Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: MaoMao Liang ; Babooshka Shavazipour ; Bhupinder Saini ; Michael Emmerich and Kaisa Miettinen

Affiliation: University of Jyvaskyla, Faculty of Information Technology, P.O. Box 35 (Agora), 40014 University of Jyvaskyla, Finland

Keyword(s): Multiple Objective Optimization, Interactive Methods, Performance Indicators, Region of Interest, Evolutionary Algorithms, Preference-Based Hypervolume.

Abstract: Various interactive evolutionary multiobjective optimization methods have been proposed in the literature for problems with multiple, conflicting objective functions. In these methods, a decision maker, who is a domain expert, iteratively provides preference information to guide the solution process while gaining insight into the problem. To compare interactive evolutionary multiobjective optimization methods, a preference-based hypervolume indicator (PHI) has been proposed to quantify the performance of the methods. PHI was the first indicator designed based on some desirable properties of indicators for interactive evolutionary multiobjective optimization methods. However, it has some shortcomings, such as excluding some potentially interesting solutions and being limited to consider a reference point as a type of preference information. In this paper, a modified indicator called PHI+ is proposed to address the mentioned drawbacks. PHI+ modifies the region of interest in PHI. While PHI is directed at methods where a decision maker provides preference information in the form of a reference point, PHI+ is applicable for methods that utilize desirable ranges of objective function values as preference information. Therefore, PHI+ is the first indicator that can handle preference information provided as desirable ranges when evaluating interactive methods. Experimental results show that PHI+ can also better distinguish differences in the performance of interactive evolutionary multiobjective optimization methods. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 13.58.181.185

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Liang, M., Shavazipour, B., Saini, B., Emmerich, M. and Miettinen, K. (2024). A Modified Preference-Based Hypervolume Indicator for Interactive Evolutionary Multiobjective Optimization Methods. In Proceedings of the 16th International Joint Conference on Computational Intelligence - ECTA; ISBN 978-989-758-721-4; ISSN 2184-3236, SciTePress, pages 214-221. DOI: 10.5220/0012934600003837

@conference{ecta24,
author={MaoMao Liang and Babooshka Shavazipour and Bhupinder Saini and Michael Emmerich and Kaisa Miettinen},
title={A Modified Preference-Based Hypervolume Indicator for Interactive Evolutionary Multiobjective Optimization Methods},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - ECTA},
year={2024},
pages={214-221},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012934600003837},
isbn={978-989-758-721-4},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - ECTA
TI - A Modified Preference-Based Hypervolume Indicator for Interactive Evolutionary Multiobjective Optimization Methods
SN - 978-989-758-721-4
IS - 2184-3236
AU - Liang, M.
AU - Shavazipour, B.
AU - Saini, B.
AU - Emmerich, M.
AU - Miettinen, K.
PY - 2024
SP - 214
EP - 221
DO - 10.5220/0012934600003837
PB - SciTePress