MOLE: Digging Tunnels Through Multimodal Multi-objective Landscapes

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Abstract

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.

Details

OriginalspracheEnglisch
TitelGECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
Seiten592-600
Seitenumfang9
PublikationsstatusVeröffentlicht - 8 Juli 2022
Peer-Review-StatusJa

Externe IDs

Scopus 85135240841
ORCID /0000-0003-3929-7465/work/142241487
Mendeley 522b6679-8ebe-364e-b9c9-9bec22c37103

Schlagworte

Schlagwörter

  • continuous optimization, heuristics, local search, multi-objective optimization, multimodality