Plotting Impossible? Surveying Visualization Methods for Continuous Multi-Objective Benchmark Problems

Schäpermeier, Lennart; Grimme, Christian; Kerschke, Pascal

Research article (journal) | Peer reviewed

Abstract

Traditionally, visualizing benchmark problems is an integral task in the domain of evolutionary algorithms development. Researchers get inspired for new search heuristics by challenges observed in functional landscapes. Moreover, landscape characteristics, features, and even terminology to describe them are derived from visualizations. And most importantly, benchmark designers need visualizations for identifying diverse problems that potentially challenge different aspects of optimization algorithms. As easy as it is to visualize single-objective problems, until recently there were hardly any approaches for gaining similar insights for multi-objective problems. Also, there have been no seamlessly accessible tools to support such visualizations. This paper presents a comprehensive overview of the available visualization techniques from literature, including two interactive techniques to visualize three-dimensional problems, as well as two novel techniques which are suitable to scale some visualization properties to even higher-dimensional spaces. All presented techniques are integrated into a single tool, the moPLOT-dashboard, which enables users to perform landscape analyses in an interactive manner. Finally, the value of the tool and the visualizations is demonstrated in a series of usage scenarios on well-known benchmark problems.

Details about the publication

JournalIEEE Transactions on Evolutionary Computation
Volume26
Issue6
Page range1306-1320
StatusPublished
Release year2022
Language in which the publication is writtenEnglish
DOI10.1109/TEVC.2022.3214894
KeywordsMulti-Objective Optimization; Visualization; Multimodal Optimization; Benchmarks; Theory; Algorithms

Authors from the University of Münster

Grimme, Christian
Data Science: Statistics and Optimization (Statistik)
Research Group Computational Social Science and Systems Analysis (CSSSA)
Schäpermeier, Lennart Merlin
Research Group Computational Social Science and Systems Analysis (CSSSA)