A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot

•Central idea is to avoid or slow down full convergence through the dynamic variation of parameters.•Performance of different ACO variants was observed to choose one as the basis to the proposed approach.•Convergence fuzzy controller with the objective of maintaining diversity to avoid premature con...

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Bibliographic Details
Published in:Applied soft computing 2015-03, Vol.28, p.150-159
Main Authors: Castillo, Oscar, Neyoy, Héctor, Soria, José, Melin, Patricia, Valdez, Fevrier
Format: Article
Language:English
Subjects:
ACO
Fuzzy ACO
Fuzzy control
Fuzzy logic
Robotics
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Summary:•Central idea is to avoid or slow down full convergence through the dynamic variation of parameters.•Performance of different ACO variants was observed to choose one as the basis to the proposed approach.•Convergence fuzzy controller with the objective of maintaining diversity to avoid premature convergence was created. Ant Colony Optimization is a population-based meta-heuristic that exploits a form of past performance memory that is inspired by the foraging behavior of real ants. The behavior of the Ant Colony Optimization algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired optimization algorithms. The present paper explores a new fuzzy approach for diversity control in Ant Colony Optimization. The main idea is to avoid or slow down full convergence through the dynamic variation of a particular parameter. The performance of different variants of the Ant Colony Optimization algorithm is analyzed to choose one as the basis to the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence is created. Encouraging results on several traveling salesman problem instances and its application to the design of fuzzy controllers, in particular the optimization of membership functions for a unicycle mobile robot trajectory control are presented with the proposed method.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2014.12.002