Bradford, E., Schweidtmann, A. M., & Lapkin, A. (2018). Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm.
Journal of global optimization, 71(2), 407-438.
https://doi.org/10.1007/s10898-018-0609-2
Chen, L., Xin, B. & Chen, J. (2021). Interactive multiobjective evolutionary algorithm based on decomposition and compression.
Science China information sciences, 64, 201-202.
https://doi.org/10.1007/s11432-020-3092-y
Das, I., & Dennis, J. E. (1998). Normal-bounday intersection: A new method for generating Pareto optimal points in multicriteria optimization problems
. SIAM journal on optimization, 8(3), 631-657.
DOI:
10.1137/S1052623496307510
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II.
IEEE transactions on evolutionary computation, 6(2), 182-197.
DOI:
10.1109/4235.996017
Farina, M., & Amato, P. (2004). A fuzzy definition of optimality for many-criteria optimization problems.
IEEE transactions on systems, man, and cybernetics - part A: systems and humans, 34(3), 315-326.
DOI:
10.1109/TSMCA.2004.824873
Gong, M., Liu, F., Zhang, W., Jiao, L., & Zhang, Q. (2011). Interactive MOEA/D for multi-objective decision making.
Proceedings of the 13th annual conference on genetic and evolutionary computation (pp. 721-728).
https://doi.org/10.1145/2001576.2001675
He, Z., Yen, G. G., & Zhang, J. (2013). Fuzzy-based Pareto optimality for many-objective evolutionary algorithms.
IEEE transactions on evolutionary computation, 18(2), 269-285.
DOI:
10.1109/TEVC.2013.2258025
Jena, S. (2013).
Multi-objective optimization of the design parameters of shell and tube type heat exchanger based on economic and size consideration (Ph.D Thesis, Bachelor of Technology). Retrieved from
http://ethesis.nitrkl.ac.in/5390/
Khan, W., & Zhang, Q. (2010). MOEA/D-DRA with two crossover operators.
2010 UK workshop on computational intelligence (UKCI) (pp. 1-6). IEEE.
DOI:
10.1109/UKCI.2010.5625578
Leung, M. F., & Ng, S. C. (2020). A hybrid algorithm based on MOEA/D and local search for multiobjective optimization. 2020 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE. Glasgow, UK Doi: 10.1109/CEC48606.2020.9185741.
Li, H., & Zhang, Q. (2008). Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II.
IEEE transactions on evolutionary computation,
13(2), 284-302.
DOI:
10.1109/TEVC.2008.925798
Lin, W., Lin, Q., Zhu, Z., Li, J., Chen, J., & Ming, Z. (2019). Evolutionary search with multiple utopian reference points in decomposition-based multiobjective optimization.
Complexity, 2019. https://doi.org/10.1155/2019/7436712
Lu, H., & Yen, G. G. (2003). Rank-density-based multiobjective genetic algorithm and benchmark test function study.
IEEE transactions on evolutionary computation, 7(4), 325–343.
DOI:
10.1109/TEVC.2003.812220
Mashwani, W. K. (2011). Integration of NSGA-II and MOEA/D in multimethod search approach: algorithms.
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation (pp. 75-76).
https://doi.org/10.1145/2001858.2001903
Messac, A., Ismail-Yahaya, A., & Mattson, C.A. (2003). The normalized normal constraint method for generating the Pareto frontier.
Structural and multidisciplinary optimization,
25(2), 86-98.
https://doi.org/10.1007/s00158-002-0276-1
Miettinen, K. (2001). Some methods for nonlinear multi-objective optimization.
International conference on evolutionary multi-criterion optimization (pp. 1-20). Springer, Berlin, Heidelberg.
https://doi.org/10.1