MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes

Schäpermeier L.; Grimme C.; Kerschke P.


Zusammenfassung
Recent advances in the visualization of continuous multimodal multi-objective optimization (MMMOO) landscapes brought a new perspective to their search dynamics. Locally eficient (LE) sets, often considered as traps for local search, are rarely isolated in the decision space. Rather, intersections by superposing attraction basins lead to further solution sets that at least partially contain better solutions. The Multi-Objective Gradient Sliding Algorithm (MOGSA) is an algorithmic concept developed to exploit these superpositions. While it has promising performance on many MMMOO problems with linear LE sets, closer analysis of MOGSA revealed that it does not sufficiently generalize to a wider set of test problems. Based on a detailed analysis of shortcomings of MOGSA, we propose a new algorithm, the Multi-Objective Landscape Explorer (MOLE). It is able to efficiently model and exploit LE sets in MMMOO problems. An implementation of MOLE is presented for the bi-objective case, and the practicality of the approach is shown in a benchmarking experiment on the Bi-Objective BBOB testbed.

Schlüsselwörter
continuous optimization; local search; heuristics; multimodality; multi-objective optimization



Publikationstyp
Forschungsartikel in Sammelband (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2022

Konferenz
2022 Genetic and Evolutionary Computation Conference, GECCO 2022

Konferenzort
Boston, Massachusetts

Buchtitel
Proceedings of the Genetic and Evolutionary Computation Conference

Herausgeber
Fieldsend, J. E.

Erste Seite
592

Letzte Seite
600

Verlag
Association for Computing Machinery, Inc

Ort
New York, NY, USA

Sprache
Englisch

ISBN
9781450392372

DOI

Gesamter Text