Outer approximation algorithms for convex vector optimization problems

In this study, we present a general framework of outer approximation algorithms to solve convex vector optimization problems, in which the Pascoletti-Serafini (PS) scalarization is solved iteratively. This finds minimum ‘distance’ from reference point, usually taken as vertex current approximation, upper image through given direction. We propose efficient methods select parameters (the point and direction vector) PS analyse effects these on overall performance algorithm. Different existing selection rules literature, proposed do not require solving additional single-objective problems. Using some test conduct an extensive computational study where three different measures are set stopping criteria: error, runtime, cardinality solution set. observe that variants have satisfactory results, especially terms runtime compared literature.