TY - GEN

T1 - Path Planning of Mobile Robot Based on an Improved Ant Colony Algorithm

AU - Pan, Chong

AU - Wang, Hongbo

AU - Li, Jinxin

AU - Korovkin, Maxim

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - The ant colony algorithm (ACO) is an intelligent optimization algorithm inspired by the behavior of ants searching for food in the nature. As a general stochastic optimization algorithm, the ant colony algorithm has been successfully applied to TSP, mobile robot path planning and other combinatorial optimization problems, and achieved good results. But because the probability of the algorithm is a typical algorithm, the parameters set in the algorithm is usually determined by experimental method, leading to the optimization of the performance closely related to people’s experience, it is difficult to optimize the algorithm performance. Moreover, the traditional ant colony algorithm has many shortcomings, such as long convergence time and easiness to fall into the local optimal solution. In order to overcome these shortcomings, in this paper, a large number of experimental data are analyzed to obtain the main appropriate parameters of the ant colony algorithm, such as the number (Forumala Presented). of ants, the number (Forumala Presented). of iterations, the influence factor (Forumala Presented)., and a new pheromone updating method that is related to the sine function is proposed in this paper, the simulation results show that the improved algorithm can accelerate the speed by 60%, and the global optimal solution can be found more easily than the original ant colony algorithm.

AB - The ant colony algorithm (ACO) is an intelligent optimization algorithm inspired by the behavior of ants searching for food in the nature. As a general stochastic optimization algorithm, the ant colony algorithm has been successfully applied to TSP, mobile robot path planning and other combinatorial optimization problems, and achieved good results. But because the probability of the algorithm is a typical algorithm, the parameters set in the algorithm is usually determined by experimental method, leading to the optimization of the performance closely related to people’s experience, it is difficult to optimize the algorithm performance. Moreover, the traditional ant colony algorithm has many shortcomings, such as long convergence time and easiness to fall into the local optimal solution. In order to overcome these shortcomings, in this paper, a large number of experimental data are analyzed to obtain the main appropriate parameters of the ant colony algorithm, such as the number (Forumala Presented). of ants, the number (Forumala Presented). of iterations, the influence factor (Forumala Presented)., and a new pheromone updating method that is related to the sine function is proposed in this paper, the simulation results show that the improved algorithm can accelerate the speed by 60%, and the global optimal solution can be found more easily than the original ant colony algorithm.

KW - Ant colony algorithm

KW - Path planning

KW - Update pheromone

UR - http://www.scopus.com/inward/record.url?scp=85081059495&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-37436-5_11

DO - 10.1007/978-3-030-37436-5_11

M3 - Conference contribution

AN - SCOPUS:85081059495

SN - 9783030374358

T3 - Communications in Computer and Information Science

SP - 132

EP - 141

BT - Convergent Cognitive Information Technologies - 3rd International Conference, Convergent 2018, Revised Selected Papers

A2 - Sukhomlin, Vladimir

A2 - Zubareva, Elena

PB - Springer Nature

T2 - 3rd International Scientific Conference on Convergent Cognitive Information Technologies, Convergent 2018

Y2 - 29 November 2018 through 2 December 2018

ER -