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

doi:10.1109/CEC.2016.7744364

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

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 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 language	English
Title of host publication	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4516-4521
Number of pages	6
ISBN (Electronic)	9781509006229
DOIs	https://doi.org/10.1109/CEC.2016.7744364
Publication status	Published - 2016 Nov 14
Event	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada Duration: 2016 Jul 24 → 2016 Jul 29

Publication series

Name	2016 IEEE Congress on Evolutionary Computation, CEC 2016

Other

Other	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Country/Territory	Canada
City	Vancouver
Period	2016/07/24 → 2016/07/29

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

Access to Document

10.1109/CEC.2016.7744364

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). Article 7744364 (2016 IEEE Congress on Evolutionary Computation, CEC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2016.7744364

FPGA implementation of improved ant colony optimization algorithm for path planning. / Hsu, Chen Chien ; Wang, Wei Yen; Chien, Yi Hsing et al.
2016 IEEE Congress on Evolutionary Computation, CEC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 4516-4521 7744364 (2016 IEEE Congress on Evolutionary Computation, CEC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hsu, CC , Wang, WY, Chien, YH, Hou, RY & Tao, CW 2016, FPGA implementation of improved ant colony optimization algorithm for path planning. in 2016 IEEE Congress on Evolutionary Computation, CEC 2016., 7744364, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 4516-4521, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, 2016/07/24. https://doi.org/10.1109/CEC.2016.7744364

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title = "FPGA implementation of improved ant colony optimization algorithm for path planning",

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.",

keywords = "Ant colony optimization (ACO), Field-programmable gate array (FPGA), Path planning, Pheromone diffusion mechanism",

author = "Hsu, {Chen Chien} and Wang, {Wei Yen} and Chien, {Yi Hsing} and Hou, {Ru Yu} and Tao, {Chin Wang}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Congress on Evolutionary Computation, CEC 2016 ; Conference date: 24-07-2016 Through 29-07-2016",

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AU - Hsu, Chen Chien

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AU - Tao, Chin Wang

PY - 2016/11/14

Y1 - 2016/11/14

N2 - 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.

AB - 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.

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KW - Path planning

KW - Pheromone diffusion mechanism

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FPGA implementation of improved ant colony optimization algorithm for path planning

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