Full Text:
<6>
CLC number:
On-line Access: 2025-11-29
Received: 2025-07-30
Revision Accepted: 2025-11-10
Crosschecked: 0000-00-00
Cited: 0
Clicked: 13
Coevolutionary genetic programming for large-scale dynamicmulti-aircraft task allocation
| |||||||||||||
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",
volume="-1",
number="-1",
pages="",
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
%A Ce YU
%A Xianbin CAO
%A Bo ZHANG
%A Wenbo DU
%A Tong GUO
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2500540
TY - JOUR
T1 - Coevolutionary genetic programming for large-scale dynamicmulti-aircraft task allocation
A1 - Ce YU
A1 - Xianbin CAO
A1 - Bo ZHANG
A1 - Wenbo DU
A1 - Tong GUO
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2500540
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.
Open peer comments: Debate/Discuss/Question/Opinion
<1>