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General information |
| Author |
Castillo, Oscar; Neyoy, Héctor; José Soria, Mario García; Valdez, Fevrier |
| Published |
InTech Open Access Publisher, 2013
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| Abstract |
Ant Colony Optimization (ACO) is a
population-based constructive meta-heuristic that
exploits a form of past performance memory inspired by
the foraging behaviour of real ants. The behaviour of the
ACO algorithm is highly dependent on the values
defined for its parameters.
Adaptation and parameter control are recurring themes
in the field of bio-inspired algorithms. The present paper
explores a new approach to diversity control in ACO. The
central idea is to avoid or slow down full convergence
through the dynamic variation of certain parameters.
The performance of different variants of the ACO
algorithm was observed to choose one as the basis for the
proposed approach.
A convergence fuzzy logic controller with the objective
of maintaining diversity at some level to avoid
premature convergence was created. Encouraging
results have been obtained on its application to the
design of fuzzy controllers. In particular, the
optimization of membership functions for a unicycle
mobile robot trajectory control is presented with the
proposed method. |
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International Journal of Advanced Robotic Systems
Author: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Published: 2004
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