Multiobjective evolutionary algorithms based on target region preferences

Li L, Wang Y, Trautmann H, Jing N, Emmerich M


Zusammenfassung
Incorporating decision makers' preferences is of great significance in multiobjective optimization. Target region-based multiobjective evolutionary algorithms (TMOEAs), aiming at a well-distributed subset of Pareto optimal solutions within the user-provided region(s), are extensively investigated in this paper. An empirical comparison is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA. Experimental results show that T-SMS-EMOA has the best overall performance regarding the hypervolume indicator within the target region, while T-NSGA-II is the fastest algorithm. We also compare TMOEAs with other state-of-the-art preference-based approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages of TMOEAs. A case study in the mission planning of earth observation satellite is carried out to verify the capabilities of TMOEAs in the real-world application. Experimental results indicate that preferences can improve the searching ability of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by the decision maker.

Schlüsselwörter
Target region, Preferences, Multiobjective evolutionary algorithms (MOEA), Satellite mission planning



Publikationstyp
Aufsatz (Zeitschrift)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2018

Fachzeitschrift
Swarm and Evolutionary Computation

Band
40

Erste Seite
196

Letzte Seite
215

ISSN
2210-6502

DOI

Gesamter Text