Multi-Task Scheduling Optimization Method Integrating Visibility Graph, Simulated Annealing, and A Algorithm

This paper presents a multi-task allocation method integrating the visibility graph, A* algorithm, and simulated annealing to enhance scheduling stability and path accuracy in complex environments. The method constructs a path cost matrix and globally optimizes task sequences. Experimental results s...

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Veröffentlicht in:2025 2nd International Conference on Algorithms, Software Engineering and Network Security (ASENS) S. 263 - 267
Hauptverfasser: Luo, Guilan, Chen, Zijie, Zeng, Yongwang, Wang, Long, Wen, Shuzhen, Zhang, Mei
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 21.03.2025
Schlagworte:
A algorithm
Accuracy
Approximation algorithms
ask allocation
Costs
Dynamic scheduling
Heuristic algorithms
Multitasking
Path planning
Resource management
Simulated annealing
Simulated annealing algorithm
Software algorithms
Visibility graph algorithm
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Zusammenfassung:This paper presents a multi-task allocation method integrating the visibility graph, A* algorithm, and simulated annealing to enhance scheduling stability and path accuracy in complex environments. The method constructs a path cost matrix and globally optimizes task sequences. Experimental results show a 2% reduction in path length compared to Euclidean-based methods while improving path planning accuracy, allocation efficiency, and stability, providing a robust solution for dynamic multi-task scheduling.
DOI:10.1109/ASENS64990.2025.11011007