Abstract
An adaptive optimization method with hierarchical learning is proposed for nonstationary environments. It is composed of the following sub-systems: 1) search system based on the adaptive random optimization method; 2) environment change detection system; 3) pattern recognition system classifying the environment modes; and 4) memorizing system memorizing the features of environment modes in hierarchical order. Numerical simulation studies show that the use of proposed hierarchical learning improves the optimization performance even in rapidly changing environments.
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