Dragonfly algorithm (DA) is a novel swarm intelligence meta-heuristic optimization algorithm inspired by the dynamic and static swarming behaviors of artificial dragonflies in nature. It has proved its effectiveness and superiority compared to several well-known meta-heuristics available in the literature. This paper presents a comprehensive review of DA and its new variants classified into modified and hybrid versions. It also describes the main diverse applications of DA in several fields and areas such as machine learning, neural network, image processing, robotics, and engineering. Finally, the paper suggests some possible interesting research on the applications and hybridizations of DA for future works.

Dragonfly algorithm: a comprehensive review and applications

Nature-inspired hybrid techniques of IWO, DA, ES, GA, and ICA, validated through a k-fold validation process predicting monthly natural gas consumption

Upgraded Whale Optimization Algorithm for fuzzy logic based vibration control of nonlinear steel structure

In this chapter, a wrapper-based feature selection algorithm is designed and substantiated based on the binary variant of Dragonfly Algorithm (BDA). DA is a successful, well-established metaheuristic that revealed superior efficacy in dealing with various optimization problems including feature selection. In this chapter we are going first present the inspirations and methamatical modeds of DA in details. Then, the performance of this algorithm is tested on a special type of datasets that contain a huge number of features with low number of samples. This type of datasets makes the optimization process harder, because of the large search space, and the lack of adequate samples to train the model. The experimental results showed the ability of DA to deal with this type of datasets better than other optimizers in the literature. Moreover, an extensive literature review for the DA is provided in this chapter.

Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection

Fuzzy logic controller is the most common and versatile control algorithm in the structural motion control. In most cases, the formulation of the fuzzy controller is based on the human knowledge and expert so the membership functions and the rule base are formulated by trials and errors. In recent years, there is an increasing interest to optimize the fuzzy logic controller with different metaheuristics and nature inspired approaches. This paper focuses on the optimization of a fuzzy controller applied to the seismically excited nonlinear buildings. In the majority of cases, this problem is formulated based on the linear behavior of the structure, however in this paper, objective functions and the performance criteria are considered with respect to the nonlinear responses of the structures. The optimization algorithm is based on the implementation of ant lion optimizer and Jaya algorithm as a hybrid method. The new method is utilized to design a fuzzy controller for two benchmark buildings with nonlinear behavior. The performance of the hybrid method is compared with various classical and advanced optimization algorithms.

Optimum design of fuzzy controller using hybrid ant lion optimizer and Jaya algorithm

Voltage stability is a major concern in power transmission systems due to mismatch between power generation and demand.  Hence maintenance of voltage profile within the acceptable limit   becomes   a challenging task.  In this paper the weakest buses for implementing the reactive power compensators are identified by eigenvalue decomposition technique on partitioned Y-admittance matrix. The size and cost of the SVC are optimized using dragonfly algorithm. The algorithm is implemented on IEEE 14 and 30 bus systems and the results obtained with and without the placement of Static VAR Compensators are compared with the results of other algorithms to show its effectiveness. The further scope of this work is to extend this to renewable energy by implementing the wind generators at the weakest buses there by reducing the electrical distance between the generators and the farthest load buses and securing the system from voltage collapse.

https://www.ijrer.com/index.php/ijrer/article/download/6933/pdf

Optimization of Size and Cost of Static VAR Compensator using Dragonfly Algorithm for Voltage Profile Improvement in Power Transmission Systems

Voltage Stability is a severe problem in power systems, which steadily reach operating limits imposed by economic and environmental conditions. Whenever there is a change in load the system voltage level changes. With the drop in voltage level, the reactive power demand increases. If the reactive power demand is not met, then it leads to further decline in bus voltage resulting in the cascading effect on neighbouring regions. Hence to maintain the voltage profile within permissible limits becomes essential. This paper reviews various methods and techniques with their advantages and limitations adopted to retain the voltage level. The review presents sensitivity based control strategies, Control based on structural characteristics of power system, secondary voltage control strategies, design and optimal placement of FACTS devices using various optimization algorithms for VAR compensation.

Voltage profile improvement in power systems - A review

Objectives: The objective is to identify weakest buses using L-index method and to reduce the maximum value of L-index using Genetic Algorithm so as to maintain the voltage profile Methods/Statistical Analysis: Among the various existing voltage stability indices, L-index is implemented in this paper since it involves simple calculation with high accuracy and reliability Hence on identifying buses with maximum L-index, it becomes essential to minimize the L-index to maintain the system stability Genetic Algorithm is chosen since it is faster and more accurate when implemented for power system problems in particular to optimize the voltage Findings: The variations to be adopted for the control variables which are thus obtained for IEEE 30 bus system by using Genetic Algorithm are highly important to reduce L-index value The voltage profile is improved better when Static Var Compensator is placed at the first weakest bus rather than the improvement in the voltage profile on employing a compensator in the next weakest buses which indicates the importance in identification of weakest buses The significance of weakest buses are still emphasised on considering normal condition and contingency condition which shows that under contingency it is the weakest buses which is much more affected and hence driving the entire system towards voltage collapse Application/Improvements: Hence by identifying the weakest buses using L-index and by minimizing the maximum value of L-index using Genetic Algorithm along with reactive power compensator improves the voltage profile better

J. Vanishree

Papers

Optimization of Size and Cost of Static VAR Compensator using Dragonfly Algorithm for Voltage Profile Improvement in Power Transmission Systems

Voltage profile improvement in power systems - A review

Minimization of L-index using Genetic Algorithm for Improvement of Voltage profile in Power Systems