Solving Large-Scale Multiobjective Optimization Problems With Sparse Optimal Solutions via Unsupervised Neural Networks

Due to the curse of dimensionality of search space, it is extremely difficult for evolutionary algorithms to approximate the optimal solutions of large-scale multiobjective optimization problems (LMOPs) by using a limited budget of evaluations. If the Pareto-optimal subspace is approximated during t...

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Veröffentlicht in:	IEEE transactions on cybernetics Jg. 51; H. 6; S. 3115 - 3128
Hauptverfasser:	Tian, Ye, Lu, Chang, Zhang, Xingyi, Tan, Kay Chen, Jin, Yaochu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Approximation Computer science Denoising autoencoder (DAE) Evolutionary algorithms Evolutionary computation Genetic algorithms large-scale multiobjective optimization Machine learning Multiple objective analysis Neural networks Noise reduction Optimization Pareto optimization Pareto optimum Pareto-optimal subspace Representations restricted Boltzmann machine (RBM) Search problems Searching Sociology sparse Pareto-optimal solutions Statistics Subspaces
ISSN:	2168-2267, 2168-2275, 2168-2275
Online-Zugang:	Volltext
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