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