@Article{Stolfi2023a,
   author         = {Stolfi, Daniel H. and Danoy, Grégoire},
   journal        = {Applied Sciences},
   title          = {Optimising Robot Swarm Formations by Using Surrogate Models and Simulations},
   year           = {2023},
   issn           = {2076-3417},
   number         = {10},
   volume         = {13},
   abstract       = {Optimising a swarm of many robots can be computationally demanding, especially when accurate simulations are required to evaluate the proposed robot configurations. Consequentially, the size of the instances and swarms must be limited, reducing the number of problems that can be addressed. In this article, we study the viability of using surrogate models based on Gaussian processes and artificial neural networks as predictors of the robots’ behaviour when arranged in formations surrounding a central point of interest. We have trained the surrogate models and tested them in terms of accuracy and execution time on five different case studies comprising three, five, ten, fifteen, and thirty robots. Then, the best performing predictors combined with ARGoS simulations have been used to obtain optimal configurations for the robot swarm by using our proposed hybrid evolutionary algorithm, based on a genetic algorithm and a local search. Finally, the best swarm configurations obtained have been tested on a number of unseen scenarios comprising different initial robot positions to evaluate the robustness and stability of the achieved robot formations. The best performing predictors exhibited speed increases of up to 3604 with respect to the ARGoS simulations. The optimisation algorithm converged in 91% of runs and stable robot formations were achieved in 79% of the unseen testing scenarios.},
   article-number = {5989},
   doi            = {10.3390/app13105989},
   url            = {https://www.mdpi.com/2076-3417/13/10/5989},
}

@Article{Stolfi2023,
   author   = {Daniel H. Stolfi and Grégoire Danoy},
   journal  = {Robotics and Autonomous Systems},
   title    = {Design and analysis of an E-Puck2 robot plug-in for the ARGoS simulator},
   year     = {2023},
   issn     = {0921-8890},
   pages    = {104412},
   volume   = {164},
   doi      = {https://doi.org/10.1016/j.robot.2023.104412},
   keywords = {E-puck2, ARGoS, Computer simulations, Sensors, Swarm robotic},
   url      = {https://www.sciencedirect.com/science/article/pii/S0921889023000519},
}

@Article{Stolfi2022b,
   author         = {Stolfi, Daniel H. and Danoy, Grégoire},
   journal        = {Applied Sciences},
   title          = {An Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System},
   year           = {2022},
   issn           = {2076-3417},
   number         = {20},
   volume         = {12},
   article-number = {10218},
   doi            = {10.3390/app122010218},
   url            = {https://www.mdpi.com/2076-3417/12/20/10218},
}

@Article{Stolfi2022a,
   author         = {Stolfi, Daniel H. and Brust, Matthias R. and Danoy, Grégoire and Bouvry, Pascal},
   journal        = {Drones},
   title          = {SuSy-EnGaD: Surveillance System Enhanced by Games of Drones},
   year           = {2022},
   issn           = {2504-446X},
   number         = {1},
   volume         = {6},
   article-number = {13},
   doi            = {10.3390/drones6010013},
   url            = {https://www.mdpi.com/2504-446X/6/1/13},
}

@article{Stolfi2021c,
   title = {A competitive Predator–Prey approach to enhance surveillance by UAV swarms},
   journal = {Applied Soft Computing},
   volume = {111},
   pages = {107701},
   year = {2021},
   issn = {1568-4946},
   doi = {https://doi.org/10.1016/j.asoc.2021.107701},
   url = {https://www.sciencedirect.com/science/article/pii/S1568494621006220},
   author = {Daniel H. Stolfi and Matthias R. Brust and Grégoire Danoy and Pascal Bouvry},
   keywords = {Swarm robotics, Computer simulation, Mobility model, Unmanned aerial vehicle, Competitive coevolutionary genetic algorithm},
}

@article{STOLFI2021107566,
   title = {Yellow Swarm: LED panels to advise optimal alternative tours to drivers in the city of Malaga},
   journal = {Applied Soft Computing},
   volume = {109},
   pages = {107566},
   year = {2021},
   issn = {1568-4946},
   doi = {https://doi.org/10.1016/j.asoc.2021.107566},
   url = {https://www.sciencedirect.com/science/article/pii/S1568494621004877},
   author = {Daniel H. Stolfi and Enrique Alba},
   keywords = {epiGenetic algorithm, Smart mobility, LED panel, Travel time, Greenhouse gas emissions, Fuel consumption}
}

@Article{Stolfi2021a,
   author    = {Daniel H. Stolfi and Matthias R. Brust and Gr{\'{e}}goire Danoy and Pascal Bouvry},
   journal   = {Swarm Intelligence},
   title     = {CONSOLE: intruder detection using a UAV swarm and security rings},
   year      = {2021},
   issn      = {1935-3820},
   number    = {3},
   pages     = {205--235},
   volume    = {15},
   doi       = {10.1007/s11721-021-00193-7},
   publisher = {Springer Science and Business Media {LLC}},
   refid     = {Stolfi2021},
   url       = {https://doi.org/10.1007/s11721-021-00193-7},
}

@Article{Brust2021,
   author    = {Matthias R. Brust and Gr{\'{e}}goire Danoy and Daniel H. Stolfi and Pascal Bouvry},
   journal   = {Discover Internet of Things},
   title     = {Swarm-based counter {UAV} defense system},
   year      = {2021},
   month     = {feb},
   number    = {1},
   volume    = {1},
   doi       = {10.1007/s43926-021-00002-x},
   publisher = {Springer Science and Business Media {LLC}},
}

@article{Stolfi2021,
   author = {Stolfi, Daniel H. and Brust, Matthias R. and Danoy, Gr{\'{e}}goire and Bouvry, Pascal},
   doi = {10.3389/frobt.2021.616950},
   issn = {2296-9144},
   journal = {Frontiers in Robotics and AI},
   month = {feb},
   title = {{UAV-UGV-UMV Multi-Swarms for Cooperative Surveillance}},
   url = {https://www.frontiersin.org/articles/10.3389/frobt.2021.616950/full},
   volume = {8},
   year = {2021}
}

@Article{s20092566,
   AUTHOR = {Stolfi, Daniel H. and Brust, Matthias R. and Danoy, Grégoire and Bouvry, Pascal},
   TITLE = {Emerging Inter-Swarm Collaboration for Surveillance Using Pheromones and Evolutionary Techniques},
   JOURNAL = {Sensors},
   VOLUME = {20},
   YEAR = {2020},
   NUMBER = {9},
   ARTICLE-NUMBER = {2566},
   URL = {https://www.mdpi.com/1424-8220/20/9/2566},
   ISSN = {1424-8220},
   DOI = {10.3390/s20092566}
}

@article{doi:10.1080/10630732.2019.1586223,
   author = {Daniel H. Stolfi and Enrique Alba and Xin Yao},
   title = {Can I Park in the City Center? Predicting Car Park Occupancy Rates in Smart Cities},
   journal = {Journal of Urban Technology},
   volume = {27},
   number = {4},
   pages = {27-41},
   year  = {2020},
   publisher = {Routledge},
   doi = {10.1080/10630732.2019.1586223},
   URL = {https://doi.org/10.1080/10630732.2019.1586223},
   eprint = {https://doi.org/10.1080/10630732.2019.1586223}
}

@incollection{STOLFI2019361,
   title = "Chapter 14 - Sustainable Road Traffic Using Evolutionary Algorithms",
   editor = "Javier Faulin and Scott E. Grasman and Angel A. Juan and Patrick Hirsch",
   booktitle = "Sustainable Transportation and Smart Logistics",
   publisher = "Elsevier",
   pages = "361 - 380",
   year = "2019",
   isbn = "978-0-12-814242-4",
   doi = "https://doi.org/10.1016/B978-0-12-814242-4.00014-4",
   url = "http://www.sciencedirect.com/science/article/pii/B9780128142424000144",
   author = "Daniel H. Stolfi and Enrique Alba",
   keywords = "Evolutionary algorithm, Intelligent transportation systems, LED panels, Road traffic, Smart mobility, Wi-Fi connections"
}

@article{STOLFI2018952,
   author = {Daniel H. Stolfi and Enrique Alba},
   doi = {10.1016/j.asoc.2018.07.032},
   issn = {1568-4946},
   journal = {Applied Soft Computing},
   keywords = {Evolutionary algorithm; Road traffic; Smart city; Smart mobility; Gas emissions; Wi-Fi connections},
   pages = {952--963},
   title = {{Green Swarm: Greener routes with bio-inspired techniques}},
   url = {http://www.sciencedirect.com/science/article/pii/S1568494618304204},
   volume = {71},
   year = {2018}
}

@article{STOLFI201836,
   title = "Generating realistic urban traffic flows with evolutionary techniques",
   journal = "Engineering Applications of Artificial Intelligence",
   volume = "75",
   pages = "36 - 47",
   year = "2018",
   issn = "0952-1976",
   doi = "https://doi.org/10.1016/j.engappai.2018.07.009",
   url = "http://www.sciencedirect.com/science/article/pii/S0952197618301532",
   author = "Daniel H. Stolfi and Enrique Alba",
   keywords = "Evolutionary algorithm, Traffic simulation, Smart mobility, SUMO, Road traffic optimization, O–D matrix"
}

@article{STOLFI2018250,
   author = "Stolfi, Daniel H. and Alba, Enrique",
   doi = "10.1016/j.ins.2017.10.005",
   issn = "0020-0255",
   journal = "Information Sciences",
   keywords = "Evolutionary algorithm; Epigenetics; MKP; Bio-inspiration",
   number = "Supplement C",
   pages = "250--272",
   title = "{Epigenetic algorithms: A New way of building GAs based on epigenetics}",
   url = "http://www.sciencedirect.com/science/article/pii/S0020025517309921",
   volume = "424",
   year = "2018"
}

@article{STOLFI2014181,
   title = "Red Swarm: Reducing travel times in smart cities by using bio-inspired algorithms",
   journal = "Applied Soft Computing",
   volume = "24",
   pages = "181 - 195",
   year = "2014",
   issn = "1568-4946",
   doi = "https://doi.org/10.1016/j.asoc.2014.07.014",
   url = "http://www.sciencedirect.com/science/article/pii/S1568494614003457",
   author = "Daniel H. Stolfi and Enrique Alba",
   keywords = "Evolutionary algorithm, Road traffic, Smart city, Smart mobility, WiFi connections, Traffic light"
}