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Neural Surrogate-Enhanced Metaheuristic Optimization for Distributed Quadrotor Swarm Control
by
Li, Jinze
, Ning, Zhaoke
, Wen, Zeling
in
Batch processes
/ Case studies
/ Database searching
/ Digital map services
/ distributed control
/ Internet/Web search services
/ Kinematics
/ multi-objective optimization
/ neural surrogate
/ Online searching
/ Optimization
/ real-time decision making
/ Safety regulations
/ Semantics
/ Teachers
/ UAV swarms
/ Unmanned aerial vehicles
/ Velocity
2026
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Neural Surrogate-Enhanced Metaheuristic Optimization for Distributed Quadrotor Swarm Control
by
Li, Jinze
, Ning, Zhaoke
, Wen, Zeling
in
Batch processes
/ Case studies
/ Database searching
/ Digital map services
/ distributed control
/ Internet/Web search services
/ Kinematics
/ multi-objective optimization
/ neural surrogate
/ Online searching
/ Optimization
/ real-time decision making
/ Safety regulations
/ Semantics
/ Teachers
/ UAV swarms
/ Unmanned aerial vehicles
/ Velocity
2026
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Do you wish to request the book?
Neural Surrogate-Enhanced Metaheuristic Optimization for Distributed Quadrotor Swarm Control
by
Li, Jinze
, Ning, Zhaoke
, Wen, Zeling
in
Batch processes
/ Case studies
/ Database searching
/ Digital map services
/ distributed control
/ Internet/Web search services
/ Kinematics
/ multi-objective optimization
/ neural surrogate
/ Online searching
/ Optimization
/ real-time decision making
/ Safety regulations
/ Semantics
/ Teachers
/ UAV swarms
/ Unmanned aerial vehicles
/ Velocity
2026
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Neural Surrogate-Enhanced Metaheuristic Optimization for Distributed Quadrotor Swarm Control
Journal Article
Neural Surrogate-Enhanced Metaheuristic Optimization for Distributed Quadrotor Swarm Control
2026
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Overview
Real-time cooperative control of quadrotor swarms in cluttered environments requires balancing formation maintenance, obstacle avoidance, inter-UAV safety, and per-step computational cost. This paper proposes a multilayer perceptron (MLP) surrogate for high-level objective-weight selection in a modified multi-objective pigeon-inspired optimization (modified MPIO) distributed controller. The proposed MLP surrogate learns the state-to-weight mapping of the online search and directly predicts the two-dimensional objective-weight vector, while the original flocking, gap-based obstacle-avoidance, and command generation rules are retained unchanged. The surrogate is trained from teacher-generated weight labels using randomized scenes, DAgger-based state aggregation, and risk-weighted supervision. On a fixed closed-loop benchmark, the proposed controller increases the true collision free rate from 48.00% to 86.89% and the safe success rate from 38.67% to 74.22% relative to modified MPIO, while reducing the mean per-step decision latency for the whole swarm from 8494.70 ms to 0.92 ms. The improvement is most pronounced in safety-related and runtime metrics, while the formation-related gain is comparatively modest. Ablation results show that the final benchmark performance is not explained by DAgger or risk weighting alone, and that the medium-sized surrogate provides the best safety-latency tradeoff among the tested network architectures. A qualitative AirSim case study further indicates that the same high-level surrogate controller can be executed in a higher-fidelity asynchronous multirotor simulator.
Publisher
MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
Subject
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