ensemble learning-based BDA settings. The experimental results for the public gasoline NIR spectroscopy dataset showed that: (1) By using the multi-BDA and ensemble learning-based BDA methods, the stability of wavelength selection can improve; (2) With respect to the generalized performance of the quantitative analysis model, the model established with the wavelengths selected by using the multi-BDA and the ensemble learning-based BDA methods outperformed the single-BDA method. The results also indicated that the proposed method is not limited to the dragonfly algorithm but can also be combined with other swarm optimization algorithms. In addition, the ensemble learning idea can be applied to other feature selection areas to obtain more robust results.

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Urooj, Shabana, Fadwa Alrowais, Ramya Kuppusamy, Yuvaraja Teekaraman, and Hariprasath Manoharan. "New Gen Controlling Variable Using Dragonfly Algorithm in PV Panel." Energies 14, no. 4 (2021): 790. http://dx.doi.org/10.3390/en14040790.

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In the present scenario the depletion of conventional sources causes an energy crisis. The energy crisis causes load demand with respect to electricity. The use of renewable energy sources plays a vital role in reducing the energy crisis and in reduction of CO2 emission. The use of solar energy is the major source of power in generation as this is the root cause for the development of wind, tides, etc. However, due to climatic condition the availability of PV sources varies from time to time. Hence it is essential to track the maximum source of energy by implementing different types of MPPT algorithms. However, use of MPPT algorithms has the limitation of using the same during partial shadow conditions. The issue of tracking power under partial shadow conditions can be resolved by implementing an intelligent optimization tracking algorithm which involves a computation process. Though many of nature’s inspired algorithms were present to address real world problems, Mirjalili developed the dragonfly algorithm to provide a better optimization solution to the issues faced in real-time applications. The proposed concept focuses on the implementation of the dragonfly optimization algorithm to track the maximum power from solar and involves the concept of machine learning, image processing, and data computation.

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Hammouri, Abdelaziz I., Majdi Mafarja, Mohammed Azmi Al-Betar, Mohammed A. Awadallah, and Iyad Abu-Doush. "An improved Dragonfly Algorithm for feature selection." Knowledge-Based Systems 203 (September 2020): 106131. http://dx.doi.org/10.1016/j.knosys.2020.106131.

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Meraihi, Yassine, Amar Ramdane-Cherif, Dalila Acheli, and Mohammed Mahseur. "Dragonfly algorithm: a comprehensive review and applications." Neural Computing and Applications 32, no. 21 (2020): 16625–46. http://dx.doi.org/10.1007/s00521-020-04866-y.

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Pavan Ku, V. T. Ram, M. Arulselvi, and K. B. S. Sastry. "An Optimized Deep Learning Based Optimization Algorithm for the Detection of Colon Cancer Using Deep Recurrent Neural Networks." International Journal of Communication Networks and Information Security (IJCNIS) 14, no. 1s (2023): 22–36. http://dx.doi.org/10.17762/ijcnis.v14i1s.5589.

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Colon cancer is the second leading dreadful disease-causing death. The challenge in the colon cancer detection is the accurate identification of the lesion at the early stage such that mortality and morbidity can be reduced. In this work, a colon cancer classification method is identified out using Dragonfly-based water wave optimization (DWWO) based deep recurrent neural network. Initially, the input cancer images subjected to carry a pre-processing, in which outer artifacts are removed. The pre-processed image is forwarded for segmentation then the images are converted into segments using Generative adversarial networks (GAN). The obtained segments are forwarded for attribute selection module, where the statistical features like mean, variance, kurtosis, entropy, and textual features, like LOOP features are effectively extracted. Finally, the colon cancer classification is solved by using the deep RNN, which is trained by the proposed Dragonfly-based water wave optimization algorithm. The proposed DWWO algorithm is developed by integrating the Dragonfly algorithm and water wave optimization.

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Sayed, Gehad Ismail, Alaa Tharwat, and Aboul Ella Hassanien. "Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection." Applied Intelligence 49, no. 1 (2018): 188–205. http://dx.doi.org/10.1007/s10489-018-1261-8.

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Wang, Yu, Xia Zhang, Dao-Jie Yu, Yi-Jie Bai, Jian-Ping Du, and Zhou-Tai Tian. "Tent Chaotic Map and Population Classification Evolution Strategy-Based Dragonfly Algorithm for Global Optimization." Mathematical Problems in Engineering 2022 (September 30, 2022): 1–18. http://dx.doi.org/10.1155/2022/2508414.

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Dragonfly algorithm (DA) is a recently proposed optimization algorithm based on swarm intelligence, which has been successfully applied in function optimization, feature selection, parameter adjustment, etc. However, it fails to take individual optimal position into consideration but only relies on population optimal position and 5 behaviours to update individual position, leading to low accuracy, slow convergence, and local optima. To overcome these drawbacks, Tent Chaotic Map and Population Classification Evolution Strategy-Based Dragonfly Algorithm (TPDA) is proposed. Tent chaotic map is used to initialize the population, making individuals distributed more uniformly in search space to improve population diversity and search efficiency. Population is classified according to individual fitness value, and different position update methods are adopted for different types of individuals to guide the search process and improve the ability of TPDA to jump out of local optima, thus realizing a balance between exploration and exploitation. The efficiency of TPDA has been validated by tests on 18 basic unconstrained benchmark functions. A comparative performance analysis between TPDA, Particle Swarm Optimization (PSO), DA, and Adaptive Learning Factor and Differential Evolution-Based Dragonfly Algorithm (ADDA) has been carried out. Experimental and statistical results demonstrate that TPDA gives significantly better performances compared with PSO, DA, and ADDA on the average and standard deviation in all 18 functions. The global optimization capability of TPDA on high-dimensional functions and the comparison of the time complexity of TPDA and other swarm intelligence algorithms is also verified in the paper. The results indicate that TPDA is able to perform better on optimizing functions without consuming more computational time.

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Alharbi, Majed. "Investigating a Sustainable Inventory System with Controlled Non-instantaneous Deterioration for Green Products via the Dragonfly Algorithm." Sustainability 17, no. 3 (2025): 1156. https://doi.org/10.3390/su17031156.

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Sustainability is essential in addressing the environmental impacts of supply chains, a significant source of global emissions. This study develops an inventory model to optimize retailer profit by integrating joint pricing, environmental investment, ordering costs, preservation technology, and replenishment timing for non-instantaneously decaying items. Demand depends on stock and selling price, while an algorithm optimizes variables such as selling price, preservation investment, emission costs, ordering costs, and replenishment cycles. The dragonfly algorithm (DA) is employed to find optimal solutions, with numerical analysis demonstrating the model’s application. To justify the results, we have used an updated version of the dragonfly algorithm. Managerial insights highlight the practical relevance of the proposed framework.

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Zhang, Jun, and Qian Li. "Credibilistic Mean-Semi-Entropy Model for Multi-Period Portfolio Selection with Background Risk." Entropy 21, no. 10 (2019): 944. http://dx.doi.org/10.3390/e21100944.

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In financial markets, investors will face not only portfolio risk but also background risk. This paper proposes a credibilistic multi-objective mean-semi-entropy model with background risk for multi-period portfolio selection. In addition, realistic constraints such as liquidity, cardinality constraints, transaction costs, and buy-in thresholds are considered. For solving the proposed multi-objective problem efficiently, a novel hybrid algorithm named Hybrid Dragonfly Algorithm-Genetic Algorithm (HDA-GA) is designed by combining the advantages of the dragonfly algorithm (DA) and non-dominated sorting genetic algorithm II (NSGA II). Moreover, in the hybrid algorithm, parameter optimization, constraints handling, and external archive approaches are used to improve the ability of finding accurate approximations of Pareto optimal solutions with high diversity and coverage. Finally, we provide several empirical studies to show the validity of the proposed approaches.

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Emambocus, Bibi Aamirah Shafaa, Muhammed Basheer Jasser, Angela Amphawan, and Ali Wagdy Mohamed. "An Optimized Discrete Dragonfly Algorithm Tackling the Low Exploitation Problem for Solving TSP." Mathematics 10, no. 19 (2022): 3647. http://dx.doi.org/10.3390/math10193647.

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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.

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Isa, Zainuddin Mat, Norkharziana Mohd Nayan, Mohd Hafiz Arshad, and Nor Ashbahani Mohamad Kajaan. "Optimizing PEMFC model parameters using ant lion optimizer and dragonfly algorithm: A comparative study." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5295. http://dx.doi.org/10.11591/ijece.v9i6.pp5295-5303.

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This paper introduced two optimization algorithms which are Ant Lion Optimizer (ALO) and Dragonfly Algorithm (DA) for extracting the Proton Exchange Membrane Fuel Cell (PEMFC) polarization curve parameters. The results produced by both algorithms are being compared to observe their performance. As a results, the ALO shows great performance compared to DA. Furthermore, these results also being compared with the results of the other reported metaheuristics algorithms. The ALO and DA presented competitive results.

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Zainuddin, Mat Isa, Mohd Nayan Norkharziana, Hafiz Arshad Mohd, and Ashbahani Mohamad Kajaan Nor. "Optimizing PEMFC model parameters using ant lion optimizer and dragonfly algorithm: a comparative study." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5312–20. https://doi.org/10.11591/ijece.v9i6.pp5312-5320.

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This paper introduced two optimization algorithms which are Ant Lion Optimizer (ALO) and Dragonfly Algorithm (DA) for extracting the Proton Exchange Membrane Fuel Cell (PEMFC) polarization curve parameters. The results produced by both algorithms are being compared to observe their performance. As a results, the ALO shows great performance compared to DA. Furthermore, these results also being compared with the results of the other reported metaheuristics algorithms. The ALO and DA presented competitive results.

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Qasim, Omar S., Mohammed Sabah Mahmoud, and Fatima Mahmood Hasan. "Hybrid Binary Dragonfly Optimization Algorithm with Statistical Dependence for Feature Selection." International Journal of Mathematical, Engineering and Management Sciences 5, no. 6 (2020): 1420–28. http://dx.doi.org/10.33889/ijmems.2020.5.6.105.

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The aim of the feature selection technique is to obtain the most important information from a specific set of datasets. Further elaborations in the feature selection technique will positively affect the classification process, which can be applied in various areas such as machine learning, pattern recognition, and signal processing. In this study, a hybrid algorithm between the binary dragonfly algorithm (BDA) and the statistical dependence (SD) is presented, whereby the feature selection method in discrete space is modeled as a binary-based optimization algorithm, guiding BDA and using the accuracy of the k-nearest neighbors classifier on the dataset to verify it in the chosen fitness function. The experimental results demonstrated that the proposed algorithm, which we refer to as SD-BDA, outperforms other algorithms in terms of the accuracy of the results represented by the cost of the calculations and the accuracy of the classification.

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Et. al., Vijaya Bhaskar K,. "Modern Swarm Intelligence based Algorithms for Solving Optimal Power Flow Problem in a Regulated Power System Framework." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 1786–93. http://dx.doi.org/10.17762/turcomat.v12i2.1515.

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This paper presents artificial swarm intelligent based algorithms viz., Firefly Algorithm (FFA), Dragonfly Algorithm (DA) and Moth Swarm Algorithm (MSA) to take care of the issues related to optimal power flow (OPF) problem in a power system network. The optimal values of various decision variables obtained by swarm intelligent based algorithms can optimize various objective function of OPF problem. This article is focused with four objectives such as minimization of total fuel cost (TFC) and total active power loss (TAPL); improvisation of total voltage profile (TVD) and voltage stability index (VSI). The effectiveness of various swam intelligent algorithms are investigated on a standard IEEE-30 bus. The performance of distinct algorithms is compared with statistical measures and convergence characteristics.

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Fountas, Nikolaos A., John D. Kechagias, and Nikolaos M. Vaxevanidis. "Swarm intelligence algorithms for optimising sliding wear of nanocomposites." Tribology and Materials 3, no. 1 (2024): 44–50. http://dx.doi.org/10.46793/tribomat.2024.004.

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This paper presents simulation results obtained by a set of modern algorithms adhering to swarm intelligence for minimising wear rate in the case of A356/Al2O3 nanocomposites produced using a compocasting process. Grey wolf optimisation (GWO) algorithm, moth-flame optimisation (MFO) algorithm, dragonfly algorithm (DA) and whale optimisation algorithm (WOA) were the algorithms under examination. A full quadratic regression equation that predicts wear rate, as the optimisation objective by considering reinforcement content, sliding speed, normal load and reinforcement size as the independent process parameters, was utilised as the objective function. Simulation results obtained by the selected algorithms were quite promising in terms of fast convergence and global optimum result arrival, thus prompting to further investigation of applying swarm intelligence to general problem-solving aspects related to tribology.

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Cui, Xueting, Ying Li, Jiahao Fan, Tan Wang, and Yuefeng Zheng. "A Hybrid Improved Dragonfly Algorithm for Feature Selection." IEEE Access 8 (2020): 155619–29. http://dx.doi.org/10.1109/access.2020.3012838.

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Saha, Soumyajit, Somnath Chatterjee, Shibaprasad Sen, Diego Oliva, Marco Perez-Cisneros, and Ram Sarkar. "Contrast enhancement of digital images using dragonfly algorithm." Automatika 65, no. 4 (2024): 1545–57. http://dx.doi.org/10.1080/00051144.2024.2404365.

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Daely, Philip Tobianto, and Soo Yonng Shin. "Analysis of the Dragonfly Algorithm for 2-D Range-Based Wireless Localization." Journal of Korean Institute of Communications and Information Sciences 42, no. 9 (2017): 1843–49. http://dx.doi.org/10.7840/kics.2017.42.9.1843.

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Zhang, Anan, Pengxiang Zhang, and Yating Feng. "Short-term load forecasting for microgrids based on DA-SVM." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 1 (2019): 68–80. http://dx.doi.org/10.1108/compel-05-2018-0221.

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Purpose The study aims to accomplish the short-term load forecasting for microgrids. Short-term load forecasting is a vital component of economic dispatch in microgrids, and the forecasting error directly affects the economic efficiency of operation. To some extent, short-term load forecasting is more difficult in microgrids than in macrogrids. Design/methodology/approach This paper presents the method of Dragonfly Algorithm-based support vector machine (DA-SVM) to forecast the short-term load in microgrids. This method adopts the combination of penalty factor C and kernel parameters of SVM which needs to be optimized as the position of dragonfly to find the solution. It takes the forecast accuracy calculated by SVM as the current fitness value of dragonfly and the optimal position of dragonfly obtained through iteration is considered as the optimal combination of parameters C and s of SVM. Findings DA-SVM algorithm was used to do short-term load forecast in the microgrid of an offshore oilfield group in the Bohai Sea, China and the forecasting results were compared with those of PSO-SVM, GA-SVM and BP neural network models. The experimental results indicate that the DA-SVM algorithm has better global searching ability. In the case of study, the root mean square errors of DA-SVA are about 1.5 per cent and its computation time is saved about 50 per cent. Originality/value The DA-SVM model presented in this paper provides an efficient and effective method of short-term load forecasting for a microgrid electric power system.

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Fei-Fei Liu, Fei-Fei Liu, Shu-Chuan Chu Fei-Fei Liu, Xiaopeng Wang Shu-Chuan Chu, and Jeng-Shyang Pan Xiaopeng Wang. "A Collaborative Dragonfly Algorithm with Novel Communication Strategy and Application for Multi-Thresholding Color Image Segmentation." 網際網路技術學刊 23, no. 1 (2022): 045–62. http://dx.doi.org/10.53106/160792642022012301005.

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<p>The Dragonfly Algorithm (DA) is a novel swarm intelligence algorithm with some positive applications in recent years. The algorithm simulates the basic survival ability of dragonflies to evade predators and capture prey in natural environment. The original DA algorithm converges too fast, and it is easy to fall into the local optimum, which causes the search to stagnate and the algorithm effect is not ideal. Based on above, a collaborative evolutionary dragonfly algorithm (CDA) with multi-group strategy is proposed in this paper. It uses multi-group strategy and Cauchy mutation to jointly improve the convergence speed and accuracy of the original algorithm. Image segmentation is an essential aspect of computer graphics and image processing. It has become increasingly important. This paper uses threshold technology based on the CDA algorithm to find the optimal index value under different threshold conditions. The experimental results have demonstrated that the CDA is highly competitive in terms of convergence speed and convergence accuracy DA algorithm, and CDA also performs excellent advantages in graphic segmentation experiments.</p> <p>&nbsp;</p>

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Shukla, Niraj Kumar, Rajeev Srivastava, and Seyedali Mirjalili. "A Hybrid Dragonfly Algorithm for Efficiency Optimization of Induction Motors." Sensors 22, no. 7 (2022): 2594. http://dx.doi.org/10.3390/s22072594.

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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.

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Ahmed, Bestoun S. "Generating Pairwise Combinatorial Interaction Test Suites Using Single Objective Dragonfly Optimisation Algorithm." Journal of Zankoy Sulaimani - Part A 19, no. 1 (2016): 69–78. http://dx.doi.org/10.17656/jzs.10586.

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Mohammed Majeed, Nadia, and Fawziya Mahmood Ramo. "Implementation of Features Selection Based on Dragonfly Optimization Algorithm." Technium: Romanian Journal of Applied Sciences and Technology 4, no. 10 (2022): 44–52. http://dx.doi.org/10.47577/technium.v4i10.7203.

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Nowadays increasing dimensionality of data produces several issues in machine learning. Therefore, it is needed to decrease the number of features by choosing just the most important ones and eliminating duplicate features, also reducing the number of features that are important to the model. For this purpose, many methodologies known as Feature Selection are applied. In this study, a feature selection approach is proposed based on Swarm Intelligence methods, which search for the best points in the search area to achieve optimization. In this paper, a wrapper feature selection technique based on the Dragonfly algorithm is proposed. The dragonfly optimization technique is used to find the optimal subset of features that could accurately classify breast cancer as benign or malignant. Many times, the fitness function is defined as classification accuracy. In this study, hard vote classes are employed as a model developed to evaluate feature subsets that have been chosen. It is used as an evaluation function (fitness function) to evaluate each dragonfly in the population. The proposed ensemble hard voting classifier utilizes a combination of five machine-learning algorithms to produce a binary classification for feature selection: Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF). According to the results of the experiments, the voting ensemble classifier has the greatest accuracy value among the single classifiers. The proposed method showed that when training the subset features, the accuracy generated by the voting classifier is high at 98.24%, whereas the training of all features achieved an accuracy of 96.49%. The proposed approach makes use of the UCI repository's Wisconsin Diagnostic Breast Cancer (WDBC) Dataset. Which consists of 569 instances and 30 features.

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Novita Popi Wulandari, Agus Adhi Nugroho, and Eka Nuryanto Budisusila. "Application of Dragonfly Algorithm for Economic Scheduling Optimization and Power Plant Emissions." JURAL RISET RUMPUN ILMU TEKNIK 4, no. 1 (2025): 628–40. https://doi.org/10.55606/jurritek.v4i1.5266.

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A reliable electrical grid the power plant scheduling system needs to optimize the plant's performance by considering the economic value and the value of emissions generated by the plant, in addition to the reliability and economic sectors. This system should also take into account the environmental impact, as well as the CO2 and CH4 emissions produced by the plant. This research will use the Dragonfly Algorithm with weighting parameters to schedule hydro and thermal facilities' emissions and economic activities. That the weighting value impacts production costs and emissions is demonstrated by the data acquired from the Dragonfly Algorithm simulation. If economic considerations take precedence in assigning weights, then low-cost generating will result in high-value emissions, and vice versa. The plant's Emission Intensity rating also meets the standards established by the government.

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Jadhav, Pramod P., and Shashank D. Joshi. "WOADF: Whale Optimization Integrated Adaptive Dragonfly Algorithm Enabled with the TDD Properties for Model Transformation." International Journal of Computational Intelligence and Applications 18, no. 04 (2019): 1950026. http://dx.doi.org/10.1142/s1469026819500263.

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Model Transformation (MT) has led the researchers to concentrate more in the field of software engineering. MT focuses mainly on transforming the input model to the target model to make it easily understandable. For the transformation, using optimal rules among a set of rules makes the design simpler. This paper proposes an algorithm, namely Whale Optimization integrated Adaptive Dragonfly (WOADF) algorithm, which integrates Adaptive Dragonfly (ADF) algorithm and Whale Optimization Algorithm (WOA), for transforming class diagrams (CLDs) to Relational Schema (RS). Further, the UML CLD is transformed into the RS model based on specific rules incorporated by the proposed WOADF algorithm. The fitness function of the proposed model is evaluated to select the optimal rule, by including the test cases to evaluate the optimal blocks. Then, the optimal blocks obtained from the proposed WOADF algorithm are used for achieving the transformation from CLD to the RS model. The effectiveness of the proposed WOADF algorithm is checked with Automatic Correctness (AC) and fitness values and is evaluated to be the best when compared to other existing techniques with maximum AC value measured to be 0.812 and fitness value to be 0.897, respectively.

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Salgotra, Rohit, Urvinder Singh, Supreet Singh, Gurdeep Singh, and Sriparna Saha. "A New Set of Mutation Operators for Dragonfly Algorithm." Arabian Journal for Science and Engineering 46, no. 9 (2021): 8761–802. http://dx.doi.org/10.1007/s13369-021-05639-y.

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Devarakonda, Nagaraju, S. Anandarao, and Raviteja Kamarajugadda. "Detection of intruder using the improved dragonfly optimization algorithm." IOP Conference Series: Materials Science and Engineering 1074, no. 1 (2021): 012011. http://dx.doi.org/10.1088/1757-899x/1074/1/012011.

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DUAN, MeiJun, HongYu YANG, Bo YANG, XiPing WU, and HaiJun LIANG. "Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization." IEICE Transactions on Information and Systems E102.D, no. 10 (2019): 1891–901. http://dx.doi.org/10.1587/transinf.2018edp7401.

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Meqdad, Maytham N., Seifedine Kadry, and Hafiz Tayyab Rauf. "Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors." Future Internet 14, no. 10 (2022): 297. http://dx.doi.org/10.3390/fi14100297.

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Things receive digital intelligence by being connected to the Internet and by adding sensors. With the use of real-time data and this intelligence, things may communicate with one another autonomously. The environment surrounding us will become more intelligent and reactive, merging the digital and physical worlds thanks to the Internet of things (IoT). In this paper, an optimal methodology has been proposed for distinguishing outlier sensors of the Internet of things based on a developed design of a dragonfly optimization technique. Here, a modified structure of the dragonfly optimization algorithm is utilized for optimal area coverage and energy consumption reduction. This paper uses four parameters to evaluate its efficiency: the minimum number of nodes in the coverage area, the lifetime of the network, including the time interval from the start of the first node to the shutdown time of the first node, and the network power. The results of the suggested method are compared with those of some other published methods. The results show that by increasing the number of steps, the energy of the live nodes will eventually run out and turn off. In the LEACH method, after 350 steps, the RED-LEACH method, after 750 steps, and the GSA-based method, after 915 steps, the nodes start shutting down, which occurs after 1227 steps for the proposed method. This means that the nodes are turned off later. Simulations indicate that the suggested method achieves better results than the other examined techniques according to the provided performance parameters.

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Nashaat, Heba, Osama Refaat, Fayez W. Zaki, and Islam E. Shaalan. "Dragonfly-Based Joint Delay/Energy LTE Downlink Scheduling Algorithm." IEEE Access 8 (2020): 35392–402. http://dx.doi.org/10.1109/access.2020.2974856.

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Journal articles on the topic 'Dragonfly algorithm'

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