A survey on dragonfly algorithm and its applications in engineering

Rahman, Chnoor M.; Rashid, Tarik A.; Alsadoon, Abeer; Bačanin, Nebojša; Fattah, Polla; Mirjalili, Seyedali

doi:10.1007/s12065-021-00659-x

Cited by 21 publications

(11 citation statements)

References 68 publications

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“…The simplicity of DA is one of the reasons that encourages researchers to use the algorithm to optimize the problems at hand. Another reason is the accuracy of the algorithm and the speed of convergence [40]. The mathematical models of each of these behaviors are as follows [34].…”

Section: Dragonfly Algorithmmentioning

confidence: 99%

Optimal PMU Placement for Türkiye 400 kV Interconnected Power System Observability with Dragonfly Algorithm

2023

Teh. vjesn.

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The Phasor Measurement Unit (PMU) is a modern measuring device built on the system for monitoring, controlling, and protecting power systems. Since the costs of PMU devices are very high, they must be placed in the system in optimum numbers and in a way that monitors the whole system. This study determined the locations and numbers of the optimal number of PMU devices that can monitor the whole system. Integer Linear Programming (ILP) and Binary Particle Swarm Optimization (BPSO) methods are proposed to solve the optimum PMU placement (OPP) problem. Then, the solution to the problem is carried out using Dragonfly Algorithm (DA), which is proposed as a new heuristic method. Solution methods were applied to the IEEE 14-Bus Test System and Türkiye 400 kV Interconnected Power System, and the results were compared. In addition, the results of the proposed methods were compared with the results of different studies in the literature. Thanks to the ILP, BPSO, and DA methods proposed in this study, it has been determined that power systems can be observed with fewer PMU devices. The DA method offers a great cost advantage as it is the method that provides a solution with 5 fewer PMU devices for the 400 kV Interconnected Power System in Türkiye.

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Section: Dragonfly Algorithmmentioning

confidence: 99%

Optimal PMU Placement for Türkiye 400 kV Interconnected Power System Observability with Dragonfly Algorithm

2023

Teh. vjesn.

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“…DA has achieved much attention among researchers from diverse fields because of its simplicity [ 36 , 37 ]. However, it needs improvement in internal memory as it directs to premature convergence to local optima [ 38 ].…”

Section: Proposed Efficiency Optimization In Induction Motormentioning

confidence: 99%

A Hybrid Dragonfly Algorithm for Efficiency Optimization of Induction Motors

Shukla¹,

Srivastava

Mirjalili

2022

Sensors

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Induction motors tend to have better efficiency on rated conditions, but at partial load conditions, when these motors operate on rated flux, they exhibit lower efficiency. In such conditions, when these motors operate for a long duration, a lot of electricity gets consumed by the motors, due to which the computational cost as well as the total running cost of industrial plant increases. Squirrel-cage induction motors are widely used in industries due to their low cost, robustness, easy maintenance, and good power/mass relation all through their life cycle. A significant amount of electrical energy is consumed due to the large count of operational units worldwide; hence, even an enhancement in minute efficiency can direct considerable contributions within revenue saving, global electricity consumption, and other environmental facts. In order to improve the efficiency of induction motors, this research paper presents a novel contribution to maximizing the efficiency of induction motors. As such, a model of induction motor drive is taken, in which the proportional integral (PI) controller is tuned. The optimal tuning of gains of a PI controller such as proportional gain and integral gain is conducted. The tuning procedure in the controller is performed in such a condition that the efficiency of the induction motor should be maximum. Moreover, the optimization concept relies on the development of a new hybrid algorithm, the so-called Scrounger Strikes Levy-based dragonfly algorithm (SL-DA), that hybridizes the concept of dragonfly algorithm (DA) and group search optimization (GSO). The proposed algorithm is compared with particle swarm optimization (PSO) for verification. The analysis of efficiency, speed, torque, energy savings, and output power is validated, which confirms the superior performance of the suggested method over the comparative algorithms employed.

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“…hybridizing the powerful properties of various algorithms is the one of the most popular mechanism that is used to improve the performance of metaheuristic algorithms [41].…”

Section: Hybridizationmentioning

confidence: 99%

Modified Fitness Dependent Optimizer for Solving Numerical Optimization Functions

Salih¹,

Mohammed²,

Abdul³

2022

IEEE Access

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The Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that was developed in 2019. It is one of the metaheuristic algorithms that has been used by researchers to solve various applications especially for engineering design problem. In this paper, a comprehensive survey conducted about FDO and its applications. Consequently, despite of having competitive performance of FDO, it has two major problems including low exploitation and slow convergence. Therefore, a modification of FDO (MFDO) is proposed for solving FDO issues. MFDO used two methods to enhance the performance of FDO: firstly, optimizing the range of weight factor between 0 and 0.2 which is used for finding fitness weight. Secondly, using sine cardinal mathematical function to update fitness weight and pace which is referred to the speed of the bees. To evaluate the performance of MFDO, 19 classical benchmark functions and CEC2019 benchmark functions are used. MFDO compared against all the enhancement of FDO and also it is compared with Grey Wolf Optimization (GWO), Chimp Optimization Algorithm (ChOA), Genetic Algorithm (GA), and Butterfly Optimization Algorithm (BOA). Statistical results proved that MFDO achieved significant performance compared to other algorithms. Finally, MFDO is used to solve three applications: Welded Beam Design (WDB), Pressure Vessel Design (PVD), and Spring Design Problem. Results proved that MFDO outperformed well in solving these applications against FDO, Gravitational Search Algorithm (GSA), GA, and Grasshopper Optimization Algorithm (GOA). .

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A survey on dragonfly algorithm and its applications in engineering

Cited by 21 publications

References 68 publications

Optimal PMU Placement for Türkiye 400 kV Interconnected Power System Observability with Dragonfly Algorithm

Optimal PMU Placement for Türkiye 400 kV Interconnected Power System Observability with Dragonfly Algorithm

A Hybrid Dragonfly Algorithm for Efficiency Optimization of Induction Motors

Modified Fitness Dependent Optimizer for Solving Numerical Optimization Functions

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