A1 Journal article (refereed)
A Visualizable Test Problem Generator for Many-Objective Optimization (2022)

Fieldsend, J. E., Chugh, T., Allmendinger, R., & Miettinen, K. (2022). A Visualizable Test Problem Generator for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 26(1), 1-11. https://doi.org/10.1109/TEVC.2021.3084119

JYU authors or editors

Publication details

All authors or editors: Fieldsend, Jonathan E.; Chugh, Tinkle; Allmendinger, Richard; Miettinen, Kaisa

Journal or series: IEEE Transactions on Evolutionary Computation

ISSN: 1089-778X

eISSN: 1941-0026

Publication year: 2022

Volume: 26

Issue number: 1

Pages range: 1-11

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/TEVC.2021.3084119

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/76011

Abstract

Visualizing the search behavior of a series of points or populations in their native domain is critical in understanding biases and attractors in an optimization process. Distancebased many-objective optimization test problems have been developed to facilitate visualization of search behavior in a two-dimensional design space with arbitrarily many objective functions. Previous works have proposed a few commonly seen problem characteristics into this problem framework, such as the definition of disconnected Pareto sets and dominance resistant regions of the design space. The authors’ previous work has advanced this research further by providing a problem generator to automatically create user-defined problem instances featuring any combination of these problem features as well as newly introduced ones, such as landscape discontinuities, varying objective ranges, and neutrality. This work makes a number of additional contributions including the proposal of an enhanced, open-source feature-rich problem generator that can create user-defined problem instances exhibiting a range of problem features – some of which are newly introduced here or form extensions of existing features. A comprehensive validation of the problem generator is also provided using popular multiobjective optimization algorithms, and some problem generator settings to create instances exhibiting different challenges for an optimizer are identified.

Keywords: optimisation; multi-objective optimisation; visualisation; benchmarking; problem solving; open source code

Free keywords: multi-objective test problems; evolutionary optimization; benchmarking; test suite; visualization

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

Preliminary JUFO rating: 3