Affiliations: [a] Department of EE, China Jiliang University, Hangzhou,
Zhejiang, China | [b] College of EE, Zhejiang University, Hangzhou,
Zhejiang, China | Université Laval, Canada | Tohoku University, Japan
Correspondence:
[*]
Corresponding author: Siguang An, Department of EE, China
Jiliang University, Hangzhou 310018, Zhejiang, China. E-mail: annsg@126.com
Abstract: An improved vector cross-entropy (CE) method is proposed to provide
a potential candidate for solving multi-objective inverse problems with a large
number of variables. To balance the exploitation and exploration searches, the
whole iterative process is divided into two phases: diversification and
intensification phases. Different parameter evolutionary mechanisms of the
probability density functions (pdfs) are proposed for different phases. To
speed up the convergence rate, a dynamic evolutionary mechanism is proposed. To
enhance the diversity of the sampling points, a mutation manipulation is
introduced. The ZDT test functions and a high frequency inverse problem are
used as the case studies to testify the effectiveness and efficiency of the
proposed method.
