A Modified Dragonfly Optimization Algorithm for Single- and Multiobjective Problems Using Brownian Motion

Acı, Çiğdem İnan; Gülcan, Hakan

doi:10.1155/2019/6871298

“…Reference [17] stated that although using the Lévy flight improves the performance of DA, however, it may cause very long steps. In the mentioned reference, to avoid this drawback, Brownian motion was used in place of Lévy flight.…”

Section: Overview Of Dragonfly Algorithmmentioning

confidence: 99%

A Survey on Dragonfly Algorithm and its Applications in Engineering

Rahman¹,

Rashid²,

Alsadoon³

et al. 2022

Preprint

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Dragonfly algorithm developed in 2016. It is one of the algorithms used by the 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 examine 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. 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 some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.

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“…This algorithm has special characteristics including the low number of setting parameters, compatible with continuous and discrete optimizations, and access to local and global operators of the search. The movement of each dragonfly is influenced by five factors, which are explained by the following [33,34].…”

Section: Improved Optimization Based On Mdamentioning

confidence: 99%

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

Lan

¹

,

Jermsittiparsert

²

,

Al-Rashood

³

et al. 2021

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Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme.

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“…Reference [17] stated that although using the Lévy flight improves the performance of DA, however, it might cause very long steps. In the mentioned reference, to avoid this drawback, Brownian motion was used in place of Lévy flight.…”

Section: = ∑ =1mentioning

confidence: 99%

A Survey on Dragonfly Algorithm and its Applications in Engineering

Rahman¹,

Rashid²,

Alsadoon³

et al. 2020

Preprint

2

0

View full text Add to dashboard Cite

Dragonfly algorithm developed in 2016. It is one of the algorithms used by the 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 examine 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. 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 some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.

show abstract

A Modified Dragonfly Optimization Algorithm for Single- and Multiobjective Problems Using Brownian Motion

Cited by 57 publications

References 38 publications

A Survey on Dragonfly Algorithm and its Applications in Engineering

A Survey on Dragonfly Algorithm and its Applications in Engineering

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

A Survey on Dragonfly Algorithm and its Applications in Engineering

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