New Solution Creation Operator in MOEA/D for Faster Convergence
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-14721-0_17
Reference18 articles.
1. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)
2. Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-objective Problems. Springer, New York (2007). https://doi.org/10.1007/978-0-387-36797-2
3. Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
4. Mittal, S., Saxena, D.K., Deb, K., Goodman, E.D.: A learning-based innovized progress operator for faster convergence in evolutionary multi-objective optimization. ACM Trans. Evol. Learn. Optim. 2(1), 1–29 (2021)
5. Lecture Notes in Computer Science;A Ghosh,2021
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