An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks

作者：

Highlights：

• This paper proposes a boosted MFO for global search and kernel extreme learning machines.

• Two strategies have been introduced into MFO for a more stable balance.

• The extensive results on benchmark problems and real datasets have been performed.

• A hybrid kernel extreme learning machine model is established for financial stress prediction.

摘要

•This paper proposes a boosted MFO for global search and kernel extreme learning machines.•Two strategies have been introduced into MFO for a more stable balance.•The extensive results on benchmark problems and real datasets have been performed.•A hybrid kernel extreme learning machine model is established for financial stress prediction.

论文关键词：Moth-flame optimization algorithm,Parameter optimization,Chaotic local search,Gaussian mutation,Kernel extreme learning machine

论文评审过程：Received 12 December 2018, Revised 6 March 2019, Accepted 25 March 2019, Available online 3 April 2019, Version of Record 10 April 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.03.043