DocumentCode :
3600860
Title :
An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition
Author :
Ke Li ; Deb, Kalyanmoy ; Qingfu Zhang ; Sam Kwong
Author_Institution :
Dept. of Comput. Sci., City Univ. of Hong Kong, Hong Kong, China
Volume :
19
Issue :
5
fYear :
2015
Firstpage :
694
Lastpage :
716
Abstract :
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.
Keywords :
Pareto optimisation; constrained optimization problems; decomposition-based approach; dominance-based approach; evolutionary many-objective optimization algorithm; evolutionary multiobjective optimization; evolutionary process convergence; evolutionary process diversity; unconstrained benchmark problems; unified paradigm; Convergence; Educational institutions; Estimation; Optimization; Sociology; Statistics; Vectors; Constrained optimization; Many-objective optimization; Pareto optimality; constrained optimization; decomposition; evolutionary computation; many-objective optimization; steady state; steady-state;
fLanguage :
English
Journal_Title :
Evolutionary Computation, IEEE Transactions on
Publisher :
ieee
ISSN :
1089-778X
Type :
jour
DOI :
10.1109/TEVC.2014.2373386
Filename :
6964796
Link To Document :
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