Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment

Fevrier Valdez,Emer Bernal,Oscar Castillo,José Soria

doi:10.3390/axioms8010026

Fevrier Valdez, Emer Bernal + Show 2 more

Open Access

https://doi.org/10.3390/axioms8010026

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Journal: Axioms	Publication Date: Feb 25, 2019
Citations: 31	License type: CC BY 4.0

Affiliation: Instituto Tecnológico de Tijuana

Abstract

Galactic swarm optimization (GSO) is a recently created metaheuristic which is inspired by the motion of galaxies and stars in the universe. This algorithm gives us the possibility of finding the global optimum with greater precision since it uses multiple exploration and exploitation cycles. In this paper we present a modification to galactic swarm optimization using type-1 (T1) and interval type-2 (IT2) fuzzy systems for the dynamic adjustment of the c3 and c4 parameters in the algorithm. In addition, the modification is used for the optimization of the fuzzy controller of an autonomous mobile robot. First, the galactic swarm optimization is tested for fuzzy controller optimization. Second, the GSO algorithm with the dynamic adjustment of parameters using T1 fuzzy systems is used for the optimization of the fuzzy controller of an autonomous mobile robot. Finally, the GSO algorithm with the dynamic adjustment of parameters using the IT2 fuzzy systems is applied to the optimization of the fuzzy controller. In the proposed approaches, perturbation (noise) was added to the plant in order to find out if our approach behaves well under perturbation to the autonomous mobile robot plant; additionally, we consider our ability to compare the results obtained with the approaches when no perturbation is considered.

Highlights

The main goal of the optimization methods is to find the best solution of a set of possible solutions to a problem; for some cases, the search space is very extensive, which leads to a computational cost that is too high
This paper presents a modification to the Galactic swarm optimization (GSO) metaheuristic with the use of type-1 (T1) and interval type-2 (IT2) fuzzy logic for the parameter adjustment of the GSO algorithm
IT2 fuzzy systems are used for the dynamic adjustment of the c and c4 parameters used in the galactic swarm optimization

Summary

Introduction

The main goal of the optimization methods is to find the best solution of a set of possible solutions to a problem; for some cases, the search space is very extensive, which leads to a computational cost that is too high. To solve this problem there are different computational intelligence techniques that provide tools to solve these optimization problems [1]. Fuzzy systems convert a knowledge base into a mathematical model that has been shown to be effective in many applications in industry and real life [5,6]

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