Next Article in Journal
Diagonally Implicit Runge–Kutta Type Method for Directly Solving Special Fourth-Order Ordinary Differential Equations with Ill-Posed Problem of a Beam on Elastic Foundation
Previous Article in Journal
A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
Previous Article in Special Issue
Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning
Article Menu

Export Article

Open AccessArticle
Algorithms 2019, 12(1), 9;

Comparative Study in Fuzzy Controller Optimization Using Bee Colony, Differential Evolution, and Harmony Search Algorithms

Tijuana Institute of Technology, c.p 22379 Tijuana, Mexico
Author to whom correspondence should be addressed.
Received: 2 December 2018 / Revised: 19 December 2018 / Accepted: 23 December 2018 / Published: 27 December 2018
Full-Text   |   PDF [6751 KB, uploaded 27 December 2018]   |  


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. View Full-Text
Keywords: type-1 fuzzy logic; fuzzy controller; benchmark problems type-1 fuzzy logic; fuzzy controller; benchmark problems

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Castillo, O.; Valdez, F.; Soria, J.; Amador-Angulo, L.; Ochoa, P.; Peraza, C. Comparative Study in Fuzzy Controller Optimization Using Bee Colony, Differential Evolution, and Harmony Search Algorithms. Algorithms 2019, 12, 9.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top