A new method of task aggregation and optimization allocation for multiple groups collaborative task networks

• Published in: Scientific reports Vol. 15; no. 1; pp. 27566 - 24
• Main Authors: Du, WeiWei, Chen, XiaoWei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705/1042; Adaptive genetic algorithm; Algorithms; Collaboration; Decision making; Deep learning; Efficiency; Genetic algorithms; Heuristic; Humanities and Social Sciences; Linear programming; Mathematical models; Multi-constraint multi-objective optimization; multidisciplinary; Mutation; Neural networks; Optimal task allocation; Optimization algorithms; Science; Science (multidisciplinary); Submarine warfare; Task aggregation; Task networks; Multi-constraint multi-objective optimization; Task networks; Adaptive genetic algorithm; Task aggregation; Optimal task allocation
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-11762-9

As task diversity and inter-task relationship complexity grow, optimal formation power allocation is key to improving task execution efficiency. This paper proposes a task optimization allocation method with multiple groups collaboration, constructing a task network based on analysis of task static characteristics and inter-task relationship attributes. Firstly, the Lasswell 5W model is introduced to explore task characteristics, extend inter-task relationship types, and propose a generalized quantitative description method of the task network based on binary groups. Secondly, considering task allocation requirements, space–time constraints, resource capacity and demand constraints, the multi-group collaborative task allocation problem is transformed into a multi-constraint multi-objective optimization problem, establishing a multi-group task allocation mathematical model. Subsequently, the large-scale task network is decomposed into sub-task networks. The clustering cost function for tasks is constructed by analyzing the similarity between formation force locations and resource requirements, and between sub-task locations and resource requirements. The initial allocation strategy for subsets of formations and tasks is established. Finally, an adaptive mechanism is introduced to optimize the genetic algorithm’s crossover and mutation strategies, proposing a new adaptive optimal allocation algorithm for multiple groups tasks. Experimental results show that the proposed method achieves efficient task assignment under complex and diverse task planning scenarios.