Realization of an Energy-Based Ant Colony Optimization Algorithm for Path Planning

Kuan Tung Lee, Shih Hua Huang, Shih Hsun Sun, Yih-Guang Leu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose an energy-based ant colony optimization algorithm for path planning. Because the shortest path does not guarantee the optimal energy-conserving way, this paper utilizes the ant colony optimization algorithm to acquire the optimal energy-conserving path. For battery-powered electric vehicles, the energy consumption depends on the road condition. Therefore, according to the road condition, the update law with energy pheromone is obtained. Finally, computer simulations and real road experiments of the battery-powered electric vehicle were conducted to verify the efficiency of the proposed method.

Original languageEnglish
Title of host publicationNew Trends on System Sciences and Engineering - Proceedings of ICSSE 2015
EditorsHamido Fujita, Shun-Feng Su
PublisherIOS Press
Pages193-199
Number of pages7
ISBN (Electronic)9781614995210
DOIs
Publication statusPublished - 2015 Jan 1
EventInternational Conference on System Science and Engineering, ICSSE 2015 - Morioka, Japan
Duration: 2015 Jul 62015 Jul 8

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume276
ISSN (Print)0922-6389

Other

OtherInternational Conference on System Science and Engineering, ICSSE 2015
CountryJapan
CityMorioka
Period15/7/615/7/8

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Keywords

  • Ant Colony Optimization
  • Path planning

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Lee, K. T., Huang, S. H., Sun, S. H., & Leu, Y-G. (2015). Realization of an Energy-Based Ant Colony Optimization Algorithm for Path Planning. In H. Fujita, & S-F. Su (Eds.), New Trends on System Sciences and Engineering - Proceedings of ICSSE 2015 (pp. 193-199). (Frontiers in Artificial Intelligence and Applications; Vol. 276). IOS Press. https://doi.org/10.3233/978-1-61499-522-7-193