References

[1]
[2]
[3]
[4]
D. Karaboga and B. Basturk. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization 39, 459–471 (2007).
[5]
H. Li and Q. Zhang. Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE transactions on evolutionary computation 13, 284–302 (2008).
[6]
S. Mirjalili and A. H. Gandomi. Chaotic gravitational constants for the gravitational search algorithm. Applied soft computing 53, 407–419 (2017).
[7]
P. J. Van L. and E. Aarts. Simulated annealing. In Simulated annealing: Theory and applications, editors, 7–15. Springer (1987).
[8]
S. Mirjalili and A. Lewis. The whale optimization algorithm. Advances in engineering software 95, 51–67 (2016).
[9]
K. Deb, A. Pratap, S. Agarwal and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation 6, 182–197 (2002).
[10]
K. Deb and H. Jain. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints. IEEE Transactions on Evolutionary Computation 18, 577-601 (2014).
[11]
M. Emmerich, N. Beume and B. Naujoks. An EMO Algorithm Using the Hypervolume Measure as Selection Criterion. In Lecture Notes in Computer Science, editors, 62–76. Springer Berlin Heidelberg (2005).
[12]
E. Zitzler, M. Laumanns and L. Thiele. SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-report 103 (2001).
[13]
M. H. Satman and E. Akadal. Machine Coded Compact Genetic Algorithms for Real Parameter Optimization Problems. Alphanumeric Journal 8, 43–58 (2020).
[14]
Y. Tian, T. Zhang, J. Xiao, X. Zhang and Y. Jin. A Coevolutionary Framework for Constrained Multiobjective Optimization Problems. IEEE Transactions on Evolutionary Computation 25, 102-116 (2021).
[15]
T. Takahama and S. Sakai. Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites. In 2006 IEEE International Conference on Evolutionary Computation, 1-8 (2006).
[16]
J. F. Gon{ç}alves and M. G. Resende. Biased random-key genetic algorithms for~combinatorial optimization. Journal of Heuristics 17, 487–525 (2010).
[17]
E. Zitzler, L. Thiele, M. Laumanns, C. Fonseca and V. da Fonseca. Performance assessment of multiobjective optimizers: an analysis and review. IEEE Transactions on Evolutionary Computation 7, 117-132 (2003).
[18]
M. H. Satman, B. F. Yıldırım and E. Kuruca. JMcDM: A Julia package for multiple-criteria decision-making tools. Journal of Open Source Software 6, 3430 (2021).
[19]
S. de-la-Cruz-Martínez, J. Mejía-de-Dios and M.-M. E.. Efficient Archiving Method for Handling Preferences in Constrained Multiobjective Evolutionary Optimization. Handbook on Decision Making: Trends and Challenges in Intelligent Decision Support Systems, 1–30, Springer-Verlag (2022).
[20]