Joint UAV Activation and Placement for Post-Disaster Wireless Restoration via a Hybrid Quantum-Inspired Evolutionary Framework

Fatima Azzahraa Amarcha; Lahcen Hassine; Rachid Saadane; Mohamed Rahouti; Rachid Ahl Laamara; Abdallah Slaoui; Hany S. khalifa

cs.NESep 7, 2026

Joint UAV Activation and Placement for Post-Disaster Wireless Restoration via a Hybrid Quantum-Inspired Evolutionary Framework

Authors: Fatima Azzahraa Amarcha, Lahcen Hassine, Rachid Saadane, Mohamed Rahouti, Rachid Ahl Laamara, Abdallah Slaoui, Hany S. khalifa

Organizations: LPHE, Modeling and Simulations, Faculty of Science, Mohammed V University in Rabat, Morocco. · Hassania School of Public Works (EHTP), Laboratory Engineering System, SIRC Team, Casablanca, Morocco. · Genuis Lab, School of Management, Telecommunications and Computer Science (SUPMTI), Rabat, Morocco. · Department of Computer and Information Science, Fordham University, New York, NY, USA. ·

Abstract

In post-disaster environments, the failure of terrestrial communication infrastructure necessitates the rapid deployment of unmanned aerial vehicles (UAVs) as aerial base stations to restore wireless connectivity. This paper addresses the joint UAV activation-and-placement problem in continuous space, with the objective of minimizing the number of deployed UAVs while satisfying coverage and minimum-separation constraints. To solve this problem, we propose a Hybrid K-means Quantum-Inspired Evolutionary Algorithm (HKQEA) that combines K-means-guided initialization, a calibrated penalty-based feasibility objective, non-elitist evolutionary search, and a quantum-inspired learning update. Experimental results over 50 independent runs show that HKQEA attains a best fully feasible solution with 8 UAVs, while achieving average values of 98.94% for coverage, 99.94% for non-overlap, and 99.68% for minimum-distance satisfaction. Comparative evaluation against standard Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization algorithm (PSO) and an elitist variant of HKQEA further shows that the proposed method provides a more favorable balance among exploration, convergence behavior, and reliable feasibility preservation in constrained deployment problems. An illustrative procurement-level cost analysis also indicates that reducing the fleet from 10 UAVs to 8 can yield a 20% reduction in hardware count, corresponding to a simplified savings ratio of 25% for the studied deployment setting. These results demonstrate the potential of the proposed framework for resource-efficient post-disaster communication restoration.

Figures & tables

Reference	Technique	Strength	Main limitation
( Park et al., 2020 )	B&P	Exact optimization framework for constrained deployment with optimality guarantees	High computational burden, limiting scalability in large instances
( Premkumar and Van Scoy, 2025 )	MILP + K-means	Rigorous formulation with good solution quality; clustering improves tractability	Primarily maximizes served users and typically assumes a fixed UAV fleet size
( Sabino et al., 2018 )	NSGA-II	Multi-objective search balancing deployment-related objectives and service performance	Performance can depend strongly on discretization and may suffer from premature convergence
( Ballinas et al., 2024 )	HQGA + fuzzy rotation	Introduces quantum-inspired operators to improve search behavior in UAV placement	Focuses mainly on coverage-oriented placement and assumes a fixed number of UAVs
( Mondal et al., 2026 )	Quantum-inspired bi-level neuro-swarm optimization	Demonstrates the promise of quantum-inspired search in UAV disaster-management settings	Does not directly target joint fleet minimization with explicit coverage and separation constraints

Table 1. Representative optimization approaches for UAV deployment in disaster communication scenarios

Figure 1. Illustration of the post-disaster UAV-assisted communication model.

Figure 2. Algorithmic flow of the proposed HKQEA

$(\lambda_{1},\lambda_{2},\lambda_{3})$	$S^{\ast}$	Co (%)	Over (%)	Dis (%)
(1,1,1)	6	31.25	90.00	99.60
(100,10,5)	7	90.62	100.00	99.00
(250,20,10)	8	96.87	90.40	99.00
(500,50,20)	6	84.37	100.00	100.00
(750,70,30)	8	93.75	100.00	98.80
(1000,100,30)	8	96.87	100.00	100.00

Table 2. Calibration of penalty coefficients for the feasibility objective.

Method	$S^{\ast}$	Co (%)	Over (%)	Dis (%)	Time (s)
Static penalty	8	100.00	100.00	100.00	161
Multi-level penalty	7	90.62	96.55	99.39	293
Deb $\epsilon$ -constraint	9	87.50	97.00	99.80	202
Repair method	9	78.12	88.00	98.60	1404
BOP	2	21.87	100.00	100.00	1337

Table 3. Comparison of constraint-handling methods under the same evolutionary search framework.

Figure 3. Normalized comparison of the evaluated constraint-handling methods.

Method	Statistic	$S^{\ast}$	Co (%)	Over (%)	Dis (%)	Time (s)
HKQEA	Best	8.00	100.00	100.00	100.00	81.93
	Avg	8.20	98.94	99.94	99.68	148.61
	Worst	9.00	87.50	96.77	98.99	298.93
	Std	0.40	2.22	0.45	0.32	55.20

Table 4. Descriptive statistics of HKQEA over 50 independent runs.

Panel of Figure 6 — Figure 6. HKQEA-Run 21: Convergence behavior of the proposed optimization procedure over 1000 generations.

Panel of Figure 7 — Figure 7. HKQEA-Run 21: early-stage convergence over the first 100 generations.

Method	Statistic	$S^{\ast}$	Co (%)	Over (%)	Dis (%)	Time (s)
HKQEA	Best	8.00	100.00	100.00	100.00	81.93
	Avg	8.20	98.94	99.94	99.68	148.61
	Worst	9.00	87.50	96.77	98.99	298.93
	Std	0.40	2.22	0.45	0.32	55.20
HKQEA (Elitist)	Best	9.00	100.00	100.00	99.60	124.69
	Avg	9.00	100.00	100.00	99.20	156.68

Table 5. Descriptive statistics over 50 runs for the compared methods. The Best , Avg , and Worst entries are marginal statistics per metric and do not necessarily correspond to the same run.

Method	Run	$S^{\ast}$	Co (%)	Over (%)	Dis (%)	Conv. Gen
HKQEA	21	8	100.00	100.00	100.00	30–40
Elitist HKQEA	42	9	100.00	100.00	98.99	80–100
NSGA-II	40	8	100.00	100.00	100.00	100–150
PSO	47	8	96.88	100.00	99.80	30–40

Table 6. Best fully feasible run obtained by each algorithm.

Panel of Figure 16 — Figure 16. PSO convergence over 1000 iterations.

Wireless Communications

Wireless communications studies how information is sent over radio links and how networks manage signals, spectrum, and resources reliably.

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Coverage Path Planning Multi-Agent Coordination World Model-Based Planning Multi-Agent Negotiation World Models

Open paper AskPDF arXiv

Post-disaster damage to terrestrial infrastructure can disrupt wireless coverage,while Uncrewed Aerial Vehicle (UAV) swarms provide a promising solution for rapid restoration.However, due to the limitations in local geometry observations hidden radio impact,and inter-UAV communication,there exists a significant gap between locally visible movement choices and swarm-level coverage outcomes.To combat this gap,we propose a raido World-model-based Optimized Negotiation framework for Distributed UAV covERage (WONDER).Particularly, to tackle the unavailability of the future radio field from onboard observations, WONDER uses a Joint-Embedding Predictive Architecture (JEPA)-based radio world model to learn and predict the incremental radio effect of each candidate trajectory from deployment-available information.Multi-round negotiation in WONDER then coordinates ranked proposals by committing one trajectory at a time and re-evaluating the remaining proposals under the updated context. Our theoretical analyses further validate the effectiveness of such a world model-based framework. WONDER also adopts a Proximal Policy Optimization (PPO)-style Actor and alternates between updating the world model and the actor. Furthermore,we build RadioDynamics,a comprehensive simulation environment that integrates UAV mobility,radio propagation, inter-UAV communication modeling,and digital-twin geometry with ray-traced fields in

62

metropolitan scenes.Experiments on

11

testing scenes in RadioDynamics show that WONDER achieves the highest balanced score among seven evaluated methods,reaching

0.870

with a

0.162

coverage advantage over STACCA, while maintaining

100\%

connectivity between UAVs.

Abstract

Wireless Communications

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