A. Auger, S. Finck, N. Hansen, and R. Ros, BBOB 2009: Comparison Tables of All Algorithms on All Noiseless Functions, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00471251

H. G. Beyer, The Theory of Evolution Strategies, Natural Computing Series, 2001.
DOI : 10.1007/978-3-662-04378-3

D. Bratton and J. Kennedy, Defining a Standard for Particle Swarm Optimization, 2007 IEEE Swarm Intelligence Symposium, pp.120-127368035, 2007.
DOI : 10.1109/SIS.2007.368035

J. F. Chang, S. C. Chu, J. F. Roddick, and J. S. Pan, A parallel particle swarm optimization algorithm with communication strategies, J. Inf. Sci. Eng, vol.21, issue.4, pp.809-818, 2005.

M. Clerc and J. Kennedy, The particle swarm - explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, vol.6, issue.1, pp.58-73, 2002.
DOI : 10.1109/4235.985692

M. Clerc, Beyond standard particle swarm optimisation, pp.46-61, 2010.
DOI : 10.4018/978-1-4666-1592-2.ch001

S. Das and P. N. Suganthan, Differential Evolution: A Survey of the State-of-the-Art, IEEE Transactions on Evolutionary Computation, vol.15, issue.1, pp.4-31, 2011.
DOI : 10.1109/TEVC.2010.2059031

M. Gallagher, Clustering Problems for More Useful Benchmarking of Optimization Algorithms, Simulated Evolution and Learning -10th International Conference Proceedings. pp, pp.131-142978, 2014.
DOI : 10.1007/978-3-319-13563-2_12

M. Gardner, A. W. Mcnabb, and K. D. Seppi, A speculative approach to parallelization in particle swarm optimization, Swarm Intelligence, vol.1, issue.1, pp.77-116, 2012.
DOI : 10.1007/s11721-011-0066-8

N. I. Gould, D. Orban, and P. L. Toint, CUTEr and SifDec, ACM Transactions on Mathematical Software, vol.29, issue.4, pp.373-394, 2003.
DOI : 10.1145/962437.962439

N. Hansen and A. Ostermeier, Completely Derandomized Self-Adaptation in Evolution Strategies, Evolutionary Computation, vol.9, issue.2, 2003.
DOI : 10.1016/0004-3702(95)00124-7

M. Keijzer, J. J. Merelo, G. Romero, and M. Schoenauer, Evolving Objects: A General Purpose Evolutionary Computation Library, In: Artificial Evolution. pp, pp.231-244, 2001.
DOI : 10.1007/3-540-46033-0_19

J. Kennedy and R. C. Eberhart, Particle swarm optimization, Proceedings of ICNN'95, International Conference on Neural Networks, pp.1942-1948, 1995.
DOI : 10.1109/ICNN.1995.488968

A. Latorre, S. Muelas, and J. M. Pena, Large scale global optimization: Experimental results with MOS-based hybrid algorithms, 2013 IEEE Congress on Evolutionary Computation, pp.2742-2749, 2013.
DOI : 10.1109/CEC.2013.6557901

B. Mahdad, K. Srairi, T. Bouktir, and M. Benbouzid, Fuzzy Controlled Parallel PSO to Solving Large Practical Economic Dispatch, Proceedings of the 2010 IEEE International Conference of the IEEE Industrial Electronics Society, pp.2695-2701, 2010.
DOI : 10.1109/iecon.2010.5675112

URL : https://hal.archives-ouvertes.fr/hal-00564733

A. Mcnabb, C. Monson, and K. Seppi, Parallel PSO using MapReduce, 2007 IEEE Congress on Evolutionary Computation, pp.7-14, 2007.
DOI : 10.1109/CEC.2007.4424448

J. Nelder and R. Mead, A Simplex Method for Function Minimization, The Computer Journal, vol.7, issue.4, pp.308-311, 1965.
DOI : 10.1093/comjnl/7.4.308

K. E. Parsopoulos and M. N. Vrahatis, Parameter selection and adaptation in Unified Particle Swarm Optimization, Mathematical and Computer Modelling, vol.46, issue.1-2, pp.198-213, 2007.
DOI : 10.1016/j.mcm.2006.12.019

M. J. Powell, Developments of NEWUOA for minimization without derivatives, IMA Journal of Numerical Analysis, vol.28, issue.4, p.47, 2008.
DOI : 10.1093/imanum/drm047

J. F. Schutte, J. A. Reinbolt, B. J. Fregly, R. T. Haftka, and A. D. George, Parallel global optimization with the particle swarm algorithm, International Journal for Numerical Methods in Engineering, vol.28, issue.13, pp.2296-2315, 2003.
DOI : 10.1002/nme.1149

H. P. Schwefel, Numerical Optimization of Computer Models, pp.1995-1997, 1981.

Y. Shi and R. C. Eberhart, A modified particle swarm optimizer, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp.69-73, 1998.
DOI : 10.1109/ICEC.1998.699146

R. Storn and K. Price, Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, vol.11, issue.4, pp.341-3591008202821328, 1997.
DOI : 10.1023/A:1008202821328

P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. P. Chen et al., Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization, 2005.

I. C. Trelea, The particle swarm optimization algorithm: convergence analysis and parameter selection, Information Processing Letters, vol.85, issue.6, pp.317-325, 2003.
DOI : 10.1016/S0020-0190(02)00447-7

URL : https://hal.archives-ouvertes.fr/hal-01313364

M. Zambrano-bigiarini, M. Clerc, and R. Rojas, Standard particle swarm optimisation 2011 at cec-2013: A baseline for future pso improvementsa) DE outperforms everything for the specific policy SAiso (1 + 1) SA SACov CM A, IEEE Congress on Evolutionary Computation, pp.2337-234486, 2013.

S. Sacov, C. De, and . So, With the neural network, PSO is clearly the best algorithm SAiso (1 + 1)

S. Sacov, C. De, and . So, CMA is the best performing algorithm for Conformant Planning SAiso (1 + 1)

S. Escov, 86 CM A ? ES 100.00 100.00 100.00 100.00 100.00 64.29 100

S. Sacov, C. De, and . So, For Fuzzy control, SA-iso is the best algorithm SAiso (1 + 1)

S. Sacov and C. Saiso, DE outperforms everything for the specific policy SAiso (1 + 1), p.86

S. Sacov, C. De, and . So, With the neural network, PSO is clearly the best algorithm SAiso (1 + 1)

S. Sacov, C. De, and . So, DE is the best performing algorithm for Conformant Planning SAiso (1 + 1), p.29

S. Sacov, C. De, and . So, For Fuzzy control, SA-iso is the best algorithm SAiso (1 + 1)