M. Aghabeig and A. Jaszkiewicz, Experimental analysis of design elements of scalarizing function-based multiobjective evolutionary algorithms, Soft Computing, vol.23, issue.21, pp.10769-10780, 2019.

N. Beume, B. Naujoks, and M. Emmerich, SMS-EMOA: Multiobjective selection based on dominated hypervolume, Eur. J. Oper. Res, vol.181, issue.3, pp.1653-1669, 2007.

X. Cai, Y. Li, Z. Fan, and Q. Zhang, An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization, IEEE Trans. Evolut. Comput, vol.19, issue.4, pp.508-523, 2015.

T. Chiang and Y. Lai, MOEA/D-AMS: Improving MOEA/D by an adaptive mating selection mechanism, CEC 2011, pp.1473-1480, 2011.

D. Corus and P. S. Oliveto, Standard Steady State Genetic Algorithms Can Hillclimb Faster Than Mutation-Only Evolutionary Algorithms, IEEE Transactions on Evolutionary Computation, vol.22, issue.5, pp.720-732, 2018.

M. ?repin?ek, S. H. Liu, and M. Mernik, Exploration and exploitation in evolutionary algorithms: A survey, ACM Computing Surveys, vol.45, issue.3, pp.1-33, 2013.

K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, 2001.

T. Glasmachers, B. Naujoks, and G. Rudolph, Start small, grow big? Saving multi-objective function evaluations, Parallel Problem Solving from Nature (PPSN XIII), vol.8672, pp.579-588, 2014.

H. Ishibuchi, R. Imada, N. Masuyama, and Y. Nojima, Two-layered weight vector specification in decomposition-based multi-objective algorithms for many-objective optimization problems, pp.2434-2441, 2019.

Y. Lavinas, C. Aranha, and M. Ladeira, Improving resource allocation in MOEA/D with decision-space diversity metrics, Theory and Practice of Natural Computing (TPNC 2019), pp.134-146, 2019.

H. Li and Q. Zhang, Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II, IEEE Trans. Evol. Comput, vol.13, issue.2, pp.284-302, 2009.

K. Li, Q. Zhang, S. Kwong, M. Li, and R. Wang, Stable Matching-Based Selection in Evolutionary Multiobjective Optimization, IEEE TEC, vol.18, issue.6, pp.909-923, 2014.

G. Marquet, B. Derbel, A. Liefooghe, and E. G. Talbi, Shake them all! Rethinking selection and replacement in MOEA/D. In: Parallel Problem Solving from Nature (PPSN XIII), pp.641-651, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00987800

M. Schumer and K. Steiglitz, Adaptive step size random search, IEEE Trans. Autom. Control, vol.13, issue.3, pp.270-276, 1968.

R. Tanabe and H. Ishibuchi, An analysis of control parameters of MOEA/D under two different optimization scenarios, Appl. Soft Comput, vol.70, pp.22-40, 2018.

A. Trivedi, D. Srinivasan, K. Sanyal, and A. Ghosh, A survey of multiobjective evolutionary algorithms based on decomposition, IEEE TEC, vol.21, issue.3, pp.440-462, 2017.

S. Verel, A. Liefooghe, L. Jourdan, and C. Dhaenens, On the structure of multiobjective combinatorial search space: MNK-Landscapes with correlated objectives, Eur. J. Oper. Res, vol.227, issue.2, pp.331-342, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00760097

P. Wang, W. Zhu, H. Liu, B. Liao, L. Cai et al., A new resource allocation strategy based on the relationship between subproblems for MOEA/D, Information Sciences, vol.501, pp.337-362, 2019.

Z. Wang, Q. Zhang, A. Zhou, M. Gong, and L. Jiao, Adaptive replacement strategies for MOEA/D, IEEE Trans. Cybern, vol.46, issue.2, pp.474-486, 2016.

C. Witt, Population size versus runtime of a simple evolutionary algorithm. Theoretical, Computer Science, vol.403, issue.1, pp.104-120, 2008.

S. Zapotecas-martínez, H. Aguirre, K. Tanaka, and C. Coello, On the lowdiscrepancy sequences and their use in MOEA/D for high-dimensional objective spaces, Congress on Evol. Computation (CEC 2015), pp.2835-2842, 2015.

Q. Zhang and H. Li, MOEA/D: A multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput, vol.11, issue.6, pp.712-731, 2007.

A. Zhou and Q. Zhang, Are all the subproblems equally important? Resource allocation in decomposition-based multiobjective evolutionary algorithms, IEEE Trans. Evol. Comput, vol.20, issue.1, pp.52-64, 2016.

E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. Grunert-da-fonseca, Performance assessment of multiobjective optimizers: An analysis and review, IEEE Trans. Evol. Comput, vol.7, issue.2, pp.117-132, 2003.