MATEC Web Conf.
Volume 228, 20182018 3rd International Conference on Circuits and Systems (CAS 2018)
|Number of page(s)||4|
|Section||Intelligent Computing and Information Processing|
|Published online||14 November 2018|
Path Planning Based on an Improved Ant Colony Algorithm
College of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou, 545006, China
a Corresponding author: firstname.lastname@example.org
The classical ant colony algorithm has the disadvantages of initial search blindness, slow convergence speed and easy to fall into local optimum when applied to mobile robot path planning. This paper presents an improved ant colony algorithm in order to solve these disadvantages. First, the algorithm use A* search algorithm for initial search to generate uneven initial pheromone distribution to solve the initial search blindness problem. At the same time, the algorithm also limits the pheromone concentration to avoid local optimum. Then, the algorithm optimizes the transfer probability and adopts the pheromone update rule of "incentive and suppression strategy" to accelerate the convergence speed. Finally, the algorithm builds an adaptive model of pheromone coefficient to make the pheromone coefficient adjustment self-adaptive to avoid falling into a local minimum. The results proved that the proposed algorithm is practical and effective.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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