Comparative study of type-2 fuzzy Particle swarm, Bee Colony and Bat Algorithms in optimization of fuzzy controllers

被引：44
|
作者：
Olivas F. [1 ]
Amador-Angulo L. [1 ]
Perez J. [1 ]
Caraveo C. [1 ]
Valdez F. [1 ]
Castillo O. [1 ]
机构：
[1] Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana
来源：
Castillo, Oscar (ocastillo@tectijuana.mx) | 1600年 / MDPI AG卷 / 10期
关键词：
Bio-inspired algorithms; Footprint uncertainty; Fuzzy controller; Interval type-2 fuzzy logic;
D O I：
10.3390/a10030101
中图分类号：
学科分类号：
摘要：
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. © 2017 by the authors.
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