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A Novel Technique for Drone Path Planning Based on a Neighborhood dragonfly algorithm

SENSORS 2025年第3期25卷 863-863页

作者： Agrawal, Sameer Patle, Bhumeshwar K. Sanap, Sudarshan MIT Art Design & Technol Univ Dept Mech Engn Pune 412201 India

Autonomous aerial drone navigation is a rapidly growing topic of research due to its vast application in various indoor applications, including surveillance, search and rescue missions, and environmental monitoring. Current research focuses on the implementation of neighborhood dragonfly algorithms (NDAs) for path planning for single and multiple drones in various indoor environments containing stationary and moving obstacles. The collaborative behavior of dragonflies is a key concept in the current study that helps in exploring the solution space effectively and results in a faster convergence rate. To validate the performance of the proposed NDA approach, various environments are created in real time, and replicas of the same are generated using MATLAB software. Our analysis shows a close agreement between simulation and experimental results, with path length and navigational time differences of less than 5.7%. This underscores the consistency and feasibility of the NDA approach, placing the groundwork for robust and efficient drone navigation systems. The proposed NDA approach is also compared with those already developed, like IACO and PRM, in a similar environment. The NDA approach shows a better performance in terms of smooth path planning and path length optimization. The saving in path length is more than 5%.

关键词： path planning dragonfly algorithm aerial navigation drone AI technique optimization

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dragonfly algorithm-support vector machine approach for prediction the optical properties of blood

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2024年第9期27卷 1119-1128页

作者： Omari, Faiza Khaouane, Latifa Laidi, Maamar Ibrir, Abdellah Fissa, Mohamed Roubehie Hentabli, Mohamed Hanini, Salah Yahia Fares Univ Lab Biomat & Transport Phenomena LBMTP Medea Algeria Yahia Fares Univ Fac Technol Mat & Environm Lab LME Medea Algeria SAIDAL Complex Medea Qual Control Lab Medea Algeria

Knowledge of the optical properties of blood plays important role in medical diagnostics and therapeutic applications in laser medicine. In this paper, we present a very rapid and accurate artificial intelligent approach using dragonfly algorithm/Support Vector Machine models to estimate the optical properties of blood, specifically the absorption coefficient, and the scattering coefficient using key parameters such as wavelength (nm), hematocrit percentage (%), and saturation of oxygen (%), in building very highly accurate dragonfly algorithm-Support Vector Regression models (DA-SVR). 1000 training and testing sets were selected in the wavelength range of 250-1200 nm and the hematocrit of 0-100%. The performance of the proposed method is characterized by high accuracy indicated in the correlation coefficients (R) of 0.9994 and 0.9957 for absorption and scattering coefficients, respectively. In addition, the root mean squared error values (RMSE) of 0.972 and 2.9193, as well as low mean absolute error values (MAE) of 0.2173 and 0.2423, this result showed a strong match with the experimental data. The models can be used to accurately predict the absorption and scattering coefficients of blood, and provide a reliable reference for future studies on the optical properties of human blood.

关键词： Absorption coefficient scattering coefficient human blood dragonfly algorithm support vector machine

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dragonfly algorithm: a comprehensive survey of its results, variants, and applications

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第10期80卷 14979-15016页

作者： Alshinwan, Mohammad Abualigah, Laith Shehab, Mohammad Abd Elaziz, Mohamed Khasawneh, Ahmad M. Alabool, Hamzeh Al Hamad, Husam Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Aqaba Univ Technol Comp Sci Dept Aqaba 77110 Jordan Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Zagazig Univ Dept Math Fac Sci Zagazig Egypt Saudi Elect Univ Coll Comp & Informat Abha Saudi Arabia

This paper thoroughly introduces a comprehensive review of the so-called dragonfly algorithm (DA) and highlights its main characteristics. DA is considered one of the promising swarm optimization algorithms because it successfully applied in a wide range of optimization problems in several fields, such as engineering design, medical applications, image processing, power and energy systems, and economic load dispatch problems. The review describes the available literature on DA, including its variants like binary, discrete, modify, and hybridization of DA. Conclusions focus on the current work on DA, highlighting its disadvantages with suggests possible future research directions. Researchers and practitioners of DA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining, and clustering, among others will benefit from this study.

关键词： dragonfly algorithm Meta-heuristic optimization algorithms Optimization problems Nature-inspired algorithms Swarm intelligence

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dragonfly algorithm: a comprehensive review and applications

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NEURAL COMPUTING & APPLICATIONS 2020年第21期32卷 16625-16646页

作者： Meraihi, Yassine Ramdane-Cherif, Amar Acheli, Dalila Mahseur, Mohammed Univ MHamed Bougara Boumerdes LIST Lab Ave Independence Boumerdes 35000 Algeria Univ Versailles St Quentin En Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France Univ MHamed Bougara Boumerdes LAA Lab Ave Independence Boumerdes 35000 Algeria Univ Sci & Technol Houari Boumediene Fac Elect & Informat Algiers 16025 Algeria

dragonfly algorithm (DA) is a novel swarm intelligence meta-heuristic optimization algorithm inspired by the dynamic and static swarming behaviors of artificial dragonflies in nature. It has proved its effectiveness and superiority compared to several well-known meta-heuristics available in the literature. This paper presents a comprehensive review of DA and its new variants classified into modified and hybrid versions. It also describes the main diverse applications of DA in several fields and areas such as machine learning, neural network, image processing, robotics, and engineering. Finally, the paper suggests some possible interesting research on the applications and hybridizations of DA for future works.

关键词： dragonfly algorithm Nature-inspired algorithm Swarm intelligence Meta-heuristics

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dragonfly algorithm for solving probabilistic Economic Load Dispatch problems

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NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 3029-3045页

作者： Das, Diptanu Bhattacharya, Aniruddha Ray, Rup Narayan Natl Inst Technol Agartala Dept Elect Engn Agartala 799046 Tripura India Natl Inst Technol Durgapur Dept Elect Engn Durgapur 713209 W Bengal India

Economic Load Dispatch problem in power system is solved by different methods to run the generating station in an economic way for different loading conditions. A combination of thermal power plant and renewable plant of wind and solar photovoltaic is considered for optimal solution at minimal cost using dragonfly algorithm. Swarming behaviour of dragonfly is used for optimization of the present Economic Load Dispatch problem. The modelling of solar and wind power plant is done using 2-m point estimation technique to consider the uncertainties in power generation from such renewable sources. The solution of objective function is found using the proposed method (dragonfly algorithm), and the same is compared with that obtained using other well-known algorithms such as Crow Search algorithm, Ant Lion Optimizer, Oppositional Real-Coded Chemical Reaction Optimization, Biogeography-Based Optimization, Particle Swarm Optimization and Genetic algorithm. It is found that the proposed method gives better solution in terms of execution time and cost effectiveness. To validate the performance of dragonfly algorithm, four test systems have been considered. It is observed that the total generation cost obtained by dragonfly algorithm for each test system is less (10,049.1948 $/h for test system I, 2018.0762 $/h for test system II, 15,268.8325 $/h for test system III and 32,310.2922 $/h for test system IV) as compared to other well-known optimization methods. It is also found that the time required to reach minimum solution is lesser in case of dragonfly algorithm (8 s for test system I, 12 s for test system II, 15 s for test system III and 20 s for test system IV) as compared to other optimization techniques.

关键词： Economic dispatch dragonfly algorithm Point estimate method Solar power Valve point loading Wind power

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A Modified dragonfly algorithm for Solving the Temporary Facility Location Planning Problem in Construction Sites

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KSCE Journal of Civil Engineering 2025年

作者： Vu Hong Son Pham Thuy Dung Dau Van Hung Nguyen Department of Construction Engineering & Management Faculty of Civil Engineering Ho Chi Minh City University of Technology (HCMUT) Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

Optimizing construction site layouts is fundamental to enhancing productivity and operational efficiency, directly contributing to project success. However, determining the optimal layout remains a complex challenge due to the need to balance multiple conflicting objectives. This study addresses these challenges by developing a hybrid optimization model that combines the dragonfly algorithm (DA) with Particle Swarm Optimization (PSO). The hybrid DA-PSO model is designed to leverage DA’s global exploration capability and PSO’s efficient local refinement, providing a robust framework for multi-objective optimization. The findings underscore the effectiveness of hybrid DA-PSO algorithm in delivering economically and environmentally optimized layouts while enhancing on-site safety. This research provides construction managers with a powerful decision-support tool to address the complexities of modern construction projects, setting a foundation for broader application in diverse and dynamic construction environments.

关键词： dragonfly algorithm Particle Swarm Optimization Temporary facility Construction site layout Evidential reasoning

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dragonfly algorithm for Solving Flexible Jobshop Scheduling Problem 35

Dragonfly algorithm for Solving Flexible Jobshop Scheduling ...

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35th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Yang, Dongsheng Wu, Mingliang Yang, Zhile Guo, Yuanjun Feng, Wei Northeastern Univ Intelligent Elect Sci & Technol Res Inst Shenyang Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9781728176840

Industrial scheduling problem has always been one of key problems in manufacturing and management planning enterprises. Flexible job shop scheduling problem is one of most difficult scheduling problems, which is also a classical NP hard problem. Compared to the JSP, each process in the FJSP should be processed on each available machine to minimize the total processing time. For this reason, it is far more complex than the classic JSP. In this paper, a recent proposed swarm intelligence algorithm named dragonfly algorithm (DA) is adopted to solve the FJSP. Some traditional algorithms are also used to evaluate the performance on several famous FJSP benchmark compared with DA. Numerical results show that DA has stronger global searching ability and can achieve better results in solving FJSP than other algorithms.

关键词： dragonfly algorithm Flexible job shop scheduling problem Global optimization

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dragonfly algorithm for Multi-target Search Problem in Swarm Robotic with Dynamic Environment Size 35th

Dragonfly Algorithm for Multi-target Search Problem in Swarm...

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35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Hamami, Mohd Ghazali Mohd Ismail, Zool H. Univ Teknol MARA Cawangan Johor Coll Engn Sch Mech Engn Kampus Pasir Gudang Masai 81750 Johor Malaysia Univ Teknol Malaysia Malaysia Japan Int Inst Technol Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia

ISBN: (纸本)9783031085307;9783031085291

Target search elements are very important in real-world applications such as post-disaster search and rescue missions, and pollution detection. In such situations, there will be time limitations, especially under a dynamic environment size which makes multi-target search problems are more demanding and need a special approach and intention. To answer this need, a proposed multi-target search strategy, based on dragonfly algorithm (DA) has been presented in this paper for a Swarm Robotic application. The proposed strategy utilized the DA static swarm (food hunting process) and dynamic swarm (migration process) to achieve the optimized balance between the exploration and exploitation phases during the multi-target search process. For performance evaluation, numerical simulations have been done and the initial results of the proposed strategy show more stability and efficiency than the previous works.

关键词： Multi-target search problem Swarm robotics dragonfly algorithm

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dragonfly algorithm for Optimal Allocation of D-STATCOM in Distribution Systems 1

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4th International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES)

作者： Tejaswini, V Susitra, D. Sathyabama Inst Sci & Technol Deemed Univ Elect Enigineering Chennai 600119 Tamil Nadu India

ISBN: (数字)9789811501999

ISBN: (纸本)9789811501999;9789811501982

In distribution systems, parameters like outage of components, sudden or continuous load growth may cause power quality problems like excessive power losses, voltage instabilities, etc. To compensate power quality problems, usage of custom power devices is most effective approach. To get the best performance, custom power devices must place in optimal locations. Hence, this paper proposes a new swarm intelligence-based dragonfly algorithm (DA) to determine optimal placement of D-STATCOM in the radial distribution systems. DA algorithm is inspired by unique and rare swarming behaviors of dragonflies in nature. Power losses minimization and voltage profile improvement are considered as main objectives for solving optimization problems. The proposed DA model is compared with conventional methods like genetic (GA) and PSO in terms of convergence and cost analysis.

关键词： Power quality-STATCOM dragonfly algorithm GA PSO

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dragonfly algorithm Based Global Maximum Power Point Tracker for Photovoltaic Systems

Dragonfly Algorithm Based Global Maximum Power Point Tracker...

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7th International Conference on Swarm Intelligence (ICSI)

作者： Raman, Gururaghav Raman, Gurupraanesh Manickam, Chakkarapani Ganesan, Saravana Ilango Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli 620015 Tamil Nadu India

ISBN: (纸本)9783319410005;9783319409993

This paper presents the application of the dragonfly algorithm (DA) for tracking the Global Maximum Power Point (GMPP) of a photovoltaic (PV) system. Optimization techniques employed for GMPP tracking (GMPPT) are required to be fast and efficient in order to reduce the tracking time and energy loss respectively. The DA, being a meta-heuristic algorithm with good exploration and exploitation characteristics, is a suitable candidate for this application. Due to its simplicity, the DA is implemented on a low cost microcontroller, and is proven to track the GMPP effectively under various irradiation conditions. The performance of the proposed DA based GMPPT scheme is compared with that of the conventional PSO based GMPPT scheme, and proves to be superior in terms of tracking time and energy loss during tracking.

关键词： dragonfly algorithm Global maximum power point tracking Photovoltaic systems Swarm intelligence

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