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Coevolutionary genetic programming for large-scale dynamicmulti-aircraft task allocation
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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, 1998, -1(-1): .
@article{title="Coevolutionary genetic programming for large-scale dynamicmulti-aircraft task allocation",
author="Ce YU, Xianbin CAO, Bo ZHANG, Wenbo DU, Tong GUO",
journal="Frontiers of Information Technology & Electronic Engineering",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2500540"
}
%0 Journal Article
%T Coevolutionary genetic programming for large-scale dynamicmulti-aircraft task allocation
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%A Wenbo DU
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%J Journal of Zhejiang University SCIENCE C
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%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2500540
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J0 - Journal of Zhejiang University Science C
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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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