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

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

Research article in edited proceedings (conference) | Peer reviewed

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.

Details about the publication

PublisherIshibuchi, H. et al.
Book titleEvolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings
Page range311-322
Publishing companySpringer
Place of publicationHeidelberg, Berlin
StatusPublished
Release year2021
Language in which the publication is writtenEnglish
Conference11th International Conference on Evolutionary Multi-Criterion Optimization (EMO), Shenzhen, China, China
DOI10.1007/978-3-030-72062-9_25
Link to the full texthttps://link.springer.com/chapter/10.1007/978-3-030-72062-9_25
KeywordsMultiobjective Optimization, Multimodalit

Authors from the University of Münster

Aspar, Pelin
Data Science: Statistics and Optimization (Statistik)
Grimme, Christian
Data Science: Statistics and Optimization (Statistik)
Research Group Computational Social Science and Systems Analysis (CSSSA)
Kerschke, Pascal
Data Science: Statistics and Optimization (Statistik)
Steinhoff, Vera
Data Science: Statistics and Optimization (Statistik)
Trautmann, Heike
Data Science: Statistics and Optimization (Statistik)