Broad learning approach to Surrogate-Assisted Multi-Objective evolutionary fuzzy clustering algorithm based on reference points for color image segmentation

作者：

Highlights：

• BLS is employed as the surrogate model to assist the evolutionary process.

• Fuzzy clustering functions with image region information are constructed.

• An adaptively updating strategy for the parameters of BLS is adopted.

• A model update mechanism is used to boost the prediction accuracy of BLS.

摘要

•BLS is employed as the surrogate model to assist the evolutionary process.•Fuzzy clustering functions with image region information are constructed.•An adaptively updating strategy for the parameters of BLS is adopted.•A model update mechanism is used to boost the prediction accuracy of BLS.

论文关键词：Image segmentation,Fuzzy clustering,Multi-objective optimization,Broad learning system,Surrogate-assisted evolutionary algorithm,Reference points

论文评审过程：Received 20 October 2021, Revised 13 February 2022, Accepted 27 March 2022, Available online 1 April 2022, Version of Record 4 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117015