Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent

Vera Steinhoff, Pascal Kerschke, Pelin Aspar, Heike Trautmann, Christian Grimme

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress. This does not only challenge local strategies that can get stuck. It also hinders meta-heuristics like evolutionary algorithms in convergence to the global optimum. In this paper we present a new concept of gradient descent, which is able to escape local traps. It relies on multiobjectivization of the original problem and applies the recently proposed and here slightly modified multi-objective local search mechanism MOGSA. We use a sophisticated visualization technique for multi-objective problems to prove the working principle of our idea. As such, this work highlights the transfer of new insights from the multi-objective to the single-objective domain and provides first visual evidence that multiobjectivization can link single-objective local optima in multimodal landscapes.
Original languageEnglish
Title of host publicationIEEE Symposium Series on Computational Intelligence (SSCI)
Pages2445-2452
Number of pages8
DOIs
Publication statusPublished - 5 Jan 2021
Externally publishedYes
EventIEEE Symposium Series on Computational Intelligence, SSCI 2020 - Virtual Event
Duration: 1 Dec 20204 Dec 2020
http://www.ieeessci2020.org/

Conference

ConferenceIEEE Symposium Series on Computational Intelligence, SSCI 2020
Abbreviated titleSSCI 2020
CityVirtual Event
Period1/12/204/12/20
Internet address

Fingerprint Dive into the research topics of 'Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent'. Together they form a unique fingerprint.

Cite this