A Novel Optimal Combined Fuzzy PID Controller Employing Dragonfly Algorithm for Solving Automatic Generation Control Problem

Kouba, Nour El Yakine; Menaa, Mohamed; Hasni, Mourad; Boudour, Mohamed

doi:10.1080/15325008.2018.1533604

Cited by 30 publications

(10 citation statements)

References 38 publications

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“…In [37], DA has been applied to the economic load dispatch problem in power systems, which consists of minimizing the generation cost while satisfying constraints such as ramp rate, demand and generator operating limit; in [38], it has been applied to the static economic dispatch problem incorporating solar energy; in [39], it has been applied to the combined economic emission dispatch problem, and in [40], it has been applied to the dynamic economic dispatch problem. In [41,42], DA has been applied to the automatic generation control problem to optimize the gains of the controller. Furthermore, DA has been used in power transmission systems [43] for optimizing the size and cost of static var compensator, in PV panel [44] to maximize the tracking of solar power, in photovoltaic system [45] to track the global maximum power point (GMPP) and in Photovoltaic-biomass system [46] to find the optimal size and cost of grid-integrated renewable energy resources.…”

Section: Electrical Engineeringmentioning

confidence: 99%

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Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Emambocus

Jasser

Amphawan

2021

Sensors

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Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.

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Section: Electrical Engineeringmentioning

confidence: 99%

“…Figure 7 shows how the dragonfly algorithm is used for solving the automatic generation control problem in [41] by optimizing the parameters of the fuzzy PID controller.…”

Section: Electrical Engineeringmentioning

confidence: 99%

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Emambocus

Jasser

Amphawan

2021

Sensors

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“…Atomic generation control (AGC) problem was examined in reference [64] by using DA. In this work, the DA optimized the control parameters, such as PID gains and scaling factors of fuzzy logic.…”

Section: B Electrical 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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“…Atomic generation control (AGC) problem was examined in reference [51] by using DA. In this work, the DA optimized the control parameters, for example, scaling factors of fuzzy logic and PID gains.…”

Section: B Electrical Engineeringmentioning

confidence: 99%

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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A Novel Optimal Combined Fuzzy PID Controller Employing Dragonfly Algorithm for Solving Automatic Generation Control Problem

Cited by 30 publications

References 38 publications

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

OUP accepted manuscript

A Survey on Dragonfly Algorithm and its Applications in Engineering

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