BBOB 2009: Comparison Tables of All Algorithms on All Noiseless Functions, 2010. ,
URL : https://hal.archives-ouvertes.fr/inria-00471251
The Theory of Evolution Strategies, Natural Computing Series, 2001. ,
DOI : 10.1007/978-3-662-04378-3
Defining a Standard for Particle Swarm Optimization, 2007 IEEE Swarm Intelligence Symposium, pp.120-127368035, 2007. ,
DOI : 10.1109/SIS.2007.368035
A parallel particle swarm optimization algorithm with communication strategies, J. Inf. Sci. Eng, vol.21, issue.4, pp.809-818, 2005. ,
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
Beyond standard particle swarm optimisation, pp.46-61, 2010. ,
DOI : 10.4018/978-1-4666-1592-2.ch001
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
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
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
CUTEr and SifDec, ACM Transactions on Mathematical Software, vol.29, issue.4, pp.373-394, 2003. ,
DOI : 10.1145/962437.962439
Completely Derandomized Self-Adaptation in Evolution Strategies, Evolutionary Computation, vol.9, issue.2, 2003. ,
DOI : 10.1016/0004-3702(95)00124-7
Evolving Objects: A General Purpose Evolutionary Computation Library, In: Artificial Evolution. pp, pp.231-244, 2001. ,
DOI : 10.1007/3-540-46033-0_19
Particle swarm optimization, Proceedings of ICNN'95, International Conference on Neural Networks, pp.1942-1948, 1995. ,
DOI : 10.1109/ICNN.1995.488968
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
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
Parallel PSO using MapReduce, 2007 IEEE Congress on Evolutionary Computation, pp.7-14, 2007. ,
DOI : 10.1109/CEC.2007.4424448
A Simplex Method for Function Minimization, The Computer Journal, vol.7, issue.4, pp.308-311, 1965. ,
DOI : 10.1093/comjnl/7.4.308
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
Developments of NEWUOA for minimization without derivatives, IMA Journal of Numerical Analysis, vol.28, issue.4, p.47, 2008. ,
DOI : 10.1093/imanum/drm047
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
Numerical Optimization of Computer Models, pp.1995-1997, 1981. ,
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
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
Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization, 2005. ,
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
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. ,
With the neural network, PSO is clearly the best algorithm SAiso (1 + 1) ,
CMA is the best performing algorithm for Conformant Planning SAiso (1 + 1) ,
86 CM A ? ES 100.00 100.00 100.00 100.00 100.00 64.29 100 ,
For Fuzzy control, SA-iso is the best algorithm SAiso (1 + 1) ,
DE outperforms everything for the specific policy SAiso (1 + 1), p.86 ,
With the neural network, PSO is clearly the best algorithm SAiso (1 + 1) ,
DE is the best performing algorithm for Conformant Planning SAiso (1 + 1), p.29 ,
For Fuzzy control, SA-iso is the best algorithm SAiso (1 + 1) ,