New Reconfiguration Method for Improving Voltage Profile of Distribution Networks

Vizhiy, S. Aruul; Santhi, R. K.

doi:10.5120/ijca2016908460

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…It also kept the radial structure. In this study, the results obtained using DORM compared to some other nature-inspired algorithms for solving configuration problems, such as PSO [31], GA [32], and BBO [33]. According to the study, the obtained results proved that the DORM provided better configuration that minimized NVD and provided good VP.…”

Section: A Mechanical Engineeringmentioning

confidence: 84%

A Survey on Dragonfly Algorithm and its Applications in Engineering

Rahman¹,

Rashid²,

Alsadoon³

et al. 2020

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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Section: A Mechanical Engineeringmentioning

confidence: 84%

A Survey on Dragonfly Algorithm and its Applications in Engineering

Rahman¹,

Rashid²,

Alsadoon³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Section: A Mechanical Engineeringmentioning

confidence: 99%

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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