Are All the Subproblems Equally Important? Resource Allocation in Decomposition-Based Multiobjective Evolutionary Algorithms

Decomposition-based multiobjective evolutionary algorithms (MOEAs) decompose a multiobjective optimization problem into a set of scalar objective subproblems and solve them in a collaborative way. A naïve way to distribute computational effort is to treat all the subproblems equally and assign the...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 20; H. 1; S. 52 - 64
Hauptverfasser: Zhou, Aimin, Zhang, Qingfu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Allocations
Computation
Decomposition
Evolutionary algorithms
Evolutionary computation
Iron
Linear programming
Mathematical analysis
multiobjective optimization
Optimization
Resource allocation
Resource management
Sociology
Statistics
Strategy
Vectors (mathematics)
multiobjective optimization
Decomposition
resource allocation
ISSN:1089-778X, 1941-0026
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Decomposition-based multiobjective evolutionary algorithms (MOEAs) decompose a multiobjective optimization problem into a set of scalar objective subproblems and solve them in a collaborative way. A naïve way to distribute computational effort is to treat all the subproblems equally and assign the same computational resource to each subproblem. This paper proposes a generalized resource allocation (GRA) strategy for decomposition-based MOEAs by using a probability of improvement vector. Each subproblem is chosen to invest according to this vector. An offline measurement and an online measurement of the subproblem hardness are used to maintain and update this vector. Utility functions are proposed and studied for implementing a reasonable and stable online resource allocation strategy. Extensive experimental studies on the proposed GRA strategy have been conducted.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2015.2424251