A survey on dragonfly algorithm and its applications in engineering

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.

[1]  Andrew Lewis,et al.  Novel performance metrics for robust multi-objective optimization algorithms , 2015, Swarm Evol. Comput..

[2]  Aboul Ella Hassanien,et al.  Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection , 2018, Applied Intelligence.

[3]  Aman Jantan,et al.  A Cognitively Inspired Hybridization of Artificial Bee Colony and Dragonfly Algorithms for Training Multi-layer Perceptrons , 2018, Cognitive Computation.

[4]  Nihad Dib,et al.  Circular antenna array synthesis using firefly algorithm , 2014 .

[5]  Velamuri Suresh,et al.  Generation dispatch of combined solar thermal systems using dragonfly algorithm , 2016, Computing.

[6]  R. K. Santhi,et al.  New Reconfiguration Method for Improving Voltage Profile of Distribution Networks , 2016 .

[7]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[8]  Aboul Ella Hassanien,et al.  Binary ant lion approaches for feature selection , 2016, Neurocomputing.

[9]  M. Haghifam,et al.  Adaptive multi-objective distribution network reconfiguration using multi-objective discrete particles swarm optimisation algorithm and graph theory , 2013 .

[10]  Banaja Mohanty,et al.  Optimized 2DOF PID for AGC of Multi-area Power System Using Dragonfly Algorithm , 2019 .

[11]  Tyrone Fernando,et al.  Sequential quadratic programming particle swarm optimization for wind power system operations considering emissions , 2013 .

[12]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[13]  S. SreeRanjiniK.,et al.  Expert Systems With Applications , 2022 .

[14]  Chnoor M. Rahman,et al.  A new evolutionary algorithm: Learner performance based behavior algorithm , 2020, Egyptian Informatics Journal.

[15]  P. Aravindhababu,et al.  Dragonfly Optimization based Reconfiguration for Voltage Profile Enhancement in Distribution Systems , 2017 .

[16]  Fariborz Jolai,et al.  Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm , 2016, J. Comput. Des. Eng..

[17]  Ismail Musirin,et al.  Power System Voltage Stability Assessment Using a Hybrid Approach Combining Dragonfly Optimization Algorithm and Support Vector Regression , 2018, Arabian Journal for Science and Engineering.

[18]  Soheyl Khalilpourazari,et al.  Optimization of time, cost and surface roughness in grinding process using a robust multi-objective dragonfly algorithm , 2018, Neural Computing and Applications.

[19]  Tarik A. Rashid,et al.  A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm , 2019, Comput. Intell. Neurosci..

[20]  Sakti Prasad Ghoshal,et al.  Design of Concentric Circular Antenna Array with Central Element Feeding Using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach and Evolutionary Programing Technique , 2010 .

[21]  Mohammad Jafari,et al.  Using dragonfly algorithm for optimization of orthotropic infinite plates with a quasi-triangular cut-out , 2017 .

[22]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[23]  Xin-She Yang.  Harmony Search as a Metaheuristic Algorithm , 2009 .

[24]  Jie Li,et al.  Wind-Solar-Hydro power optimal scheduling model based on multi-objective dragonfly algorithm , 2019, Energy Procedia.

[25]  Bilal Babayigit,et al.  Synthesis of concentric circular antenna arrays using dragonfly algorithm , 2018 .

[26]  W Wongsinlatam,et al.  The Comparison between Dragonflies Algorithm and Fireflies Algorithm for Court Case Administration: A Mixed Integer Linear Programming , 2018 .

[27]  Dan Simon,et al.  Hybrid biogeography-based evolutionary algorithms , 2014, Eng. Appl. Artif. Intell..

[28]  Paul M. Weaver,et al.  Analysis and benchmarking of meta-heuristic techniques for lay-up optimization , 2010 .

[29]  Hossam Faris,et al.  Binary multi-verse optimization algorithm for global optimization and discrete problems , 2019, Int. J. Mach. Learn. Cybern..

[30]  Andrew Lewis,et al.  S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization , 2013, Swarm Evol. Comput..

[31]  Chandan Chakraborty,et al.  Statistical analysis of mammographic features and its classification using support vector machine , 2010, Expert Syst. Appl..

[32]  Anh Tuan Nguyen,et al.  A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems , 2016 .

[33]  Koushik Guha,et al.  Novel analytical model for optimizing the pull-in voltage in a flexured MEMS switch incorporating beam perforation effect , 2017 .

[34]  Ahmed A. Zaki Diab,et al.  Optimal Sizing and Placement of Capacitors in Radial Distribution Systems Based on Grey Wolf, Dragonfly and Moth–Flame Optimization Algorithms , 2018, Iranian Journal of Science and Technology, Transactions of Electrical Engineering.

[35]  Tarik A. Rashid,et al.  A multi hidden recurrent neural network with a modified grey wolf optimizer , 2019, PloS one.

[36]  Xiaoyan Sun,et al.  Variable-Size Cooperative Coevolutionary Particle Swarm Optimization for Feature Selection on High-Dimensional Data , 2020, IEEE Transactions on Evolutionary Computation.

[37]  Chinmay Chakraborty,et al.  Chronic Wound Characterization using Bayesian Classifier under Telemedicine Framework , 2016, Int. J. E Health Medical Commun..

[38]  Xin-She Yang,et al.  Cuckoo Search and Firefly Algorithm , 2014 .

[39]  Dipayan Guha,et al.  Optimal tuning of 3 degree-of-freedom proportional-integral-derivative controller for hybrid distributed power system using dragonfly algorithm , 2018, Comput. Electr. Eng..

[40]  Nikhil Gupta,et al.  Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms , 2014 .

[41]  Chnoor M. Rahman,et al.  Dragonfly Algorithm and Its Applications in Applied Science Survey , 2019, Comput. Intell. Neurosci..

[42]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[43]  Tarik A. Rashid,et al.  Donkey and Smuggler Optimization Algorithm: A Collaborative Working Approach to Path Finding , 2019, J. Comput. Des. Eng..

[44]  Cigdem Inan Aci,et al.  A Modified Dragonfly Optimization Algorithm for Single- and Multiobjective Problems Using Brownian Motion , 2019, Comput. Intell. Neurosci..

[45]  Nihad Dib,et al.  Design of planar concentric circular antenna arrays with reduced side lobe level using symbiotic organisms search , 2017, Neural Computing and Applications.

[46]  Chinmay Chakraborty,et al.  Chronic Wound Image Analysis by Particle Swarm Optimization Technique for Tele-Wound Network , 2017, Wirel. Pers. Commun..

[47]  Hossam Faris,et al.  Binary dragonfly optimization for feature selection using time-varying transfer functions , 2018, Knowl. Based Syst..

[48]  Samia Nefti-Meziani,et al.  A Comprehensive Review of Swarm Optimization Algorithms , 2015, PloS one.

[49]  Luca Maria Gambardella,et al.  Principles and applications of swarm intelligence for adaptive routing in telecommunications networks , 2010, Swarm Intelligence.

[50]  Mahmood Reza Azizi,et al.  Constrained grinding optimization for time, cost, and surface roughness using NSGA-II , 2014 .

[51]  Vivek K. Patel,et al.  Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization , 2016, J. Comput. Des. Eng..

[52]  Yudong Zhang,et al.  A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications , 2015 .