Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems
Abstract
When dealing with continuous single-objective problems, multimodality poses one of the biggest difficulties for global optimization. Local optima are often preventing algorithms from making progress and thus pose a severe threat. In this paper we analyze how single-objective optimization can benefit from multiobjectivization by considering an additional objective. With the use of a sophisticated visualization technique based on the multi-objective gradients, the properties of the arising multi-objective landscapes are illustrated and examined. We will empirically show that the multi-objective optimizer MOGSA is able to exploit these properties to overcome local traps. The performance of MOGSA is assessed on a testbed of several functions provided by the COCO platform. The results are compared to the local optimizer Nelder-Mead.
Keywords
Cite
@article{arxiv.2006.14423,
title = {Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems},
author = {Vera Steinhoff and Pascal Kerschke and Christian Grimme},
journal= {arXiv preprint arXiv:2006.14423},
year = {2020}
}