References
-
Deb, K. and Jain, H. (2014). 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.
-
Deb, K.; Pratap, A.; Agarwal, S. and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation 6, 182–197.
-
Emmerich, M.; Beume, N. and Naujoks, B. (2005). An EMO Algorithm Using the Hypervolume Measure as Selection Criterion. In: Lecture Notes in Computer Science, editors, 62–76. Springer Berlin Heidelberg.
-
Karaboga, D. and Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization 39, 459–471.
-
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks, 1942–1948.
-
Li, H. and Zhang, Q. (2008). Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE transactions on evolutionary computation 13, 284–302.
-
Mejía-de-Dios, J.-A. and Mezura-Montes, E. (2019). A New Evolutionary Optimization Method Based on Center of Mass. In: Decision Science in Action: Theory and Applications of Modern Decision Analytic Optimisation, editors, Kusum Deep, Madhu Jain and Said Salhi, 65–74. Springer Singapore, Singapore.
-
Mirjalili, S. and Gandomi, A. H. (2017). Chaotic gravitational constants for the gravitational search algorithm. Applied soft computing 53, 407–419.
-
Satman, M. H.; Yıldırım, B. F. and Kuruca, E. (2021). JMcDM: A Julia package for multiple-criteria decision-making tools. Journal of Open Source Software 6, 3430.
-
Satman, M. H. and Akadal, E. (2020). Machine Coded Compact Genetic Algorithms for Real Parameter Optimization Problems. Alphanumeric Journal 8, 43–58.
-
Takahama, T. and Sakai, S. (2006). 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.
-
Tian, Y.; Zhang, T.; Xiao, J.; Zhang, X. and Jin, Y. (2021). A Coevolutionary Framework for Constrained Multiobjective Optimization Problems. IEEE Transactions on Evolutionary Computation 25, 102-116.
-
Van L., P. J. and Aarts, E. (1987). Simulated annealing. In: Simulated annealing: Theory and applications, editors, 7–15. Springer.
-
Zitzler, E.; Thiele, L.; Laumanns, M.; Fonseca, C. and da Fonseca, V. (2003). Performance assessment of multiobjective optimizers: an analysis and review. IEEE Transactions on Evolutionary Computation 7, 117-132.
-
Zitzler, E.; Laumanns, M. and Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-report 103.
-
de-la-Cruz-Martínez, S.; Mejía-de-Dios, J. and E., M.-M. (2022). 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.