This paper presents the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model used by an Unmanned Aerial Vehicle (UAV) swarm to perform surveillance tasks. CACOC uses chaotic solutions of a dynamical system and pheromones for optimising area coverage. Consequently, several parameters of CACOC are to be optimised with the aim of improving its coverage performance. We propose a Genetic Algorithm (GA) and two Cooperative Coevolutionary Genetic Algorithms (CCGA) to tackle this problem. After testing our proposals on four case studies we performed a comparative analysis to conclude that the cooperative approaches allow a better exploration of the search space by optimising each UAV parameters independently. https://doi.org/10.1109/CCNC46108.2020.9045643

1. University of Luxembourg
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A Cooperative Coevolutionary Approach to Maximise
Surveillance Coverage of UAV Swarms
Daniel H. Stolfi1 Matthias R. Brust1 Grégoire Danoy1,2 Pascal Bouvry1,2
IEEE CCNC 2020 – Communication and Applications for Connected and Autonomous Vehicles on Land, Water, and Sky
January 11
th
, 2020
1 SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg
2 FSTC/CSC, University of Luxembourg, Luxembourg

2. CONTENTS
1 INTRODUCTION
2 CACOC MOBILITY MODEL
3 PARAMETER OPTIMIZATION
4 RESULTS
5 CONCLUSION AND FUTURE WORK
1/13

3. CONTENTS
1 INTRODUCTION
2 CACOC MOBILITY MODEL
3 PARAMETER OPTIMIZATION
4 RESULTS
5 CONCLUSION AND FUTURE WORK
1/13

4. CONTENTS
1 INTRODUCTION
2 CACOC MOBILITY MODEL
3 PARAMETER OPTIMIZATION
4 RESULTS
5 CONCLUSION AND FUTURE WORK
1/13

5. CONTENTS
1 INTRODUCTION
2 CACOC MOBILITY MODEL
3 PARAMETER OPTIMIZATION
4 RESULTS
5 CONCLUSION AND FUTURE WORK
1/13

6. CONTENTS
1 INTRODUCTION
2 CACOC MOBILITY MODEL
3 PARAMETER OPTIMIZATION
4 RESULTS
5 CONCLUSION AND FUTURE WORK
1/13

7. INTRODUCTION
UAVs (drones) has gained importance in recent years.
Environmental monitoring
Early wildfire detection
Architecture surveillance
Goods transportation
Cooperative surveillance
2/13

8. INTRODUCTION
UAVs (drones) has gained importance in recent years.
Environmental monitoring
Early wildfire detection
Architecture surveillance
Goods transportation
Cooperative surveillance
2/13

9. INTRODUCTION
UAVs (drones) has gained importance in recent years.
Environmental monitoring
Early wildfire detection
Architecture surveillance
Goods transportation
Cooperative surveillance
2/13

10. INTRODUCTION
UAVs (drones) has gained importance in recent years.
Environmental monitoring
Early wildfire detection
Architecture surveillance
Goods transportation
Cooperative surveillance
2/13

11. INTRODUCTION
UAVs (drones) has gained importance in recent years.
Environmental monitoring
Early wildfire detection
Architecture surveillance
Goods transportation
Cooperative surveillance
2/13

12. INTRODUCTION
UAVs (drones) has gained importance in recent years.
Environmental monitoring
Early wildfire detection
Architecture surveillance
Goods transportation
Cooperative surveillance
2/13

13. MOTIVATION
Mobility model for surveillance tasks.
Swarm of UAVs (and UGVs, etc.)
Improve coverage
Unpredictable trajectories
Deterministic routes (for the Ground Control Station)
Bioinspired approach
3/13

14. MOTIVATION
Mobility model for surveillance tasks.
Swarm of UAVs (and UGVs, etc.)
Improve coverage
Unpredictable trajectories
Deterministic routes (for the Ground Control Station)
Bioinspired approach
3/13

15. PHEROMONE BASED MODEL
4/13

16. CACOC MOBILITY MODEL1
Chaotic attractor B solution of the Rössler system
1M. Rosalie, G. Danoy, S. Chaumette, and P. Bouvry. “Chaos-enhanced mobility models for multilevel swarms of UAVs”. In: Swarm and
Evolutionary Computation 41.November 2017 (2018), pp. 36–48.
5/13

17. PARAMETER OPTIMIZATION
TABLE: Parameters proposed for CACOC.
Parameter Symbol Units Range
Pheromone amount τa % [1 − 100]
Pheromone radius τr cells [0.5 − 2.5]
Pheromone scan depth τd cells [1 − 10]
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18. OPTIMIZATION ALGORITHMS: GA
Genetic Algorithm (GA)
Inspired in the Theory of Evolution
Solves complex combinatorial problems
Population of individuals
Natural Selection
Reproduction (DNA recombination)
Mutation
Survival of the fittest
Fitness Function
F(x) =
# of explored cells
# of cells in the scenario
(maximisation)
7/13

19. OPTIMIZATION ALGORITHMS: CCGA
Cooperative Coevolutionary Genetic Algorithm (CCGA)2
Two versions of a cooperative coevolutionary genetic algorithm.
2M. A. Potter and K. A. De Jong. “A cooperative coevolutionary approach to function optimization”. In: Parallel Problem Solving from
Nature — PPSN III. ed. by Y. Davidor, H.-P. Schwefel, and R. Männer. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994, pp. 249–257.
8/13

20. OPTIMIZATION ALGORITHMS: CCGA
Cooperative Coevolutionary Genetic Algorithm (CCGA)2
CCGA-1
2M. A. Potter and K. A. De Jong. “A cooperative coevolutionary approach to function optimization”. In: Parallel Problem Solving from
Nature — PPSN III. ed. by Y. Davidor, H.-P. Schwefel, and R. Männer. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994, pp. 249–257.
8/13

21. OPTIMIZATION ALGORITHMS: CCGA
Cooperative Coevolutionary Genetic Algorithm (CCGA)2
CCGA-2
2M. A. Potter and K. A. De Jong. “A cooperative coevolutionary approach to function optimization”. In: Parallel Problem Solving from
Nature — PPSN III. ed. by Y. Davidor, H.-P. Schwefel, and R. Männer. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994, pp. 249–257.
8/13

22. OPTIMIZATION ALGORITHMS: CCGA
Cooperative Coevolutionary Genetic Algorithm (CCGA)2
Evaluation of Vector 1 using the best solutions from the other populations
2M. A. Potter and K. A. De Jong. “A cooperative coevolutionary approach to function optimization”. In: Parallel Problem Solving from
Nature — PPSN III. ed. by Y. Davidor, H.-P. Schwefel, and R. Männer. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994, pp. 249–257.
8/13

23. OPTIMIZATION ALGORITHMS: CCGA
Cooperative Coevolutionary Genetic Algorithm (CCGA)2
Evaluation of Vector 1 using random solutions from the other populations
2M. A. Potter and K. A. De Jong. “A cooperative coevolutionary approach to function optimization”. In: Parallel Problem Solving from
Nature — PPSN III. ed. by Y. Davidor, H.-P. Schwefel, and R. Männer. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994, pp. 249–257.
8/13

24. CASE STUDIES
50x50.2 50x50.4
100x100.4 100x100.6
TABLE: Characteristics of our four case studies.
Case Study Size # Cells # UAVs
50x50.2 50x50 2500 2
50x50.4 50x50 2500 4
100x100.4 100x100 10000 4
100x100.6 100x100 10000 6
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25. RESULTS (I)
TABLE: Results of the optimisation of our four case studies.
Case Study Alg. Mean StDev Max.
Friedman
Rank
Wilcoxon
p-value
50x50.2
CACOC0 79.52 0.00 79.52 1.00 0.000
GA 95.07 0.11 95.12 2.13 0.000
CCGA-1 95.70 0.43 96.28 3.27 0.271
CCGA-2 95.82 0.30 96.28 3.60 —
50x50.4
CACOC0 96.68 0.00 96.68 1.00 0.000
GA 97.56 0.06 97.64 2.42 0.000
CCGA-1 97.71 0.28 98.48 3.07 0.092
CCGA-2 97.83 0.24 98.36 3.52 —
100x100.4
CACOC0 53.40 0.00 53.40 1.00 0.000
GA 77.90 0.21 78.53 2.00 0.000
CCGA-1 81.92 1.81 86.11 3.43 0.544
CCGA-2 82.18 1.64 85.34 3.57 —
100x100.6
CACOC0 62.03 0.00 62.03 1.00 0.000
GA 90.50 0.37 90.68 2.63 0.010
CCGA-1 91.34 1.18 93.77 3.07 0.688
CCGA-2 91.09 1.21 94.34 3.30 —
10/13

26. RESULTS (I)
TABLE: Results of the optimisation of our four case studies.
Case Study Alg. Mean StDev Max.
Friedman
Rank
Wilcoxon
p-value
50x50.2
CACOC0 79.52 0.00 79.52 1.00 0.000
GA 95.07 0.11 95.12 2.13 0.000
CCGA-1 95.70 0.43 96.28 3.27 0.271
CCGA-2 95.82 0.30 96.28 3.60 —
50x50.4
CACOC0 96.68 0.00 96.68 1.00 0.000
GA 97.56 0.06 97.64 2.42 0.000
CCGA-1 97.71 0.28 98.48 3.07 0.092
CCGA-2 97.83 0.24 98.36 3.52 —
100x100.4
CACOC0 53.40 0.00 53.40 1.00 0.000
GA 77.90 0.21 78.53 2.00 0.000
CCGA-1 81.92 1.81 86.11 3.43 0.544
CCGA-2 82.18 1.64 85.34 3.57 —
100x100.6
CACOC0 62.03 0.00 62.03 1.00 0.000
GA 90.50 0.37 90.68 2.63 0.010
CCGA-1 91.34 1.18 93.77 3.07 0.688
CCGA-2 91.09 1.21 94.34 3.30 —
10/13

27. RESULTS (II)
11/13

28. SURVEILLANCE EXAMPLE
12/13

29. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using GA and CCGAs
. . . achieved better coverage rates.
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . use our proposal in Predator-Prey scenarios
13/13

30. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using GA and CCGAs
. . . achieved better coverage rates.
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . use our proposal in Predator-Prey scenarios
13/13

31. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using GA and CCGAs
. . . achieved better coverage rates.
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . use our proposal in Predator-Prey scenarios
13/13

32. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using GA and CCGAs
. . . achieved better coverage rates.
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . use our proposal in Predator-Prey scenarios
13/13

33. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using GA and CCGAs
. . . achieved better coverage rates.
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . use our proposal in Predator-Prey scenarios
13/13

34. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using GA and CCGAs
. . . achieved better coverage rates.
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . use our proposal in Predator-Prey scenarios
13/13

35. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using GA and CCGAs
. . . achieved better coverage rates.
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . use our proposal in Predator-Prey scenarios
13/13

36. CONCLUSION AND FUTURE WORK
We have. . .
. . . added new features to CACOC (parameters)
. . . optimised these parameters using GA and CCGAs
. . . achieved better coverage rates.
We plan to. . .
. . . use CACOC in other different scenarios
. . . implement inter-swarm collaborations (different vehicles)
. . . use our proposal in Predator-Prey scenarios
13/13

37. QUESTIONS?
https://hunted.gforge.uni.lu/
https://pcog.uni.lu/
https://wwwen.uni.lu/snt/
https://wwwen.uni.lu/
A Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms
Daniel H. Stolfi1, Matthias R. Brust1, Grégoire Danoy1,2, Pascal Bouvry1,2
1 SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg
2 FSTC/CSC, University of Luxembourg, Luxembourg
This work relates to Department of Navy award N62909-18-1-2176 issued by the Office of Naval Research. The United States Government has a royalty-free license throughout the
world in all copyrightable material contained herein. This work is partially funded by the joint research programme UL/SnT-ILNAS on Digital Trust for Smart-ICT. The experiments
presented in this paper were carried out using the HPC facilities of the University of Luxembourg – see https://hpc.uni.lu.