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Relevant bibliographies by topics / Dragonfly algorithm

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Academic literature on the topic 'Dragonfly algorithm'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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Journal articles on the topic "Dragonfly algorithm"

1

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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Book chapters on the topic "Dragonfly algorithm"

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Zolghadr-Asli, Babak, Omid Bozorg-Haddad, and Xuefeng Chu. "Dragonfly Algorithm (DA)." In Advanced Optimization by Nature-Inspired Algorithms. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5221-7_15.

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Ehteram, Mohammad, Akram Seifi, and Fatemeh Barzegari Banadkooki. "Structure of Dragonfly Optimization Algorithm." In Application of Machine Learning Models in Agricultural and Meteorological Sciences. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9733-4_8.

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Guajardo, Hector M., and Fevrier Valdez. "Dragonfly Algorithm for Benchmark Mathematical Functions Optimization." In New Horizons for Fuzzy Logic, Neural Networks and Metaheuristics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55684-5_16.

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Xia, Shenyang, and Xing Liu. "Improved Dragonfly Algorithm Based on Mixed Strategy." In Computer Science and Education. Computer Science and Technology. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0730-0_11.

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Singh, Sandeep, Alaknanda Ashok, Manjeet Kumar, Garima, and Tarun Kumar Rawat. "Optimal Design of IIR Filter Using Dragonfly Algorithm." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1819-1_21.

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Tharwat, Alaa, Thomas Gabel, and Aboul Ella Hassanien. "Parameter Optimization of Support Vector Machine Using Dragonfly Algorithm." In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64861-3_29.

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Abdel-Basset, Mohamed, Qifang Luo, Fahui Miao, and Yongquan Zhou. "Solving 0–1 Knapsack Problems by Binary Dragonfly Algorithm." In Intelligent Computing Methodologies. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63315-2_43.

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Pramanik, Sabari, and S. K. Setua. "A Modified Dragonfly Algorithm for Real Parameter Function Optimization." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2188-1_33.

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Padmavathi, C. J. Niraimathi, and Smruti Padhi. "Cryptocurrency Price Prediction Using LSTM and DragonFly Optimization Algorithm." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4071-3_17.

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Mafarja, Majdi, Ali Asghar Heidari, Hossam Faris, Seyedali Mirjalili, and Ibrahim Aljarah. "Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection." In Nature-Inspired Optimizers. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12127-3_4.

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Conference papers on the topic "Dragonfly algorithm"

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Hou, Yanjun, Chenqi Ke, Siying Chen, Daoming Tang, and Chenyin Wu. "UAV Path Planning Based on Improved Dragonfly Algorithm." In 2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI). IEEE, 2024. http://dx.doi.org/10.1109/seai62072.2024.10674190.

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Tian, Jiaqiang, Xinxiang Yin, Tianhong Pan, Xu Zhang, Duo Yang, and Liping Ni. "Parameter Identification of Lithium-ion Battery using Dragonfly Algorithm." In 2024 IEEE 25th China Conference on System Simulation Technology and its Application (CCSSTA). IEEE, 2024. http://dx.doi.org/10.1109/ccssta62096.2024.10691832.

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Wang, Chao, Yong Cheng, and Yerong Zhang. "Application and Improvement of Dragonfly Algorithm in Array Synthesis." In 2024 International Conference on Microwave and Millimeter Wave Technology (ICMMT). IEEE, 2024. http://dx.doi.org/10.1109/icmmt61774.2024.10672247.

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Senthilkumar, S., Gopika B S, S. P. Mangaiyarkarasi, R. Gandhi Raj, M. Nuthal Srinivasan, and P. J. Suresh Babu. "Nature-Inspired Dragonfly MPPT Algorithm for Solar PV System." In 2024 Conference on Renewable Energy Technologies and Modern Communications Systems: Future and Challenges. IEEE, 2024. https://doi.org/10.1109/ieeeconf63577.2024.10881507.

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Dasgupta, Satyaki, Uttara Das, Nabendu Biswas, and Sadhan Gope. "Economic and Environmental Analysis of Power System Using Multi-Objective Dragonfly Algorithm." In 2024 IEEE Silchar Subsection Conference (SILCON). IEEE, 2024. https://doi.org/10.1109/silcon63976.2024.10910337.

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Seth, Kanika, Kambam Vedantan, Vishwesh Deshmukh, V. P. Arul Kumar, Gunveen Ahluwalia, and G. Ramya. "A Novel Hybrid Dragonfly-Cuckoo Search Algorithm For Optimized Mobile Robot Navigation." In 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG). IEEE, 2024. https://doi.org/10.1109/ictbig64922.2024.10911345.

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Balassem, Zaid Ajzan, Ensteih Silvia, Harpreet Kaur Thind, J. Harirajkumar, and P. Kavitha. "Anomaly Detection in Traffic Surveillance Videos using Differential Evolution Dragonfly Algorithm with VGG16." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796197.

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Ramakrishna, J. Siva, Konakati Srija, Madduri Srujana, and Yedulla Srinath. "EEG Based Epileptic Seizure Detection Using Binary Dragonfly Algorithm and Deep Neural Networks." In 2025 3rd International Conference on Smart Systems for applications in Electrical Sciences (ICSSES). IEEE, 2025. https://doi.org/10.1109/icsses64899.2025.11009854.

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Mouna, Chraigui, Nizar Rokbani, Haykal Chaabane, and Sofiène Mansouri. "Toward a Neural-Meta Swarm for inverse kinematics, the Neural-Dragonfly Algorithm, N-DA." In 2024 IEEE International Conference on Artificial Intelligence & Green Energy (ICAIGE). IEEE, 2024. https://doi.org/10.1109/icaige62696.2024.10776647.

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Ganesh Babu, B., B. Harshini, Rashmi Kapoor, D. Ravi Kumar, Poonam Upadhyay, and B. Madhuri. "Enhancing Selective Harmonics Elimination in Cascaded H-Bridge Multilevel Inverters Through Dragonfly Algorithm Optimization." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696326.

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Reports on the topic "Dragonfly algorithm"

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Chance, Frances S. Dragonfly-Inspired Algorithms for Intercept Trajectory Planning. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1569338.

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