Enhanced crow search algorithm for AVR optimization

Bhullar, Amrit Kaur; Kaur, Ranjit; Sondhi, Swati

doi:10.1007/s00500-019-04640-w

Cited by 58 publications

(34 citation statements)

References 42 publications

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“…3) introducing the upper bound strategy. Likewise, Bhullar et al [106] proposed another version of CSA by 1) adding an archive component to use crow experience. 2) formulating a nonhideout position.…”

Section: ) Modified Csamentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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In this paper, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

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Section: ) Modified Csamentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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“…In this article, the linear model of each fundamental components of an AVR system is used 73,76,77,82 . The transfer function of various component of an AVR system is mentioned as below:…”

Section: Avr System: Mathematical Modelingmentioning

confidence: 99%

“…In Table 2, the lower and upper bounds of AVR system parameters K A , K E , K G , K S , τ A , τ E , τ G , and τ S are illustrated. For the comparison of simulation results, the values of AVR system parameters are adopted from Bhullar et al 73,76,77,82 and presented in Table 2.…”

Section: Avr System: Mathematical Modelingmentioning

confidence: 99%

Equilibrium optimizer tuned novel FOPID‐DN controller for automatic voltage regulator system

Paliwal

Srivastava

Pandit

2021

Int Trans Electr Energ Syst

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Summary This paper presents a novel fractional‐order PID plus derivative with filter coefficient (FOPID‐DN) controller for automatic voltage regulator (AVR) system using the MATLAB/Simulink environment. An AVR system is employed in the power system to maintain the terminal voltage at the desired level. Parameters of the proposed FOPID‐DN controller are optimally tuned by the recently developed Equilibrium Optimizer (EO) algorithm. Performance of the designed FOPID‐DN controller for an AVR system is compared with various controllers which are optimally tuned by different optimization algorithms. Comparative transient response analysis of the proposed technique has been carried out by considering vital transient response indicators like maximum overshoot, rise time, and settling time. Performance analysis of the proposed technique for an AVR system has been done by considering several input conditions. Robustness analysis of the EO algorithm tuned FOPID‐DN controller has been performed by varying the time constants of different components in an AVR system. The capability of the FOPID‐DN controller to handle nonlinearities of an AVR system has been investigated. The stability of the FOPID‐DN controlled AVR system has been analyzed. In addition, the disturbance rejection capability of the FOPID‐DN controlled AVR system has also been investigated. From the various simulation results obtained using the MATLAB/Simulink environment, it has been observed that the proposed FOPID‐DN‐based AVR system provided better performance as compared to different existing controllers.

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“…The optimal design of structures is the main topic among structural engineers (Mashayekhi et al 2012(Mashayekhi et al , 2016Gholizadeh 2013;Khatibinia and Yazdani 2018;Bhullar et al 2020). In this section, three design problems are chosen for truss structures.…”

Section: Structural Problemsmentioning

confidence: 99%

An enhanced approach for optimizing mathematical and structural problems by combining PSO, GSA and gradient directions

Salajegheh

Shojaee

2021

Preprint

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In this paper, the combination of particle swarm optimization (PSO) and gravitational search algorithm (GSA) is enhanced by the first-order gradient method and a new optimization algorithm is introduced as GPSG. In metaheuristic methods, some search directions are selected at random and the resulting points gradually progress toward the optimal. Since the gradient direction usually has the largest decrease in the desired function, it is added to the GSA and PSO process to allow for faster and more accurate convergence. By integrating the metaheuristic methods with the gradient directions, a powerful method for optimizing functions has been made possible. Numerous examples of unconstrained problems of mathematical functions of CEC2005 and constrained examples of stress and displacement structural design problems have been chosen to demonstrate the reliability and capability of the presented method.

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Enhanced crow search algorithm for AVR optimization

Cited by 58 publications

References 42 publications

Crow Search Algorithm: Theory, Recent Advances, and Applications

Crow Search Algorithm: Theory, Recent Advances, and Applications

Equilibrium optimizer tuned novel FOPID‐DN controller for automatic voltage regulator system

An enhanced approach for optimizing mathematical and structural problems by combining PSO, GSA and gradient directions

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