Dragonfly Algorithm and Its Applications in Applied Science Survey

Rahman, Chnoor M.; Rashid, Tarik A.

doi:10.1155/2019/9293617

Cited by 107 publications

(64 citation statements)

References 60 publications

(96 reference statements)

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“…Hence, the hybrid algorithm-MHDA provided better performance compared to the original DA. The reason for this was that the MHDA provided excellent stability between exploration and exploitation *This survey is different from our survey [88]. In this survey, we present a comprehensive investigation of DA in the engineering areas.…”

Section: Iteration Level Hybridization With Psomentioning

confidence: 87%

“…The calculated complexity proved that using Brownian motion did not have an impact on the complexity time of the original DA. By using the Brownian motion, the massive jumps caused by the Lévy flight were *This survey is different from our survey [88]. In this survey, we present a comprehensive investigation of DA in the engineering areas.…”

Section: Overview Of Dragonfly Algorithmmentioning

confidence: 96%

“…The work describes the Dragonfly algorithm, its variants, modifications, hybridizations in the following engineering areas: Mechanical Engineering, Electrical Engineering, Optimal Parameters, Economic Load Dispatch and Loss Reduction. However, our survey [88] is more into Dragonfly Algorithm and its Applications in Applied Science. The work in [88] describes the Dragonfly algorithm, its variants, modifications, hybridizations in the following applied science areas: Machine Learning, Image Processing, Wireless, and Networking.…”

Section: Overview Of Dragonfly Algorithmmentioning

confidence: 99%

“…When the segments are created, for every segment a roulette-wheel mechanism with the following probability is used for the selection process [23]. = (15) *This survey is different from our survey [88]. In this survey, we present a comprehensive investigation of DA in the engineering areas.…”

Section: The Da For Multi-objective Problemsmentioning

confidence: 99%

“…The settings of parameters are shown in Table 1. [88]. In this survey, we present a comprehensive investigation of DA in the engineering areas.…”

Section: B a Hybrid Dragonfly Algorithm With Extreme Learning Machinmentioning

confidence: 99%

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OUP accepted manuscript

2020

Journal of Computational Design and Engineering

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Dragonfly algorithm (DA) is one of the most recently developed heuristic optimization algorithms by Mirjalili in 2016. It is now one of the most widely used algorithms. In some cases, it outperforms the most popular algorithms. However, this algorithm is not far from obstacles when it comes to complex optimization problems. In this work, along with the strengths of the algorithm in solving real-world optimization problems, the weakness of the algorithm to optimize complex optimization problems is addressed. This survey presents a comprehensive investigation of DA in the engineering area. First, an overview of the algorithm is discussed. Additionally, the different variants of the algorithm are addressed too. The combined versions of the DA with other techniques and the modifications that have been done to make the algorithm work better are shown. Besides, a survey on applications in engineering area that used DA is offered. The algorithm is compared with some other metaheuristic algorithms to demonstrate its ability to optimize problems comparing to the others. The results of the algorithm from the works that utilized the DA in the literature and the results of the benchmark functions showed that in comparison with some other algorithms DA has an excellent performance, especially for small to medium problems. Moreover, the bottlenecks of the algorithm and some future trends are discussed. Authors conduct this research with the hope of offering beneficial information about the DA to the researchers who want to study the algorithm and utilize it to optimize engineering problems.

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Section: Iteration Level Hybridization With Psomentioning

confidence: 87%

Section: Overview Of Dragonfly Algorithmmentioning

confidence: 96%

Section: Overview Of Dragonfly Algorithmmentioning

confidence: 99%

Section: The Da For Multi-objective Problemsmentioning

confidence: 99%

“…The settings of parameters are shown in Table 1. [88]. In this survey, we present a comprehensive investigation of DA in the engineering areas.…”

Section: B a Hybrid Dragonfly Algorithm With Extreme Learning Machinmentioning

confidence: 99%

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OUP accepted manuscript

2020

Journal of Computational Design and Engineering

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Critical review of bio‐inspired optimization techniques

Johnvictor

Vaishali

Venkata

et al. 2020

WIREs Computational Stats

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In today's world of engineering evolution, the need for optimized design has led to development of a plethora of optimization algorithms. Right from hardware engineering design problems that need optimization of design parameters to software applications that require reduction of data sets, optimization algorithms play a vital role. These algorithms are either based on statistical measures or on heuristics. Traditional optimization algorithms use statistical methods in which the optimal solution may not be the global minimal point. These standard optimization techniques are more application specific and demand different parameter sets for different applications. Rather, the bio‐inspired meta‐heuristic algorithms act like black boxes enabling multiple applications with definite global optimal solutions. This review work gives an insight of various bio‐inspired optimization algorithms including dragonfly algorithm, the whale optimization algorithm, gray wolf optimizer, moth‐flame optimization algorithm, cuckoo optimization algorithm, artificial bee colony algorithm, ant colony optimization, grasshopper optimization algorithm, binary bat algorithm, salp algorithm, and the ant lion optimizer. The biological behaviors of the living things that lead to modeling of these algorithms have been discussed in detail. The parametric setting of each algorithm has been studied and their evaluation with benchmark test functions has been reviewed. Also their application to real‐world engineering design problems has been discussed. Based on these characteristics, the possibility to extend these algorithms to data set optimization, feature set reduction, or optimization has been discussed. This article is categorized under: Algorithms and Computational Methods > Algorithms Algorithms and Computational Methods > Computational Complexity Algorithms and Computational Methods > Genetic Algorithms and Evolutionary Computing

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Critical review of bio‐inspired data optimization techniques: An image steganalysis perspective

Johnvictor

Amalanathan

Venkata

et al. 2022

WIREs Data Min & Knowl

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Image steganalysis involves the discovery of secret information embedded in an image. The common method is blind image steganalysis, which is a twoclass classification problem. Blind steganalysis extracts all possible feature variations in an image due to embedding, select the most appropriate feature data, and then classifies the image. The dimensionality of the extracted image features are high and demand data reduction to identify the most relevant features and to aid accurate classification of an image. The classification is under two classes namely, clean (cover) image and stego (image with embedded

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Dragonfly Algorithm and Its Applications in Applied Science Survey

Cited by 107 publications

References 60 publications

OUP accepted manuscript

OUP accepted manuscript

Critical review of bio‐inspired optimization techniques

Critical review of bio‐inspired data optimization techniques: An image steganalysis perspective

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