Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning
As a widely used path planning algorithm, the ant colony optimization algorithm (ACO) has evolved into a well-developed method within the realm of optimization algorithms and has been extensively applied across various fields. In this study, a multi-strategy adaptable ant colony optimization (MsAACO...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/180180 |
| _version_ | 1826127304997208064 |
|---|---|
| author | Cui, Junguo Wu, Lei Huang, Xiaodong Xu, Dengpan Liu, Chao Xiao, Wensheng |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Cui, Junguo Wu, Lei Huang, Xiaodong Xu, Dengpan Liu, Chao Xiao, Wensheng |
| author_sort | Cui, Junguo |
| collection | NTU |
| description | As a widely used path planning algorithm, the ant colony optimization algorithm (ACO) has evolved into a well-developed method within the realm of optimization algorithms and has been extensively applied across various fields. In this study, a multi-strategy adaptable ant colony optimization (MsAACO) is proposed to alleviate the insufficient and inefficient convergence of ACO, employing four-design improvements. First, a direction-guidance mechanism is proposed to improve the performance of node selection. Second, an adaptive heuristic function is introduced to decrease the length and number of turns of the optimal path solutions. Moreover, the deterministic state transition probability rule was employed to promote the convergence speed of ACO. Finally, nonuniform pheromone initialization was utilized to enhance the ability of ACO to select advantageous regions. Subsequently, the major parameters of the strategies were optimized and their effectiveness was validated. MsAACO was proposed by combining these four strategies with ACO. To verify the advantages of MsAACO, five representative environment models were employed, and comprehensive experiments were conducted by comparing them with existing approaches, including the A* algorithm, variants of ACO, Dijkstra's algorithm, jump point search algorithm, best-first search, breadth-first search, trace algorithm, and other excellent algorithms. The experimental statistical results demonstrate that MsAACO can efficiently generate smoother optimal path-planning solutions with lower length and turn times and improve the convergence efficiency and stability of ACO compared to other algorithms. The generated results of MsAACO verified its superiority in solving the path-planning problem of mobile robots. |
| first_indexed | 2024-10-01T07:06:44Z |
| format | Journal Article |
| id | ntu-10356/180180 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:06:44Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1801802024-09-23T04:56:29Z Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning Cui, Junguo Wu, Lei Huang, Xiaodong Xu, Dengpan Liu, Chao Xiao, Wensheng School of Civil and Environmental Engineering Maritime Institute Engineering Path planning Ant colony optimization algorithm As a widely used path planning algorithm, the ant colony optimization algorithm (ACO) has evolved into a well-developed method within the realm of optimization algorithms and has been extensively applied across various fields. In this study, a multi-strategy adaptable ant colony optimization (MsAACO) is proposed to alleviate the insufficient and inefficient convergence of ACO, employing four-design improvements. First, a direction-guidance mechanism is proposed to improve the performance of node selection. Second, an adaptive heuristic function is introduced to decrease the length and number of turns of the optimal path solutions. Moreover, the deterministic state transition probability rule was employed to promote the convergence speed of ACO. Finally, nonuniform pheromone initialization was utilized to enhance the ability of ACO to select advantageous regions. Subsequently, the major parameters of the strategies were optimized and their effectiveness was validated. MsAACO was proposed by combining these four strategies with ACO. To verify the advantages of MsAACO, five representative environment models were employed, and comprehensive experiments were conducted by comparing them with existing approaches, including the A* algorithm, variants of ACO, Dijkstra's algorithm, jump point search algorithm, best-first search, breadth-first search, trace algorithm, and other excellent algorithms. The experimental statistical results demonstrate that MsAACO can efficiently generate smoother optimal path-planning solutions with lower length and turn times and improve the convergence efficiency and stability of ACO compared to other algorithms. The generated results of MsAACO verified its superiority in solving the path-planning problem of mobile robots. This research is funded by “the National Key R&D Program of China” (grant number 2021YFB3401400), the Major Scientific and Technological Innovation Project of Shandong Province, China (2022CXGC020405), the Taishan Scholars Program of Shandong Province (tsqn201909067), the Shandong Province Natural Science Foundation (ZR2020QE300), Fundamental Research Funds for the Central Universities (20CX06012A), and the Project of Ministry of Industry and Information Technology of the People’s Republic of China (Research on the key technology of treatment process for high -flow offshore natural gas, CJ09N20). 2024-09-23T04:56:29Z 2024-09-23T04:56:29Z 2024 Journal Article Cui, J., Wu, L., Huang, X., Xu, D., Liu, C. & Xiao, W. (2024). Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning. Knowledge-Based Systems, 288, 111459-. https://dx.doi.org/10.1016/j.knosys.2024.111459 0950-7051 https://hdl.handle.net/10356/180180 10.1016/j.knosys.2024.111459 2-s2.0-85184143291 288 111459 en Knowledge-Based Systems © 2024 Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering Path planning Ant colony optimization algorithm Cui, Junguo Wu, Lei Huang, Xiaodong Xu, Dengpan Liu, Chao Xiao, Wensheng Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning |
| title | Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning |
| title_full | Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning |
| title_fullStr | Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning |
| title_full_unstemmed | Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning |
| title_short | Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning |
| title_sort | multi strategy adaptable ant colony optimization algorithm and its application in robot path planning |
| topic | Engineering Path planning Ant colony optimization algorithm |
| url | https://hdl.handle.net/10356/180180 |
| work_keys_str_mv | AT cuijunguo multistrategyadaptableantcolonyoptimizationalgorithmanditsapplicationinrobotpathplanning AT wulei multistrategyadaptableantcolonyoptimizationalgorithmanditsapplicationinrobotpathplanning AT huangxiaodong multistrategyadaptableantcolonyoptimizationalgorithmanditsapplicationinrobotpathplanning AT xudengpan multistrategyadaptableantcolonyoptimizationalgorithmanditsapplicationinrobotpathplanning AT liuchao multistrategyadaptableantcolonyoptimizationalgorithmanditsapplicationinrobotpathplanning AT xiaowensheng multistrategyadaptableantcolonyoptimizationalgorithmanditsapplicationinrobotpathplanning |