# Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems

Source: [Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems](<https://www.sota2.com/research/paper/68fa67ca1e209131cea1868f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:22:05.587Z

Authors: Vera Steinhoff, Pascal Kerschke, Christian Grimme

Published: 2020-06-25T14:04:37.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2006.14423>)

[PDF](<https://arxiv.org/pdf/2006.14423.pdf>)