FPGA implementation of improved ant colony optimization algorithm for path planning

Chen Chien Hsu, Wei Yen Wang, Yi Hsing Chien, Ru Yu Hou, Chin Wang Tao

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

6 Citations (Scopus)

Abstract

An improved ant colony optimization (ACO) algorithm is proposed in this paper for improving the accuracy of path planning. The main idea of this paper is to avoid local minima by continuously tuning a setting parameter and the establishment of novel mechanisms for updating partial pheromone and opposite pheromone. As a result, the global search of the proposed ACO algorithm can be significantly enhanced in terms of calculating optimal path compared to the conventional ACO algorithm. Simulation results of the proposed approach show better performances in terms of the shortest distance, mean distance, and success rate towards optimal paths. To further reduce the computation time, the proposed ACO algorithm for path planning is realized on a FPGA chip to verify its practicalities. Experimental results indicate that the efficiency of the path planning is significantly improved by the hardware design of embedded applications.

Original languageEnglish
Title of host publication2016 IEEE Congress on Evolutionary Computation, CEC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4516-4521
Number of pages6
ISBN (Electronic)9781509006229
DOIs
Publication statusPublished - 2016 Nov 14
Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
Duration: 2016 Jul 242016 Jul 29

Publication series

Name2016 IEEE Congress on Evolutionary Computation, CEC 2016

Other

Other2016 IEEE Congress on Evolutionary Computation, CEC 2016
CountryCanada
CityVancouver
Period16/7/2416/7/29

Keywords

  • Ant colony optimization (ACO)
  • Field-programmable gate array (FPGA)
  • Path planning
  • Pheromone diffusion mechanism

ASJC Scopus subject areas

  • Artificial Intelligence
  • Modelling and Simulation
  • Computer Science Applications
  • Control and Optimization

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  • Cite this

    Hsu, C. C., Wang, W. Y., Chien, Y. H., Hou, R. Y., & Tao, C. W. (2016). FPGA implementation of improved ant colony optimization algorithm for path planning. In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 4516-4521). [7744364] (2016 IEEE Congress on Evolutionary Computation, CEC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2016.7744364