Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers

Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers

In this paper, a comparison among Particle swarm optimization (PSO), Bee Colony Optimization (BCO) and the Bat Algorithm (BA) is presented. In addition, a modification to the main parameters of each algorithm through an interval type-2 fuzzy logic system is presented. The main aim of using interval...

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Bibliographic Details
Main Authors: Frumen Olivas, Leticia Amador-Angulo, Jonathan Perez, Camilo Caraveo, Fevrier Valdez, Oscar Castillo
Format: Article
Language:English
Published: MDPI AG 2017-08-01
Series:Algorithms
Subjects:
interval type-2 fuzzy logic
footprint uncertainty
bio-inspired algorithms
fuzzy controller
Online Access:https://www.mdpi.com/1999-4893/10/3/101
Description
Summary:In this paper, a comparison among Particle swarm optimization (PSO), Bee Colony Optimization (BCO) and the Bat Algorithm (BA) is presented. In addition, a modification to the main parameters of each algorithm through an interval type-2 fuzzy logic system is presented. The main aim of using interval type-2 fuzzy systems is providing dynamic parameter adaptation to the algorithms. These algorithms (original and modified versions) are compared with the design of fuzzy systems used for controlling the trajectory of an autonomous mobile robot. Simulation results reveal that PSO algorithm outperforms the results of the BCO and BA algorithms.
ISSN:1999-4893

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