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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Adaptive Differential Evolution for Multi-objective Optimization
verfasst von : Zai Wang, Zhenyu Yang, Ke Tang, Xin Yao
Erschienen in: Cutting-Edge Research Topics on Multiple Criteria Decision Making
Verlag: Springer Berlin Heidelberg
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No existing multi-objective evolutionary algorithms (MO-EAs) have ever been applied to problems with more than 1000 real-valued decision variables. Yet the real world is full of large and complex multi-objective problems. Motivated by the recent success of SaNSDE [1], an adaptive differential evolution algorithm that is capable of dealing with more than 1000 real-valued decision variables effectively and efficiently, this paper extends the ideas behind SaNSDE to develop a novel MOEA named MOSaNSDE. Our preliminary experimental studies have shown that MOSaNSDE outperforms state-of-the-art MOEAs significantly on most problems we have tested, in terms of both convergence and diversity metrics. Such encouraging results call for a more in-depth study of MOSaNSDE in the future, especially about its scalability.