Comparative Study in Fuzzy Controller Optimization Using Bee Colony, Differential Evolution, and Harmony Search Algorithms
Oscar Castillo,Fevrier Valdez,José Soria,Leticia Amador-Angulo,Patricia Ochoa,Cinthia Peraza +5 more
TLDR
Simulation results provide evidence that the FDE algorithm outperforms the results of the FBCO and FHS algorithms in the optimization of fuzzy controllers and the better errors are found with the implementation of the fuzzy systems to enhance each proposed algorithm.Abstract:
This paper presents a comparison among the bee colony optimization (BCO), differential evolution (DE), and harmony search (HS) algorithms. In addition, for each algorithm, a type-1 fuzzy logic system (T1FLS) for the dynamic modification of the main parameters is presented. The dynamic adjustment in the main parameters for each algorithm with the implementation of fuzzy systems aims at enhancing the performance of the corresponding algorithms. Each algorithm (modified and original versions) is analyzed and compared based on the optimal design of fuzzy systems for benchmark control problems, especially in fuzzy controller design. Simulation results provide evidence that the FDE algorithm outperforms the results of the FBCO and FHS algorithms in the optimization of fuzzy controllers. Statistically is demonstrated that the better errors are found with the implementation of the fuzzy systems to enhance each proposed algorithm.read more
Citations
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Optimization of fuzzy controller design using a Differential Evolution algorithm with dynamic parameter adaptation based on Type-1 and Interval Type-2 fuzzy systems
TL;DR: In this paper, four control optimization problems in which the Differential Evolution algorithm optimizes the membership functions of the fuzzy controllers are presented.
Journal ArticleDOI
Optimize TSK Fuzzy Systems for Classification Problems: Minibatch Gradient Descent With Uniform Regularization and Batch Normalization
Yuqi Cui,Dongrui Wu,Jian Huang +2 more
TL;DR: A minibatch gradient descent (MBGD) based algorithm to efficiently and effectively train TSK fuzzy classifiers is proposed, which integrates two novel techniques: first, uniform regularization (UR), which forces the rules to have similar average contributions to the output, and hence to increase the generalization performance of the TSK classifier.
Journal ArticleDOI
Optimal designing of static var compensator to improve voltage profile of power system using fuzzy logic control
Amirreza Naderipour,Zulkurnain Abdul-Malek,Foad H. Gandoman,Saber Arabi Nowdeh,Mohsen Aghazadeh Shiran,Mohammad Jafar Hadidian Moghaddam,Iraj Faraji Davoodkhani +6 more
TL;DR: The results show that in case of EAF load, the SVC is capable of rapidly compensating its effect and improve the system power quality, and the proposed SVC based fuzzy method has good performance in reduction of harmonic currents and improving the voltage profile of the system.
Journal ArticleDOI
Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning
Alaa A. Alomoush,AbdulRahman A. Alsewari,Hammoudeh S. Alamri,Khalid S. Aloufi,Kamal Z. Zamli +4 more
TL;DR: A hybrid algorithm of HS with grey wolf optimizer (GWO) has been developed to solve the problem of HS parameter selection and it is shown that the GWO-HS is superior over the old HS variants and other well-known metaheuristics in terms of accuracy and speed process.
Journal ArticleDOI
Electronically Tunable ACO Based Fuzzy FOPID Controller for Effective Speed Control of Electric Vehicle
TL;DR: In this article, a novel fuzzy fractional order PID (FOPID) controller using Ant Colony Optimization (ACO) algorithm has been proposed to control EV speed effectively, which is verified using the new European driving cycle (NEDC) test in the MATLAB-Simulink platform.
References
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