Optimization Method for Multi Agricultural Machinery Collaborative Operation Based on Genetic Algorithm and A* Algorithm

Authors

Yiran Yu, The College of Computer Science and Technology, Xinjiang University, Urumqi 830000, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Urumqi 830000, China
Huicheng Lai, The College of Computer Science and Technology, Xinjiang University, Urumqi 830000, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Urumqi 830000, China
Guxue Gao, The College of Computer Science and Technology, Xinjiang University, Urumqi 830000, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Urumqi 830000, China
Guo Zhang, The College of Computer Science and Technology, Xinjiang University, Urumqi 830000, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Urumqi 830000, China
Wangyinan Peng, The College of Computer Science and Technology, Xinjiang University, Urumqi 830000, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Urumqi 830000, China
Longfei Yang, The College of Computer Science and Technology, Xinjiang University, Urumqi 830000, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Urumqi 830000, China
Junhao Huang, The College of Computer Science and Technology, Xinjiang University, Urumqi 830000, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Urumqi 830000, China

Abstract

Abstract: To address the uneven task distribution among multiple agricultural machines (referred to as farm machinery) and the high time cost due to numerous turning points at intersections, this paper proposes a task planning method that combines a pre-heat multi grouped genetic algorithm (PHMGA) with the turn A* algorithm (tA*). PHMGA allocates tasks to each piece of farm machinery based on the known environment, ensuring balanced workload through a cost objective function that considers travel, operation, and turning distances. It also designs various operators and strategies to search for nearoptimal solutions. The tA* algorithm is used to select paths in the fields, avoiding complex areas with many turning points through turning penalties, thereby further reducing operation time. Simulation results show that our proposed method effectively balances the workload among the farm machinery and significantly reduces operation and waiting times, achieving a reduction of 5% and 56%~67% respectively compared to traditional methods.

Recommended Citation

Yu, Yiran; Lai, Huicheng; Gao, Guxue; Zhang, Guo; Peng, Wangyinan; Yang, Longfei; and Huang, Junhao (2025) "Optimization Method for Multi Agricultural Machinery Collaborative Operation Based on Genetic Algorithm and A* Algorithm," Journal of System Simulation: Vol. 37: Iss. 9, Article 17.
DOI: 10.16182/j.issn1004731x.joss.24-0453
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss9/17

First Page

2397

Last Page

2408

CLC

TP391.9

Recommended Citation

Yu Yiran, Lai Huicheng, Gao Guxue, et al. Optimization Method for Multi Agricultural Machinery Collaborative Operation Based on Genetic Algorithm and A * Algorithm[J]. Journal of System Simulation, 2025, 37(9):2397-2408.

Corresponding Author

Lai Huicheng

DOI

10.16182/j.issn1004731x.joss.24-0453