Multi 3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-objective Space by Means of Multiobjectivization
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
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
Original language | English |
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Title of host publication | Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings |
Editors | Hisao Ishibuchi, Qingfu Zhang, Ran Cheng, Ke Li, Hui Li, Handing Wang, Aimin Zhou |
Pages | 311-322 |
Number of pages | 12 |
Publication status | Published - 2021 |
Peer-reviewed | Yes |
External IDs
Scopus | 85107268207 |
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Mendeley | 52ec54d7-4853-3f1d-9cec-a254acd96cfc |
Keywords
ASJC Scopus subject areas
Keywords
- Local search, Multimodal optimization, Multiobjectivization