Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization

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

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

Abstract

In this work we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems by first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective space, and subsequently exploiting local structures and ridges of the resulting landscapes. Our study particularly focusses on the sensitivity of this multiobjectivization approach w.r.t. (i) the parametrization of the artificial second objective, as well as (ii) the position of the initial starting points in the search space.
As SOMOGSA is a modular framework for encapsulating local search, we integrate Gradient and Nelder-Mead local search (as optimizers in the respective module) and compare the performance of the resulting hybrid local search to their original single-objective counterparts. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Combined with more sophisticated local search and metaheuristics this may help in solving highly multimodal optimization problems in future.
Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization
Subtitle of host publication11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings
PublisherSpringer International Publishing AG
Pages311-322
Number of pages12
ISBN (Electronic)978-3-030-72062-9
ISBN (Print)978-3-030-72061-2
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021 - Hampton by Hilton Hotel, Shenzhen, China
Duration: 28 Mar 202131 Mar 2021
Conference number: 11
https://emo2021.org/

Publication series

NameLecture Notes in Computer Science
Volume12654

Conference

Conference11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021
Abbreviated titleEMO 2021
CountryChina
CityShenzhen
Period28/03/2131/03/21
Internet address

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