Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system

Gözde, Haluk; Taplamacıoğlu, M. Cengiz

doi:10.1016/j.jfranklin.2011.05.012

Cited by 240 publications

(194 citation statements)

References 19 publications

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“…Well-known analysis techniques such as root locus, transient response, and Bode analysis are used in our study. Obtained results are compared with reference [1] and the ABC, PSO, and DEA algorithms. The robustness analysis of this algorithm is also tested under different working circumstances and compared with the ABC algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…In recent power systems, the output voltage of a generator is commonly detected with automatic voltage regulator (AVR) systems. At the same time, it initiates corrective action by adjusting the exciter control in a definite direction [1][2][3]. The AVR uses the exciter voltage of a generator to handle the terminal voltage.…”

Section: Introductionmentioning

confidence: 99%

“…Later on, several methods such as the intelligent particle swarm algorithm [8], chaotic ant swarm optimization [9], and discrete action reinforcement learning automata [10] were suggested. Gozde and Taplamacioglu [1] proposed the artificial bee colony (ABC) algorithm with the aim of improving the performance of the self-tuning PID controller in this system. In the performance analysis, they compared the results with the differential evolution (DE) algorithm and PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis of biogeography-based optimization for automatic voltage regulator system

Güvenç¹,

Yi̇ği̇t²,

Işık³

et al. 2016

Turk J Elec Eng & Comp Sci

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A self-tuning method to determine the appropriate parameters of a proportional-integral-derivative controller for an automatic voltage regulator (AVR) system using a biogeography-based optimization (BBO) algorithm is proposed in this study. The BBO algorithm was developed based on the theory of biogeography, which describes migration and its results. We propose that the BBO algorithm has a high-quality solution and stable convergence characteristics, and thus it improves the transient response of the controlled system. The performance of the BBO algorithm depends on the transient response, root locus, and Bode analysis. Robustness analysis is done in the AVR system, which is tuned by an artificial bee colony (ABC) algorithm in order to identify its response to changes in the system parameters. We compare the BBO algorithm with the ABC algorithm, particle swarm optimization algorithm, and differential evolution algorithm. The results of this comparison show that the BBO algorithm has a better tuning capability than the other optimization algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance analysis of biogeography-based optimization for automatic voltage regulator system

Güvenç¹,

Yi̇ği̇t²,

Işık³

et al. 2016

Turk J Elec Eng & Comp Sci

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show abstract

“…The ABC algorithm performs better on a number of benchmark functions than many other optimization algorithms, such as particle swarm optimization (PSO) or differential evolution (DE) [14][15][16]. Moreover, the ABC algorithm was already successfully applied in numerous engineering applications [2,[17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Multiple-global-best guided artificial bee colony algorithm for induction motor parameter estimation

Abro¹,

Mohamad–Saleh²

2014

Turk J Elec Eng & Comp Sci

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Abstract:An induction motor is the most commonly used motor in industry today. Motor circuit parameters are essential for designing, evaluating performance, and controlling the applications of the motor. However, it is difficult to measure the electric parameters, e.g., resistance and reactance, of induction motors accurately. Therefore, researchers have noted the parameter estimation of induction motors as an essential optimization problem. The artificial bee colony (ABC) algorithm is an efficient element of bioinspired optimization algorithms and has been successfully applied in numerous engineering applications. However, the ABC algorithm suffers from slow convergence and poor exploitation. Additionally, there are bleak chances of getting a fitter food source for scout bees using the the standard ABC algorithm scheme.Therefore, different solutions have already been proposed to avoid the flaws of the ABC algorithm. Nevertheless, the proposed solutions are either computationally intensive or prone to local optima traps or they require additional control variables to tune. Moreover, there is no systematic way to tune the additional control variables for yielding the optimal performance of the algorithms. Therefore, this research work proposes a novel variant of the ABC algorithm, which capitalizes on multiple global-best food sources rather than a single global-best food source. In addition, this research work proposes a novel scheme for enhancing the performance of the ABC algorithm's scout bee. Two modifications for the performance enhancement of the ABC algorithm are proposed in this research work. The proposed algorithm is compared with various recently proposed variants of the ABC algorithm and various other available methods for estimating induction motor parameters. The performance of the proposed algorithm is also assessed using the chaotic map initialization technique. The results prove that the proposed algorithm is able to achieve the best convergence among all of the compared algorithms.

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“…But actual fuel cost functions are non-linear, nonconvex, non differentiable and may have multiple local minima [3]. Recently some heuristic techniques such as genetic algorithm [4], ant colony search algorithm [5], evolutionary programming [6], improved tabu search [7], differential evolution [8], particle swarm optimization [9] and artificial bee colony algorithm [10] have been used to solve the complex non-linear optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Methods for Economic Load Dispatch Problems

Hardiansyah¹,

Junaidi²,

Ms³

2012

IJCA

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Economic load dispatch problem is an optimization problem where objective function is highly non linear, non-convex, non differentiable and may have multiple local minima. Therefore, classical optimization methods may not converge or get trapped to any local minima. This paper presents a comparative study of three different evolutionary algorithms i.e. differential evolution, artificial bee colony algorithm and particle swarm optimization for solving the economic load dispatch problem. All the methods are tested on 3-units and 6-units test system. Simulation results are presented to show the comparative performance of these methods.

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Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system

Cited by 240 publications

References 19 publications

Performance analysis of biogeography-based optimization for automatic voltage regulator system

Performance analysis of biogeography-based optimization for automatic voltage regulator system

Multiple-global-best guided artificial bee colony algorithm for induction motor parameter estimation

Application of Soft Computing Methods for Economic Load Dispatch Problems

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