Adaptive differential evolution algorithm for multiobjective optimization problems

In this paper, a new adaptive differential evolution algorithm (ADEA) is proposed for multiobjective optimization problems. In ADEA, the variable parameter F based on the number of the current Pareto-front and the diversity of the current solutions is given for adjusting search size in every generat...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied mathematics and computation Jg. 201; H. 1; S. 431 - 440
Hauptverfasser: Qian, Weiyi, li, Ajun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY Elsevier Inc 15.07.2008
Elsevier
Schlagworte:
Adaptive
Differential evolution algorithm
Exact sciences and technology
Global analysis, analysis on manifolds
Mathematical analysis
Mathematics
Multiobjective optimization problems
Numerical analysis
Numerical analysis. Scientific computation
Sciences and techniques of general use
Select operator
Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds
Multiobjective optimization problems
Select operator
Differential evolution algorithm
Adaptive
Numerical analysis
Adaptive algorithm
Applied mathematics
Optimization method
Implementation
Sorting
ISSN:0096-3003, 1873-5649
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper, a new adaptive differential evolution algorithm (ADEA) is proposed for multiobjective optimization problems. In ADEA, the variable parameter F based on the number of the current Pareto-front and the diversity of the current solutions is given for adjusting search size in every generation to find Pareto solutions in mutation operator, and the select operator combines the advantages of DE with the mechanisms of Pareto-based ranking and crowding distance sorting. ADEA is implemented on five classical multiobjective problems, the results illustrate that ADEA efficiently achieves two goals of multiobjective optimization problems: find the solutions converge to the true Pareto-front and uniform spread along the front.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2007.12.052