Maintaining Diversity in The Bounded Pareto-Set

Abstract

For more than two decades, stand-alone evolutionary multi-objective optimization (EMO) methods have been adequately demonstrated to find a set of trade-off solutions near Pareto-front for various multi-objective optimization problems. Despite a long-standing existence of classical generative single-objective based methods, a very few EMO studies have combined the two approaches for a better gain. In this paper, we investigate the effect of seeding the initial population of an EMO algorithm with extreme solutions obtained using a single-objective method. Our proposed approach is further aided with an opposition based offspring creation mechanism which strategically places new solutions on the current Pareto frontier by a simple, yet a novel arbitration policy that utilizes the relative distances from the extreme solutions in the current population members. We conduct an extensive simulation of our proposed approach on a wide variety of two and three-objective benchmark MOP test problems. Results are shown to be remarkably better than the original EMO approach in terms of hyper-volume metric. The results are interesting and should motivate EMO researchers to integrate single-objective focused optimization and an opposition-based concept with diversity-preserving EMO procedures for an overall better performance.