TY - JOUR
T1 - Necessary and sufficient conditions for achieving global optimal solutions in multiobjective quadratic fractional optimization problems
AU - de Oliveira, Washington Alves
AU - Rojas-Medar, Marko Antonio
AU - Beato-Moreno, Antonio
AU - Hernández-Jiménez, Maria Beatriz
N1 - Publisher Copyright:
© Springer Science+Business Media, LLC, part of Springer Nature 2019.
PY - 2019/6
Y1 - 2019/6
N2 - If x* is a local minimum solution, then there exists a ball of radius r > 0 such that f (x) ≥ f (x*) for all x ∈ B(x*, r). The purpose of the current study is to identify the suitable B(x*, r) of the local optimal solution x* for a particular multiobjective optimization problem. We provide a way to calculate the largest radius of the ball centered at local Pareto solution in which this solution is optimal. In this process, we present the necessary and sufficient conditions for achieving a global Pareto optimal solution. The results of this investigation might be useful to determine stopping criteria in the algorithms development.
AB - If x* is a local minimum solution, then there exists a ball of radius r > 0 such that f (x) ≥ f (x*) for all x ∈ B(x*, r). The purpose of the current study is to identify the suitable B(x*, r) of the local optimal solution x* for a particular multiobjective optimization problem. We provide a way to calculate the largest radius of the ball centered at local Pareto solution in which this solution is optimal. In this process, we present the necessary and sufficient conditions for achieving a global Pareto optimal solution. The results of this investigation might be useful to determine stopping criteria in the algorithms development.
KW - Multiobjective optimization
KW - Pareto optimality conditions
KW - Quadratic fractional optimization problems
UR - https://www.scopus.com/pages/publications/85064273176
U2 - 10.1007/s10898-019-00766-1
DO - 10.1007/s10898-019-00766-1
M3 - Article
AN - SCOPUS:85064273176
SN - 0925-5001
VL - 74
SP - 233
EP - 253
JO - Journal of Global Optimization
JF - Journal of Global Optimization
IS - 2
ER -