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A survey on dragonfly algorithm and its applications in engineering

EVOLUTIONARY INTELLIGENCE 2023年第1期16卷 1-21页

作者： Rahman, Chnoor M. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mirjalili, Seyedali Charmo Univ Coll Hlth & Appl Sci Appl Comp Dept Sulaimany Krg Iraq Univ Kurdistan Hewler Sch Sci & Engn Comp Sci & Engn Dept Erbil Krg Iraq Charles Sturt Univ Sch Comp & Math Sydney NSW Australia Western Sydney Univ Sch Comp Engn & Math Sydney City Campus Sydney NSW Australia Asia Pacific Int Coll APIC Informat Technol Dept Sydney NSW Australia Kent Inst Australia Informat Technol Dept Sydney NSW Australia Singidunum Univ Belgrade Serbia Salahaddin Univ Erbil Software & Informat Engn Erbil Krg Iraq Torrens Univ Ctr Artificial Intelligence Res & Optimisat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

The dragonfly algorithm was developed in 2016. It is one of the algorithms used by researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large-scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance,

关键词： dragonfly algorithm Swarm intelligence Metaheuristic algorithm Optimization algorithm Single and multi-objective optimization DA

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A Conceptual Comparison of dragonfly algorithm Variants for CEC-2021 Global Optimization Problems

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第2期48卷 1563-1593页

作者： Joshi, Milan Kalita, Kanak Jangir, Pradeep Ahmadianfar, Iman Chakraborty, Shankar SVKMs Narsee Monjee Inst Management Studies Mukesh Patel Sch Technol Management & Engn Shirpur Campus Dhule Maharashtra India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Mech Engn Avadi India Rajasthan Rajya Vidyut Prasaran Nigam Sikar Rajasthan India Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran Jadavpur Univ Dept Prod Engn Kolkata India

Since the past few years, several metaheuristic algorithms, inspired by the natural processes, have been introduced to solve different complex optimization problems. Studying and comparing the convergence, computational burden and statistical significance of those metaheuristics are helpful for future algorithmic development and their applications. This paper focuses on comparing the optimization performance of classical dragonfly algorithm (DA) and its seven different variants, i.e., hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder-Mead algorithm and dragonfly algorithm (INMDA) and hybridization of dragonfly algorithm and artificial bee colony (HDA) while solving 80 CEC-2021 benchmark problems. It is observed that the convergence rates of different variants of DA algorithm vary, and the corresponding computational times for such variations are also evaluated. This paper finally ranks DA and its variants according to their convergence efficiency and Friedman test. The DADE, QGDA, BMDA and DA evolve out as the most efficient algorithms for solving the considered CEC-2021 benchmark problems.

关键词： dragonfly algorithm Metaheuristic algorithm Optimization Nature-inspired algorithm Swarm intelligence

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PMSM Vector Control Optimization Based on Fractional PI^(λ) of Rotational Speed Outer Loop of dragonfly algorithm

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Wuhan University Journal of Natural Sciences 2021年第5期26卷 429-436页

作者： ZENG Guohui FU Xiuwei LIU Jin HUANG Bo DU Tao ZHU Xiangchen School of Electronic and Electrical Engineering Shanghai University of Engineering ScienceShanghai 201620China

Aiming at the problems of slow dynamic response and weak robustness of integer-order proportional integral(PI)controller in double closed loop vector control system of permanent magnet synchronous motor(PMSM),a method of combining dragonfly algorithm with fractional order PI control is proposed for off-line parameter tuning for the outer loop of speed of the *** parameter to be optimized is used as the spatial position of the optimal individual searching for food sources in the search space,and the error performance index integrated time and absolute error(ITAE)is used as its target fitness *** motor speed regulation performances of traditional engineering experience setting integer order PI,particle swarm optimization algorithm tuning fractional order PI and dragonfly algorithm tuning fractional order PI are compared,*** show that the fractional order PI controller optimized by dragonfly algorithm can improve the dynamic response performance of the system,reduce overshoot and enhance robustness,which proves the feasibility and superiority of the optimization strategy.

关键词： permanent magnet synchronous motor dragonfly algorithm fractional-order PI control parameter offline tuning integrated time and absolute error(ITAE)

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Solving Graph Coloring Problem Using an Enhanced Binary dragonfly algorithm

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2019年第3期10卷 23-45页

作者： Baiche, Karim Meraihi, Yassine Hina, Manolo Dulva Ramdane-Cherif, Amar Mahseur, Mohammed Univ MHamed Bougara Boumerdes Appl Automat Lab Boumerdes Algeria Univ MHamed Bougara Boumerdes Dept Automat Appl Automat Lab Boumerdes Algeria ECE Paris Sch Engn Paris France Univ Versailles St Quentin Yvelines LISV Lab Versailles France Univ Sci & Technol Houari Boumediene Bab Ezzouar Algeria

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 (Delta 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.

关键词： Binary dragonfly algorithm Chaotic Maps Combinatorial Optimization Problem dragonfly algorithm Graph Coloring Problem

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Non Dominated Sorting dragonfly algorithm For Multi-Objectives Optimal Power Flow

Non Dominated Sorting Dragonfly Algorithm For Multi-Objectiv...

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IEEE International Conference on Innovations in Power and Advanced Computing Technologies

作者： Pandya, Sundaram B. Jariwala, Hitesh R. Sardar Vallabhbhai Natl Inst Technol Elect Engn Dept Surat India

ISBN: (纸本)9781538681909

This paper shows the single and multi-objective edition of the newly projected dragonfly algorithm (DA) known as Non-Dominated Sorting dragonfly algorithm (NSDA) for the solution of multi-objectives optimal power flow problem. This projected NSDA algorithm is working in such a manner that, it primary finds all non-dominated Pareto optimal results at last end iteration number. Then crowding distance approach and fuzzy decision making technique are applied for finding the best compromise solutions among all the Pareto fronts in the dominated regions of multi-objective search spaces. The results are validated through the IEEE 30-bus test system and compared with the other latest optimization algorithms.

关键词： Non Dominated Sorting Crowding Distance approach Optimal Power Flow dragonfly algorithm Metaheuristic Methods

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Binary dragonfly algorithm for Feature Selection

Binary Dragonfly Algorithm for Feature Selection

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Mafarja, Majdi M. Eleyan, Derar Jaber, Iyad Mirjalili, Seyedali Hammouri, Abdelaziz Birzeit Univ Dept Comp Sci Birzeit West Bank Palestine Griffith Univ Sch Informat & Commun Technol Nathan Campus Brisbane Qld 4111 Australia Al Balqa Appl Univ Dept Comp Informat Syst Amman Jordan

ISBN: (纸本)9781538605271

Wrapper feature selection methods aim to reduce the number of features from the original feature set to and improve the classification accuracy simultaneously. In this paper, a wrapper-feature selection algorithm based on the binary dragonfly algorithm is proposed. dragonfly algorithm is a recent swarm intelligence algorithm that mimics the behavior of the dragonflies. Eighteen UCI datasets are used to evaluate the performance of the proposed approach. The results of the proposed method are compared with those of Particle Swarm Optimization (PSO), Genetic algorithms (GAs) in terms of classification accuracy and number of selected attributes. The results show the ability of Binary dragonfly algorithm (BDA) in searching the feature space and selecting the most informative features for classification tasks.

关键词： Feature Selection Optimization dragonfly algorithm classification

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Research on vehicle routing problem with time windows based on the dragonfly algorithm 17

Research on vehicle routing problem with time windows based ...

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IEEE 17th Int Conf on Dependable, Autonom and Secure Comp / IEEE 17th Int Conf on Pervas Intelligence and Comp / IEEE 5th Int Conf on Cloud and Big Data Comp / IEEE 4th Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)

作者： Liu, Chenlei Tao, Wenhan Zhao, Chencong Li, Xinwan Su, Yaning Sun, Zhixin Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Networ Nanjing Peoples R China Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing Peoples R China Nanjing Univ Posts & Telecommun Inst Modern Posts Nanjing Peoples R China

ISBN: (纸本)9781728130248

The vehicle routing problem(VRP) plays a key role in the logistics industry which constrains the development of the logistics industry. In order to improve the distribution efficiency in logistics distribution and reduce the empty driving rate, driving distance of vehicles, and transportation costs, this paper studies the vehicle routing problem with time window constraints (VRPTW). Firstly, based on the logistics costs factors such as freight transportation costs, warehouse resident costs, and overtime penalty costs, this paper constructs a mathematical model of vehicle routing problem with time windows considering the minimization of integrated costs. Then, according to the mathematical model, we design a way of using the dragonfly algorithm (DA) to solve the vehicle routing problem with time windows. Finally, the effectiveness and feasibility of the dragonfly algorithm in solving the problem are verified by simulation experiments.

关键词： vehicle routing problem time windows dragonfly algorithm logistics distribution

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Research on the Improved dragonfly algorithm-Based Flexible Flow-Shop Scheduling 11th

Research on the Improved Dragonfly Algorithm-Based Flexible ...

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11th International Conference on Modelling, Identification and Control (ICMIC)

作者： Han, Zhonghua Zhang, Jingyuan Lin, Shuo Liu, Chunguang Shenyang Jianzhu Univ Fac Informat & Control Engn Shenyang 110168 Liaoning Peoples R China Chinese Acad Sci Shenyang Inst Automat Dept Digital Factory Shenyang 110016 Peoples R China Chinese Acad Sci Key Lab Network Control Syst Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Inst Robot Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Inst Intelligent Mfg Shenyang 110016 Liaoning Peoples R China Journal Shenyang Jianzhu Univ Editorial Dept Shenyang Peoples R China

ISBN: (纸本)9789811504747;9789811504730

Compared with the classic flexible flow shop, limited buffer flexible flow shop may have a limited buffer production blockage, which will increase the complexity and uncertainty of the scheduling process. In order to solve the limited-buffer flexible flow-shop scheduling problem (LBFFSP), a mathematical programming model of limited buffer flexible flow shop is established and an Improved dragonfly algorithm (IDA) is proposed to solves this problem. Based on the standard dragonfly algorithm (DA), the idea of Simulated Anneal (SA) is combined to improve the ability of the algorithm to jump out local extremum and the algorithm combines the standard dragonfly algorithm (DA) with the Simulated Anneal (SA) idea to improve the ability to jump out local extremum and improve its persistence.

关键词： Flexible flow-shop Limited buffer dragonfly algorithm Simulating anneal

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Optimizing Neural Networks using dragonfly algorithm for Medical Prediction 8

Optimizing Neural Networks using Dragonfly Algorithm for Med...

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8th International Conference on Computer Science and Information Technology (CSIT)

作者： Yasen, Mais Al-Madi, Nailah Obeid, Nadim Princess Sumaya Univ Technol Dept Comp Sci Amman Jordan Univ Jordan King Abdullah II Sch IT Amman Jordan

ISBN: (纸本)9781538641521

As the early diagnoses helps in curing most diseases or in making them more bearable, it increases the need to build a good prediction model. Artificial Neural Network (ANN) is one of the evolutionary computation techniques that can be used as a prediction model for new data records. The ANN training method implemented is backpropagation which can be used in conjunction with an optimization method such as dragonfly algorithm (DA) and Artificial Bee Colony (ABC) algorithm. ABC has been used in the optimization of synaptic weights from an ANN. This research presents the use of DA as an optimization algorithm for the weights of each connection between the ANN neurons in a model called (ANN-DA). A disease prediction model that makes use of ANN-DA and takes real data as a case study is presented. We shall also present a model that makes use of ABC, ANN-ABC, in order to evaluate ANN-DA. An experimental comparison of both optimization algorithms with other well-known classifiers has been made using different metrics. The results show that ANN-DA proved its efficiency over ANN-ABC and regular ANN. Furthermore, ANN-DA results were more robust for all datasets.

关键词： Machine Learning Evolutionary Computation Artificial Bee Colony algorithm dragonfly algorithm Artificial Neural Network Optimization Disease Prediction

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UAV Path Planning based on Improved dragonfly algorithm 4

UAV Path Planning based on Improved Dragonfly Algorithm

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4th IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI)

作者： Hou, Yanjun Ke, Chenqi Chen, Siying Tang, Daoming Wu, Chenyin Minjiang Univ Coll Comp & Big Data Fuzhou Peoples R China

ISBN: (纸本)9798350374353;9798350374346

With the advancements in information technology, the adoption of UAV (unmanned aerial vehicle) in other fields to perform tasks in place of manual labor is gradually increasing. For UAV in complex environments and threat areas and other external factors, there is the problem of trajectory planning that is not easy. In this paper, we propose an efficient trajectory scheme using an improved dragonfly algorithm to propose an effective trajectory in a specified area. First, the formula of inertia weights and field radius is redefined, and the concept of congested area is introduced to improve the shortcomings of easily falling into local optimal solutions and converging too fast. With the experimental results presented for 20 evaluation functions, there is a superior performance compared to other swarm intelligence algorithms. Finally, a smoother and more efficient trajectory planning path can be obtained in designated areas with complex environments and threat areas.

关键词： UAV dragonfly algorithm congested area

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