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

Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22379, Mexico
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Algorithms 2017, 10(3), 101; https://doi.org/10.3390/a10030101
Received: 16 July 2017 / Revised: 18 August 2017 / Accepted: 23 August 2017 / Published: 28 August 2017
(This article belongs to the Special Issue Extensions to Type-1 Fuzzy Logic: Theory, Algorithms and Applications)
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. View Full-Text
Keywords: interval type-2 fuzzy logic; footprint uncertainty; bio-inspired algorithms; fuzzy controller interval type-2 fuzzy logic; footprint uncertainty; bio-inspired algorithms; fuzzy controller
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Olivas, F.; Amador-Angulo, L.; Perez, J.; Caraveo, C.; Valdez, F.; Castillo, O. Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers. Algorithms 2017, 10, 101.

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