Multi-AGV path planning with double-path constraints by using an improved genetic algorithm.
This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the trad...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5528885?pdf=render |
| _version_ | 1818257125607997440 |
|---|---|
| author | Zengliang Han Dongqing Wang Feng Liu Zhiyong Zhao |
| author_facet | Zengliang Han Dongqing Wang Feng Liu Zhiyong Zhao |
| author_sort | Zengliang Han |
| collection | DOAJ |
| description | This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm. |
| first_indexed | 2024-12-12T17:38:41Z |
| format | Article |
| id | doaj.art-53150385c23844c386b38494a5d39e65 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-12T17:38:41Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-53150385c23844c386b38494a5d39e652022-12-22T00:17:09ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01127e018174710.1371/journal.pone.0181747Multi-AGV path planning with double-path constraints by using an improved genetic algorithm.Zengliang HanDongqing WangFeng LiuZhiyong ZhaoThis paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm.http://europepmc.org/articles/PMC5528885?pdf=render |
| spellingShingle | Zengliang Han Dongqing Wang Feng Liu Zhiyong Zhao Multi-AGV path planning with double-path constraints by using an improved genetic algorithm. PLoS ONE |
| title | Multi-AGV path planning with double-path constraints by using an improved genetic algorithm. |
| title_full | Multi-AGV path planning with double-path constraints by using an improved genetic algorithm. |
| title_fullStr | Multi-AGV path planning with double-path constraints by using an improved genetic algorithm. |
| title_full_unstemmed | Multi-AGV path planning with double-path constraints by using an improved genetic algorithm. |
| title_short | Multi-AGV path planning with double-path constraints by using an improved genetic algorithm. |
| title_sort | multi agv path planning with double path constraints by using an improved genetic algorithm |
| url | http://europepmc.org/articles/PMC5528885?pdf=render |
| work_keys_str_mv | AT zenglianghan multiagvpathplanningwithdoublepathconstraintsbyusinganimprovedgeneticalgorithm AT dongqingwang multiagvpathplanningwithdoublepathconstraintsbyusinganimprovedgeneticalgorithm AT fengliu multiagvpathplanningwithdoublepathconstraintsbyusinganimprovedgeneticalgorithm AT zhiyongzhao multiagvpathplanningwithdoublepathconstraintsbyusinganimprovedgeneticalgorithm |