Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis
作者：
Highlights：
• A double adaptive weight mechanism is introduced into the original Moth-flame optimization algorithm.
• WEMFO was compared with some famous optimizers on benchmark functions.
• The performance of WEMFO is evaluated in different dimensions.
• WEMFO is used to optimize the parameters of kernel extreme learning machine.
• The performance of WEMFO is evaluated on six typical engineering problems.
摘要
•A double adaptive weight mechanism is introduced into the original Moth-flame optimization algorithm.•WEMFO was compared with some famous optimizers on benchmark functions.•The performance of WEMFO is evaluated in different dimensions.•WEMFO is used to optimize the parameters of kernel extreme learning machine.•The performance of WEMFO is evaluated on six typical engineering problems.
论文关键词：Moth flame optimizer,Medical diagnosis,Parameter optimization,Performance optimization,Kernel Extreme Learning Machine
论文评审过程：Received 2 September 2020, Revised 3 December 2020, Accepted 28 December 2020, Available online 31 December 2020, Version of Record 14 January 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.106728
