R. Jin, X. Du, C. , and W. , The use of metamodeling techniques for optimization under uncertainty, Structural and Multidisciplinary Optimization, vol.25, pp.99-116, 2003.

N. V. Queipo, R. T. Haftka, W. Shyy, and T. Goel, Surrogate-based analysis and optimization, Progress in Aerospace Sciences, vol.41, pp.1-28, 2005.

J. Sacks, W. J. Welch, J. , M. T. , W. et al., Design and analysis of computer experiments, Statistical Science, vol.4, issue.4, pp.409-435, 1989.

T. W. Simpson, J. D. Peplinski, P. N. Koch, A. , and J. K. , Metamodels for computer based engineering design: survey and recommendations, Engineering with Computers, vol.17, issue.2, pp.129-150, 2001.

D. Zhao and D. Xue, A multi-surrogate approximation method for metamodeling, Engineering with Computers, vol.27, pp.139-153, 2005.

G. G. Wang and T. W. Simpson, Fuzzy Clustering Based Hierarchical Metamodeling for Design Space Reduction and Optimization, Engineering Optimization, vol.36, issue.3, pp.313-335, 2004.

I. Voutchkov and A. J. Keane, Multiobjective optimization using surrogates, 7th International Conference on Adaptive Computing in Design and Manufacture, pp.167-175, 2006.

A. Samad, K. Kim, T. Goel, R. T. Haftka, and W. Shyy, Multiple surrogate modeling for axial compressor blade shape optimization, Journal of Propulsion and Power, vol.25, issue.2, pp.302-310, 2008.

F. A. Viana and R. T. Haftka, Using multiple surrogates for metamodeling, 7th ASMO-UK/ISSMO International Conference on Engineering Design Optimization, 2008.

B. Glaz, T. Goel, L. Liu, P. Friedmann, and R. T. Haftka, Multiple-surrogate approach to helicopter rotor blade vibration reduction, AIAA Journal, vol.47, issue.1, pp.271-282, 2009.

Y. Shoham and K. Leyton-brown, Multiagent Systems: Algorithmic, Game-Theoric, and Logical Foundations, 2009.

P. J. Modi, W. Shen, M. Tambe, Y. , and M. , ADOPT: Asynchronous Distributed Constraint Optimization with Quality Guarantees, Artificial Intelligence, vol.161, issue.2, pp.149-180, 2005.

J. Holmgren, J. A. Persson, D. , and P. , Agent Based Decomposition of Optimization Problems, First International Workshop on Optimization in Multi-Agent Systems, 2008.

D. Beasley, D. R. Bull, M. , and R. R. , A sequential niche technique for multimodal function optimization, Evolutionary computation, vol.1, issue.2, pp.101-125, 1993.

C. Hocaoglu and A. C. Sanderson, Multimodal function optimization using minimal representation size clustering and its application to planning multipaths, Evolutionary Computation, vol.5, issue.1, pp.81-104, 1997.

R. Brits, A. P. Engelbrecht, and F. Van-den-bergh, Locating multiple optima using particle swarm optimization, Applied Mathematics and Computation, vol.189, issue.2, pp.1859-1883, 2007.

K. E. Parsopoulos and M. N. Vrahatis, Artificial Neural Networks and Genetic Algorithms, chap. Modification ofthe Particle Swarm Optimizer for Locating All the Global Minima, pp.324-327, 2001.

X. Li, Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization, Genetic and Evolutionary Computation (GECCO 2004), vol.3102, pp.105-116, 2004.

S. Nagendra, D. Jestin, Z. Gurdal, R. T. Haftka, and L. T. Watson, Improved genetic algorithm for the design of stiffened composite panels, Computers & Structures, vol.58, issue.3, pp.543-555, 1996.

J. P. Li, M. E. Balazas, G. Parks, and P. J. Clarkson, A Species Conserving Genetic Algorithm for Multimodal Function Optimization, Evolutionary Computation, vol.10, issue.3, pp.207-234, 2002.

A. Torn and A. Zilinskas, Global Optimization, Lecture Notes in Computer Science, vol.350, 1989.

J. Kleijen, Design and analysis of simulation experiments, 2008.

F. Aurenhammer, Voronoi diagrams: a survey of a fundamental geometric data structure, ACM Computing Surveys (CSUR), vol.23, issue.3, pp.345-405, 1991.

J. Hartigan and M. Wong, Algorithm AS 136: A K-Means Clustering Algorithm, Journal of the Royal Statistical Society. Series C (Applied Statistics), vol.28, issue.1, pp.100-108, 1979.

P. J. Rousseeuw, Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis, Computational and Applied Mathematics, vol.20, pp.53-65, 1987.

F. A. Viana, Multiple Surrogates for Prediction and Optimization, 27 MATLAB, version (R2009b), chap. fmincon, 2009.

D. R. Jones, M. Schonlau, W. , and W. J. , Efficient Global Optimization of Expensive Black-Box Functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492