JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

Coevolutionary genetic programming for large-scale dynamicmulti-aircraft task allocation

Author(s): Ce YU, Xianbin CAO, Bo ZHANG, Wenbo DU, Tong GUO
Affiliation(s): School of Electronic and Information Engineering, State Key Laboratory of CNS/ATM, Beihang University, Beijing 100191, China
Corresponding email(s): guotong1997@buaa.edu.cn
Key Words: Task allocation; Genetic Programming; Hyperheuristic

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Ce YU, Xianbin CAO, Bo ZHANG, Wenbo DU, Tong GUO. Coevolutionary genetic programming for large-scale dynamicmulti-aircraft task allocation[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2500540

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author="Ce YU, Xianbin CAO, Bo ZHANG, Wenbo DU, Tong GUO",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2500540"
}

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%A Bo ZHANG
%A Wenbo DU
%A Tong GUO
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doi="https://doi.org/10.1631/FITEE.2500540"

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J0 - Frontiers of Information Technology & Electronic Engineering
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doi="https://doi.org/10.1631/FITEE.2500540"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Multi-aircraft task allocation (MATA) plays a vital role in improving mission efficiency under dynamic conditions. This paper proposes a novel Coevolutionary Genetic Programming (CoGP) framework that automatically designs high-performance reactive heuristics for dynamic MATA problems. Unlike conventional single-tree genetic programming (GP) methods, CoGP jointly develops two interacting populations, i.e., task prioritization heuristics and aircraft selection heuristics, to explicitly model the coupling between these two interdependent decision phases. A comprehensive terminal set is constructed to represent the dynamic states of aircraft and tasks, whereas a lowlevel heuristic template translates developed trees into executable allocation strategies. Extensive experiments on public benchmark instances simulating post-disaster emergency delivery demonstrate that CoGP achieves superior performance compared with state-of-the-art GP and heuristic methods, exhibiting strong adaptability, scalability, and real-time responsiveness in complex and dynamic rescue environments.

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