Abstract
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.
Highlights
Bio-inspired optimization algorithms have proven that they are really good at numerical optimization and finding good results from a defined search space, no matter how many parameters they are looking for
(Figure 9) for control of the autonomous mobile robot, using the same parameters, such as (Figure 9) for control of the autonomous mobile robot, using the same parameters, such as population, population, iterations and number of experiments described in Tables 5–7 for the Particle swarm optimization (PSO), Bee Colony Optimization (BCO) and Bat Algorithm (BA)
Iterations and number of experiments described in Tables 5–7 for the PSO, BCO and BA
Summary
Bio-inspired optimization algorithms have proven that they are really good at numerical optimization and finding good results from a defined search space, no matter how many parameters they are looking for. Three bio-inspired methods are used for a comparison in the optimization of a fuzzy system used as a controller of an autonomous mobile robot. These methods are selected because they use the same methodology for parameter adaptation, as these three methods use a fuzzy system with the same inputs, but with different outputs, because the outputs are the parameters to be dynamically adjusted over the iterations of each algorithm. The Bat Algorithm is a bio-inspired metaheuristic based on the characteristics of micro-bats, essentially echolocation This echolocation is used by a bat to locate its prey; this algorithm was formulated in 2010 by Xin-She Yang in [1], and has since been used to solve global optimization problems
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