Robust generalized Pascoletti-Serafini scalarization method for uncertain multi-objective optimization problems
Fixed Point Methods and Optimization, Volume 2, Issue 3, December 2025, Pages 198–209
PEIYAO WANG
School of Mathematics and Statistics, Henan University of Technology, Zhengzhou, 450001, P.R.China
HUI GUO
School of Mathematics and Statistics, Henan University of Technology, Zhengzhou, 450001, P.R.China
Abstract
In the research of multi-objective optimization problems, various scalarization methods have always played an important role. Based on the generalized Pascoletti-Serafini scalarization method in multi-objective optimization and combined with a generalized form of the generalized Tchebycheff norm, this paper improves and generalizes the generalized Tchebycheff scalarization method, and then proposes a robust generalized Pascoletti-Serafini scalarization method suitable for uncertain multi-objective optimization problems. On this basis, under certain parameter conditions, the scalarization properties of robust (weakly, strictly, properly) efficient solutions for uncertain multi-objective optimization problems are established.
Cite this Article as
Peiyao Wang and Hui Guo, Robust generalized Pascoletti-Serafini scalarization method for uncertain multi-objective optimization problems, Fixed Point Methods and Optimization, 2(3), 198–209, 2025