Injection of extreme points in evolutionary multiobjective optimization algorithms

Abstract

This paper investigates a curious case of informed initialization technique to solve difficult multi-objective optimization (MOP) problems. The initial population was injected with non-exact (i.e. approximated) nadir objective vectors, which are the boundary solutions of a Pareto optimal front (PF). The algorithm then successively improves those boundary solutions and utilizes them to generate nondominated solutions targeted to the vicinity of the PF along the way. The proposed technique was ported to a standard Evolutionary Multiobjective Optimization (EMO) algorithm and tested on a wide variety of benchmark MOP problems. The experimental results suggest that the proposed approach is very helpful in achieving extremely fast convergence, especially if an experimenter’s goal is to find a set of well distributed trade-off solutions within a fix-budgeted solution evaluations (SEs). The proposed approach also ensures a more focused exploration of the underlying search space.