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

Oscar Castillo,José Soria,Fevrier Valdez,Cinthia Peraza,Patricia Ochoa,Leticia Amador-Angulo

doi:10.3390/a12010009

Oscar Castillo, José Soria + Show 4 more

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https://doi.org/10.3390/a12010009

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Journal: Algorithms	Publication Date: Dec 27, 2018
Citations: 48	License type: CC BY 4.0

Affiliation: Instituto Tecnológico de Tijuana

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.

Highlights

We realized a study to determine the optimal algorithm with a better error in the simulations, this will allow to have a generalization of which algorithm allows to find better errors in control problems, in this case Fuzzy Harmony Search Algorithm (FHS) presents excellent results compared to Fuzzy Bee Colony Optimization Algorithm (FBCO)
There is no work in which these three algorithms (DE, harmony search (HS), and bee colony (BCO)) have been used to make a comparison with control problems, the idea of conducting this work was to compare the performance of the three algorithms
The main contribution of this paper is the improvement that is made to each of the algorithms using fuzzy logic, which dynamically moves the parameters of the membership functions of the control problem

Summary

Introduction

Nowadays meta-heuristic algorithms are used to solve various kinds of optimization problems, and this work is based on three particular algorithms, which are the BCO, DE, and HS algorithms.In the last decade the BCO has proven to be an excellent technique in the optimization of nondeterministic polynomial time problems (NP-Problems) [1], like the following: fuzzy controllers [2,3,4,5], and general engineering problems [6,7,8].The HS algorithm is inspired by the process of jazz improvisation, and various problems like the optimization of neural networks [9,10,11], benchmark functions [12,13,14], benchmark control [15], and engineering problems [16,17,18,19] have been successfully solved with this algorithm.The DE algorithm belongs to the category of evolutionary computation. Nowadays meta-heuristic algorithms are used to solve various kinds of optimization problems, and this work is based on three particular algorithms, which are the BCO, DE, and HS algorithms. In the last decade the BCO has proven to be an excellent technique in the optimization of nondeterministic polynomial time problems (NP-Problems) [1], like the following: fuzzy controllers [2,3,4,5], and general engineering problems [6,7,8]. The DE algorithm belongs to the category of evolutionary computation. It efficiently solves nonlinear, non-differentiable and multimodal problems, and is used in the solution of complex problems [20]. There are works that combine fuzzy logic with DE [21,22,23] and some control problem applications [24,25,26]

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