An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition

Achieving balance between convergence and diversity is a key issue in evolutionary multiobjective optimization. Most existing methodologies, which have demonstrated their niche on various practical problems involving two and three objectives, face significant challenges in many-objective optimizatio...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on evolutionary computation Ročník 19; číslo 5; s. 694 - 716
Hlavní autori: Ke Li, Deb, Kalyanmoy, Qingfu Zhang, Sam Kwong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
constrained optimization
Convergence
decomposition
Educational institutions
Estimation
evolutionary computation
Many-objective optimization
Objectives
Optimization
Pareto optimality
Sociology
Statistics
steady-state
Vectors
steady state
Pareto optimality
evolutionary computation
many-objective optimization
decomposition
Constrained optimization
ISSN:1089-778X, 1941-0026
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Achieving balance between convergence and diversity is a key issue in evolutionary multiobjective optimization. Most existing methodologies, which have demonstrated their niche on various practical problems involving two and three objectives, face significant challenges in many-objective optimization. This paper suggests a unified paradigm, which combines dominance- and decomposition-based approaches, for many-objective optimization. Our major purpose is to exploit the merits of both dominance- and decomposition-based approaches to balance the convergence and diversity of the evolutionary process. The performance of our proposed method is validated and compared with four state-of-the-art algorithms on a number of unconstrained benchmark problems with up to 15 objectives. Empirical results fully demonstrate the superiority of our proposed method on all considered test instances. In addition, we extend this method to solve constrained problems having a large number of objectives. Compared to two other recently proposed constrained optimizers, our proposed method shows highly competitive performance on all the constrained optimization problems.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2014.2373386