BIBLIOGRAPHIE
[1] Nur Aira ABD RAHMAN et al. « A
coverage path planning approach for autonomous radiation mapping with a mobile
robot ». In : International Journal of Advanced Robotic Systems
19.4 (2022), p. 17298806221116483.
[2] Laith ABUALIGAH et al. « Reptile
Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer ». In :
Expert Systems with Applications 191 (2022), p. 116158.
[3] Laith ABUALIGAH et al. « The
arithmetic optimization algorithm ». In : Computer methods in applied
mechanics and engineering 376 (2021), p. 113609.
[4] Afsoon AFZAL et al. « A study on
the challenges of using robotics simulators for testing ». In : arXiv
preprint arXiv:2004.07368 (2020).
[5] Jeffrey O AGUSHAKA, Absalom E
EZUGWU et Laith ABUALIGAH. « Dwarf
mongoose optimization algorithm ». In : Computer methods in applied
mechanics and engineering 391 (2022), p. 114570.
[6] Somaye AHMADI, H. KEBRIAEI
et Hadi MORADI. « Constrained coverage path
planning: evolutionary and classical approaches ». In : Robotica
36 (fév. 2018), p. 1-21. DOI :
10.1017/S0263574718000139.
[7] Mohammad AL KHAWALDAH et Andreas
NUCHTER. « Enhanced frontier-based exploration for indoor
environment with multiple robots ». In : Advanced Robotics 29
(avr. 2015). DOI : 10.1080/01691864.2015.1015443.
[8] Sankalap ARORA et Priyanka
ANAND. « Binary butterfly optimization approaches for
feature selection ». In : Expert Systems with Applications 116
(2019), p. 147-160.
[9] Sankalap ARORA et Satvir
SINGH. « An improved butterfly optimization algorithm for
global optimization ». In : Advanced Science, Engineering and Medicine
8.9 (2016), p. 711-717.
[10] Sankalap ARORA et Satvir
SINGH. « Butterfly algorithm with levy flights for global
optimization ». In : 2015 International conference on signal
processing, computing and control (ISPCC). IEEE. 2015, p. 220-224.
[11] Sankalap ARORA et Satvir
SINGH. « Butterfly optimization algorithm: a novel
approach for global optimization ». In : Soft Computing 23.3
(2019), p. 715-734.
[12] Sankalap ARORA, Satvir SINGH
et Kaan YETILMEZSOY. « A modified butterfly
optimization algorithm for mechanical design optimization problems ». In :
Journal of the Brazilian Society of Mechanical Sciences and Engineering
40.1 (2018), p. 1-17.
[13] Adel Saad ASSIRI. « On the
performance improvement of Butterfly Optimization approaches for global
optimization and Feature Selection ». In : Plos one 16.1 (2021),
e0242612.
[14] Mahdi AZIZI. « Atomic orbital
search: A novel metaheuristic algorithm ». In : Applied Mathematical
Modelling 93 (2021), p. 657-683.
[15] Antoine BAUTIN, Olivier SIMONIN
et François CHARPILLET. « MinPos : A
Novel Frontier Allocation Algorithm for Multi-robot Exploration ». In :
oct. 2012, p. 496-508. ISBN : 978-3-642-33514-3. DOI
: 10.1007/978-3-642-33515-0_49.
135
Bibliographie
[16] Amine BENDAHMANE et Redouane TLEMSANI. « Unknown
area exploration for robots with energy constraints using a modified Butterfly
Optimization Algorithm ». In : Soft Computing 27 (2023), p.
3785-3804. DOI : 10.1007/s00500-022-07530w.
[17] James BERGSTRA et Yoshua BENGIO. « Random search
for hyper-parameter optimization. » In : Journal of machine learning
research 13.2 (2012).
[18] James BERGSTRA, Dan YAMINS, David D COx et al. «
Hyperopt: A python library for optimizing the hyperparameters of machine
learning algorithms ». In : Proceedings of the 12th Python in science
conference. T. 13. Citeseer. 2013, p. 20.
[19] Francesco BISCANI et Dario IZZO. « A parallel
global multiobjective framework for optimization: pagmo ». In :
Journal of Open Source Software 5.53 (2020), p. 2338. DOI :
10.21105/joss.02338.
[20] Johann BORENSTEIN, Yoram KOREN et al. « The vector
field histogram-fast obstacle avoidance for mobile robots ». In : IEEE
transactions on robotics and automation 7.3 (1991), p. 278-288.
[21] Ersin BÜYÜK. « Pareto-based
multiobjective particle swarm optimization: examples in geophysical modeling
». In : Optimisation Algorithms and Swarm Intelligence.
In-techOpen, 2021.
[22] Byoung-Suk CHOI et al. « A hierarchical algorithm
for indoor mobile robot localization using RFID sensor fusion ». In :
IEEE Transactions on industrial electronics 58.6 (2011), p.
2226-2235.
[23] Howie CHOSET et Philippe PIGNON. « Coverage path
planning: The boustrophedon cellular decomposition ». In : Field and
service robotics. Springer. 1998, p. 203-209.
[24] Alberto COLORNI, Marco DORIGO, Vittorio MANIEZZO et al.
« Distributed optimization by ant colonies ». In : Proceedings of
the first European conference on artificial life. T. 142. Paris, France.
1991, p. 134-142.
[25] Nichael Lynn CRAMER. « A representation for the
Adaptive Generation of Simple Sequential Programs ». In : Proceedings
of an International Conference on Genetic Algorithms and the Applications.
Sous la dir. de John J. GREFENSTETTE. Carnegie-Mellon University, Pittsburgh,
USA, juin 1985, p. 183-187.
[26] Marija DAKULOVIc, Sanja HORVATIc et Ivan
PETROVIé. « Complete Coverage D* Algorithm for Path Planning of a
Floor-Cleaning Mobile Robot ». In : IFAC Proceedings Volumes 44.1
(2011). 18th IFAC World Congress, p. 5950-5955. ISSN : 1474-6670. DOI :
https://doi.org/10.3182/20110828-6-IT-1002.03400.
[27] Andrew DAVENPORT et al. « GENET: A connectionist
architecture for solving constraint satisfaction problems by iterative
improvement ». In : AAAI. 1994, p. 325-330.
[28] Susana Estefany DE LEÔN-ALDACO, Hugo CALLEJA et
Jesús Aguayo ALQuICIRA. « Me-taheuristic optimization methods
applied to power converters: A review ». In : IEEE Transactions on
Power Electronics 30.12 (2015), p. 6791-6803.
[29] Sihao DENG et al. « Application of external axis in
robot-assisted thermal spraying ». In : Journal of thermal spray
technology 21 (2012), p. 1203-1215.
136
Bibliographie
[30] Berat DoðAN et Tamer ÖLMEZ. « A new
metaheuristic for numerical function optimization: Vortex Search algorithm
». In : Information sciences 293 (2015), p. 125-145.
[31] Alexey DosovITsKIY et al. « CARLA: An open urban
driving simulator ». In : Conference on robot learning. PMLR.
2017, p. 1-16.
[32] Akif DURDU et al. « Convolutional Neural Networks
Based Active SLAM and Exploration ». In : Avrupa Bilim ve Teknoloji
Dergisi 22 (2021), p. 342-346.
[33] Hugh DuRRANT-WHYTE et al. « Field and service
applications-an autonomous straddle carrier for movement of shipping
containers-from research to operational autonomous systems ». In :
IEEE Robotics & Automation Magazine 14.3 (2007), p. 14-23.
[34] Alberto ELFEs. « Using occupancy grids for mobile
robot perception and navigation ». In : Computer 22.6 (1989), p.
46-57.
[35] Yuqi FAN et al. « A self-adaption butterfly
optimization algorithm for numerical optimization problems ». In :
IEEE Access 8 (2020), p. 88026-88041.
[36] J.D. FARMER, N. PACKARD et A. PERELsoN. « The immune
system, adaptation and machine learning ». In : Physica D 2
(1986), 187-204.
[37] Diego FERIGo et al. « Gym-ignition: Reproducible
robotic simulations for reinforcement learning ». In : 2020 IEEE/SICE
International Symposium on System Integration (SII). IEEE. 2020, p.
885-890.
[38] Simon FoNG, Suash DEB et Ankit CHAuDHARY. « A
review of metaheuristics in robotics ». In : Computers &
Electrical Engineering 43 (2015), p. 278-291.
[39] Miguel GARCíA et al. « Voronoi-Based Space
Partitioning for Coordinated Multi-Robot Exploration ». In : JoPha:
Journal of Pysical Agents, ISSN 1888-0258, Vol. 1, N°. 1, 2007, pags. 37-44
1 (jan. 2007). DoI: 10.14198/JoPha.2007.1.1.05.
[40] Fred GLovER. « Future paths for integer programming
and links to artificial intelligence ». In : Computers &
operations research 13.5 (1986), p. 533-549.
[41] David E. GoLDBERG. Genetic Algorithms in Search,
Optimization, and Machine Learning. New York: Addison-Wesley, 1989.
[42] Nir GREsHLER et al. « Cooperative multi-agent path
finding: beyond path planning and collision avoidance ». In: 2021
International Symposium on Multi-Robot and Multi-Agent Systems (MRS).
IEEE. 2021, p. 20-28.
[43] Faiza GuL, Suleman MIR et Imran MIR. « Coordinated
multi-robot exploration: Hybrid stochastic optimization approach ». In :
AIAA SCITECH2022 Forum. 2022, p. 1414.
[44] Yanju Guo, Xianjie LIu et Lei CHEN. « Improved
butterfly optimisation algorithm based on guiding weight and population restart
». In : Journal of Experimental & Theoretical Artificial
Intelligence 33.1 (2021), p. 127-145.
[45] Nikolaus HANsEN, Sibylle MüLLER et Petros
KouMouTsAKos. « Reducing the Time Complexity of the Derandomized Evolution
Strategy with Covariance Matrix Adaptation (CMA-ES) ». In :
Evolutionary computation 11 (fév. 2003), p. 1-18. DoI : 10.
1162/106365603321828970.
[46] Peter E. HART, Nils J. NILssoN et Bertram RAPHAEL.
« A Formal Basis for the Heuristic Determination of Minimum Cost Paths
». In : IEEE Transactions on Systems Science and Cybernetics 4.2
(1968), p. 100-107. DoI : 10.1109/TSSC.1968.300136.
137
Bibliographie
[47] Dirk HOLz et al. « Evaluating the Efficiency of
Frontier-based Exploration Strategies ». In : t. 1. Juill. 2010, p. 1
-8.
[48] Erno HORVATH, Claudiu POzNA et Radu-Emil PRECUP. «
Robot coverage path planning based on iterative structured orientation ».
In : Acta Polytechnica Hungarica 15.2 (2018), p. 231-249.
[49] Luca IOCCHI, Luca MARCHETTI et Daniele NARDI. «
Multi-robot patrolling with coordinated behaviours in realistic environments
». In : 2011 IEEE/RSJ International Conference on Intelligent Robots
and Systems. IEEE. 2011, p. 2796-2801.
[50] Nick JAKOBI, Phil HUSBANDS et Inman HARVEY. « Noise
and the reality gap: The use of simulation in evolutionary robotics ». In
: Advances in Artificial Life: Third European Conference on Artificial Life
Granada, Spain, June 4-6, 1995 Proceedings 3. Springer. 1995, p.
704-720.
[51] Seyed Mohammad Jafar JALALI et al. « Evolving
artificial neural networks using butterfly optimization algorithm for data
classification ». In : International conference on neural information
processing. Springer. 2019, p. 596-607.
[52] Albina KAMALOVA, Ki Dong KIM et Suk Gyu LEE. «
Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms
». In : IEEE Access 8 (2020), p. 190342190355.
[53] Albina KAMALOVA et al. « Multi-Robot Exploration
Based on Multi-Objective Grey Wolf Optimizer ». In : Applied Sciences
9 (juill. 2019), p. 2931. DOI : 10 . 3390/ app9142931.
[54] Pierre KANCIR. « Méthodologie de conception
de système multi-robots: De la simulation à la
démonstration ». Thèse de doct. Université de
Bretagne Sud, 2018.
[55] Dervis KARABOGA. « An Idea Based on Honey Bee Swarm
for Numerical Optimization, Technical Report - TR06 ». In : Technical
Report, Erciyes University (jan. 2005).
[56] Géza KATONA, Balázs LÉNART et
János JUHASz. « Parallel ant colony algorithm for shortest path
problem ». In : Periodica Polytechnica Civil Engineering 63.1
(2019), p. 243-254.
[57] Ali KAVEH et Taha BAKHSHPOORI. « Metaheuristics:
outlines, MATLAB codes and examples ». In : (2019).
[58] J. KENNEDY et R. EBERHART. « Particle swarm
optimization ». In : Proceedings of ICNN'95 - International Conference
on Neural Networks. T. 4. 1995, 1942-1948 vol.4. DOI :
10.1109/ICNN.1995.488968.
[59] Scott KIRKPATRICK, C Daniel GELATT JR et Mario P VECCHI.
« Optimization by simulated annealing ». In : science
220.4598 (1983), p. 671-680.
[60] Nathan KOENIG et Andrew HOWARD. « Design and use
paradigms for gazebo, an open-source multi-robot simulator ». In :
2004 IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS)(IEEE Cat. No. 04CH37566). T. 3. IEEE. 2004, p. 21492154.
[61] Guocheng LI et al. « An improved butterfly
optimization algorithm for engineering design problems using the cross-entropy
method ». In : Symmetry 11.8 (2019), p. 1049.
138
Bibliographie
[62] Matteo LUPERTO et al. « Robot
exploration of indoor environments using incomplete and inaccurate prior
knowledge ». In : Robotics and Autonomous Systems 133 (2020), p.
103622.
[63] Ellips MASEHIAN et MR
AMIN-NASERI. « A voronoi diagram-visibility
graph-potential field compound algorithm for robot path planning ». In :
Journal of Robotic Systems 21.6 (2004), p. 275-300.
[64] Ashkan MEMARI, Robiah AHMAD
et Abd Rahman Abdul RAHIM. « Metaheuristic
algorithms: guidelines for implementation ». In : Journal of Soft
Computing and Decision Support Systems 4.6 (2017), p. 1-6.
[65] PE MERGOS et Anastasios
SEXTOS. Multi-objective optimum selection ofground motion
records with genetic algorithms. IEEE, juin 2018.
[66] Seyedali MIRJALILI, Seyed Mohammad
MIRJALILI et Andrew LEWIS. « Grey Wolf
Optimizer ». In : Advances in Engineering Software 69 (2014), p.
46-61. ISSN : 0965-9978. DOI :
https://doi.org/10.1016/j.advengsoft.2013.12.007.
[67] Himanshu MITTAL et al. «
Gravitational search algorithm: A comprehensive analysis of recent variants
». In : Multimedia Tools and Applications 80 (2021), p.
7581-7608.
[68] Sing Yee NG et Nur Syazreen
AHMAD. « A Bug-Inspired Algorithm for Obstacle Avoidance
of a Nonholonomic Wheeled Mobile Robot with Constraints ». In :
Intelligent Computing. Sous la dir. de Kohei ARAI,
Rahul BHATIA et Supriya KAPOOR. Cham :
Springer International Publishing, 2019, p. 1235-1246.
[69] G PAVAI et TV GEETHA.
« A survey on crossover operators ». In : ACM Computing Surveys
(CSUR) 49.4 (2016), p. 1-43.
[70] Hindriyanto Dwi PURNOMO et Hui-Ming
WEE. « Soccer game optimization with substitute players
». In : Journal of computational and applied mathematics 283
(2015), p. 79-90.
[71] Esmat RASHEDI, Hossein
NEZAMABADI-POUR et Saeid SARYAZDI. «
GSA: a gravitational search algorithm ». In : Information sciences
179.13 (2009), p. 2232-2248.
[72] Eric ROHMER, Surya PN SINGH
et Marc FREESE. « V-REP: A versatile and
scalable robot simulation framework ». In : 2013 IEEE/RSJ
international conference on intelligent robots and systems. IEEE. 2013, p.
1321-1326.
[73] Ali El ROMEH et Seyedali
MIRJALILI. « Multi-Robot Exploration of Unknown Space
Using Combined Meta-Heuristic Salp Swarm Algorithm and Deterministic
Coordinated Multi-Robot Exploration ». In : Sensors 23.4 (2023),
p. 2156.
[74] Sajad SAEEDI et al. « Occupancy
grid map merging for multiple robot simultaneous localization and mapping
». In : International Journal ofRobotics and Automation 30.2
(2015), p. 149-157.
[75] Jeffrey R SAMPSON. Adaptation in
natural and artificial systems (John H. Holland). 1976.
[76] Sushmita SHARMA et al. « MPBOA-A
novel hybrid butterfly optimization algorithm with symbiosis organisms search
for global optimization and image segmentation ». In : Multimedia
Tools and Applications 80.8 (2021), p. 12035-12076.
139
Bibliographie
[77] Zongyuan SHEN, James P. WiLsoN
et Shalabh GUPTA. «
E*+: An Online
Coverage Path Planning Algorithm for Energy-constrained Autonomous Vehicles
». In : Global Oceans2020:Singapore- U.S. GulfCoast. 2020,p. 1-6.
Doi: 10.1109/IEEECONF38699. 2020.9389353.
[78] Rakesh SHREsTHA et al. « Learned
map prediction for enhanced mobile robot exploration ». In : 2019
International Conference on Robotics and Automation (ICRA). IEEE. 2019, p.
1197-1204.
[79] Junnan SoNG et Shalabh
GUPTA. « c*: An Online Coverage Path Planning Algorithm
». In : IEEE Transactions on Robotics 34 (fév. 2018), p.
526-533. Doi: 10.1109/ TRO.2017.2780259.
[80] Rainer SToRN et Kenneth
PRiCE. « Differential evolution-a simple and efficient
heuristic for global optimization over continuous spaces ». In :
Journal of global optimization 11.4 (1997), p. 341.
[81] Daniel Perea STROM, Igor
BoGosLAVsKYi et Cyrill STACHNiss. «
Robust exploration and homing for autonomous robots ». In : Robotics
and Autonomous Systems 90 (2017), p. 125-135.
[82] Lei TAi et Ming LiU.
« A robot exploration strategy based on q-learning network ». In :
2016 IEEE international conference on real-time computing and robotics
(RCAR). IEEE. 2016, p. 57-62.
[83] Mohammad TUBisHAT et al. « Dynamic
butterfly optimization algorithm for feature selection ». In : IEEE
Access 8 (2020), p. 194303-194314.
[84] Zhongmin WANG, Qifang LUo
et Yongquan ZHoU. « Hybrid metaheuristic
algorithm using butterfly and flower pollination base on mutualism mechanism
for global optimization problems ». In : Engineering with Computers
37.4 (2021), p. 3665-3698.
[85] David H WoLPERT et William G
MACREADY. « No free lunch theorems for optimization
». In : IEEE transactions on evolutionary computation 1.1 (1997),
p. 67-82.
[86] Benjie XiAo et al. « Ant colony
optimisation algorithm-based multi-robot exploration ». In :
International Journal of Modelling, Identification and Control 18.1
(2013), p. 41-46.
[87] Lei XiE et al. « Tuna swarm
optimization: a novel swarm-based metaheuristic algorithm for global
optimization ». In : Computational intelligence and Neuroscience
2021 (2021).
[88] Anupam YADAV et al. « AEFA:
Artificial electric field algorithm for global optimization ». In :
Swarm and Evolutionary Computation 48 (2019), p. 93-108.
[89] Brian YAMAUCHi. « A frontier-based
approach for autonomous exploration ». In : Proceedings 1997 IEEE
International Symposium on Computational Intelligence in Robotics and
Automation CIRA'97. 'Towards New Computational Principles for Robotics and
Au-tomation'. 1997, p. 146-151. Doi :
10.1109/CIRA.1997.613851.
[90] Brian YAMAUCHi. « Frontier-based
exploration using multiple robots ». In : jan. 1998, p. 47-53.
isBN : 0-89791-983-1. Doi:
10.1145/280765.280773.
[91] Xin-She YANG. « A new
metaheuristic bat-inspired algorithm ». In : Nature inspired
cooperative strategies for optimization (NICSO 2010) (2010), p. 65-74.
140
Bibliographie
[92] Xin-She YANG. « Flower pollination algorithm for
global optimization ». In : International conference on unconventional
computing and natural computation. Springer. 2012, p. 240-249.
[93] Xin-She YANG. Nature-inspired metaheuristic
algorithms. Luniver press, 2010.
[94] Zuozhong YIN et al. « A Delivery robot cloud
platform based on microservice ». In : Journal of Robotics 2021
(2021), p. 1-10.
[95] Chao Yu et al. « Asynchronous Multi-Agent
Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative
Exploration ». In : arXiv preprint arXiv:2301.03398 (2023).
[96] Mengjian ZHANG et al. « A Chaotic Hybrid Butterfly
Optimization Algorithm with Particle Swarm Optimization for High-Dimensional
Optimization Problems ». In : Symmetry 12.11 (2020), p. 1800.
[97] Xiangyang ZHI, Xuming HE et Sören SCHWERTFEGER.
« Learning autonomous exploration and mapping with semantic vision ».
In : Proceedings of the 2019 International Conference on Image, Video and
Signal Processing. 2019, p. 8-15.
[98] Yi ZHOu et al. « A PSO-inspired Multi-Robot Map
Exploration Algorithm Using Frontier-Based Strategy ». In :
International Journal of System Dynamics Applications, 2 (avr. 2013),
p. 1-13. DOI : 10.4018/ijsda.2013040101.
[99] Mohammad ZOuNEMAT-KERMANI, Amin MAHDAVI-MEYMAND et
Reinhard HINKELMANN. « Nature-inspired algorithms in sanitary engineering:
modelling sediment transport in sewer pipes ». In : Soft Computing
25.8 (2021), p. 6373-6390.
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