A Modified Preference-Based Hypervolume Indicator for Interactive Evolutionary Multiobjective Optimization Methods
Year of publication
2024
Authors
Liang, MaoMao; Shavazipour, Babooshka; Saini, Bhupinder; Emmerich, Michael; Miettinen, Kaisa
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.
Show moreOrganizations and authors
Publication type
Publication format
Article
Parent publication type
Conference
Article type
Other article
Audience
ScientificPeer-reviewed
Peer-ReviewedMINEDU's publication type classification code
A4 Article in conference proceedingsPublication channel information
Parent publication name
IJCCI 2024 : Proceedings of the 16th International Joint Conference on Computational Intelligence
Parent publication editors
Marcelloni, Francesco; Madani, Kurosh; van Stein, Niki; Filipe, Joaquim
Pages
214-221
ISSN
ISBN
Publication forum
Publication forum level
0
Open access
Open access in the publisher’s service
No
Self-archived
Yes
Other information
Fields of science
Computer and information sciences
Keywords
[object Object],[object Object]
Publication country
Portugal
Internationality of the publisher
International
Language
English
International co-publication
No
Co-publication with a company
No
DOI
10.5220/0012934600003837
The publication is included in the Ministry of Education and Culture’s Publication data collection
Yes