Comparison between heterogeneous ant colony optimization algorithm and Genetic Algorithm for global path planning of mobile robot

We proposed a novel ACO algorithm to solve the global path planning problems in the previous paper, called Heterogeneous ACO (HACO) algorithm. In this paper, we compare the performance of HACO algorithm with the modified Genetic Algorithm (GA) for global path planning. The HACO algorithm differs fro...

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Bibliographic Details
Published in2011 IEEE International Symposium on Industrial Electronics pp. 881 - 886
Main Authors Joon-Woo Lee, Byoung-Suk Choi, Kyoung-Taik Park, Ju-Jang Lee
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Computer simulation
Force
Genetic algorithms
Interpolation
Mobile robots
Path planning
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Summary:We proposed a novel ACO algorithm to solve the global path planning problems in the previous paper, called Heterogeneous ACO (HACO) algorithm. In this paper, we compare the performance of HACO algorithm with the modified Genetic Algorithm (GA) for global path planning. The HACO algorithm differs from the Conventional ACO (CACO) algorithm for the path planning in three respects. First, we proposed modified Transition Probability Function (TPF) and Pheromone Update Rule (PUR). Second, we newly introduced the Path Crossover (PC) in the PUR. Finally, we also proposed the first introduction of the heterogeneous ants in the ACO algorithm. We apply the proposed HACO algorithm and modified GA to the general global path planning problems and compare the performance of these through the computer simulation.
ISBN:1424493102
9781424493104
ISSN:2163-5137
DOI:10.1109/ISIE.2011.5984275