A Norm Minimization-Based Convex Vector Optimization Algorithm

We propose an algorithm to generate inner and outer polyhedral approximations the upper image of a bounded convex vector optimization problem. It is approximation based on solving norm-minimizing scalarizations. Unlike Pascoletti–Serafini scalarization used in literature for similar purposes, it does not involve direction parameter. Therefore, free direction-biasedness. also modification by introducing suitable compact subset image, which helps proving first time finiteness optimization. The computational performance algorithms illustrated using some benchmark test problems, shows promising results comparison that scalarization.