H. E. Aguirre and K. Tanaka, Working principles, behavior, and performance of MOEAs on MNK-landscapes, European Journal of Operational Research, vol.181, issue.3, pp.1670-1690, 2007.
DOI : 10.1016/j.ejor.2006.08.004

N. Beume, B. Naujoks, and M. Emmerich, SMS-EMOA: Multiobjective selection based on dominated hypervolume, European Journal of Operational Research, vol.181, issue.3, pp.1653-1669, 2007.
DOI : 10.1016/j.ejor.2006.08.008

J. H. Chen and C. W. Kang, A force-driven evolutionary approach for multi-objective 3D differentiated sensor network deployment, International Journal of Ad Hoc and Ubiquitous Computing, vol.8, issue.1/2, pp.85-95, 2011.
DOI : 10.1504/IJAHUC.2011.041624

H. R. Hassanzadeh and M. Rouhani, A Multi-objective Gravitational Search Algorithm, 2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks, pp.7-12, 2010.
DOI : 10.1109/CICSyN.2010.32

E. J. Hughes, Multiple single objective pareto sampling, The 2003 Congress on Evolutionary Computation, 2003. CEC '03., pp.2678-2684, 2003.
DOI : 10.1109/CEC.2003.1299427

E. J. Hughes, Many Objective Optimisation: Direct Objective Boundary Identification, Parallel Problem Solving from Nature, pp.733-742, 2008.
DOI : 10.1007/978-3-540-87700-4_73

S. A. Kauffman, The Origins of Order, 1993.

J. A. Khan and S. M. Sait, Fast fuzzy force-directed simulated evolution metaheuristic for multiobjective VLSI cell placement, The Arabian J. for Sc. and Eng, vol.32, issue.2B, pp.264-281, 2007.

M. López-ibáñez, L. Paquete, and T. Stützle, Exploratory analysis of stochastic local search algorithms in biobjective optimization In: Experimental Methods for the Analysis of Optimization Algorithms, chap, pp.209-222, 2010.

K. Miettinen, Nonlinear Multiobjective Optimization, 1999.
DOI : 10.1007/978-1-4615-5563-6

H. Nobahari, M. Nikusokhan, and P. Siarry, Non-dominated sorting gravitational search algorithm, Int. Conf. on swarm intelligence, 2011.

E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, GSA: A Gravitational Search Algorithm, Information Sciences, vol.179, issue.13, pp.2232-2248, 2009.
DOI : 10.1016/j.ins.2009.03.004

S. Verel, A. Liefooghe, L. Jourdan, and C. Dhaenens, Analyzing the Effect of Objective Correlation on the Efficient Set of MNK-Landscapes, In: Learning and Intelligent OptimizatioN LNCS, vol.63, issue.3, pp.116-130, 2011.
DOI : 10.1007/BF00202749

URL : https://hal.archives-ouvertes.fr/hal-00550349

T. Wagner, N. Beume, and B. Naujoks, Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization, Conference on Evolutionary Multi-Criterion Optimization, pp.742-756, 2007.
DOI : 10.1007/978-3-540-70928-2_56

Y. Wang and J. C. Zeng, A constraint multi-objective artificial physics optimization algorithm, 2010 Second International Conference on Computational Intelligence and Natural Computing, pp.107-112, 2010.
DOI : 10.1109/CINC.2010.5643882

Y. Wang, J. C. Zeng, Z. H. Cui, and X. J. He, A novel constraint multi-objective artificial physics optimisation algorithm and its convergence, International Journal of Innovative Computing and Applications, vol.3, issue.2, pp.61-70, 2011.
DOI : 10.1504/IJICA.2011.039589

Q. Zhang and H. Li, MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition, IEEE Transactions on Evolutionary Computation, vol.11, issue.6, pp.712-731, 2007.
DOI : 10.1109/TEVC.2007.892759

E. Zitzler, L. Thiele, M. Laumanns, C. M. Foneseca, and V. Grunert-da-fonseca, Performance assessment of multiobjective optimizers: an analysis and review, IEEE Transactions on Evolutionary Computation, vol.7, issue.2, pp.117-132, 2003.
DOI : 10.1109/TEVC.2003.810758