Advanced orthogonal moth flame optimization with Broyden–Fletcher–Goldfarb–Shanno algorithm: Framework and real-world problems

作者：

Highlights：

• BFGSOLMFO is proposed for global optimization and real-world problems.

• OL is used to enhance exploitation and exploration ability of MFO.

• BFGS is employed to further excavate the potential global best solution.

摘要

•BFGSOLMFO is proposed for global optimization and real-world problems.•OL is used to enhance exploitation and exploration ability of MFO.•BFGS is employed to further excavate the potential global best solution.

论文关键词：Engineering problems,Moth-flame optimization,Orthogonal learning,Broyden-fletcher-goldfarb-shanno,Global optimization

论文评审过程：Available online 6 June 2020, Version of Record 23 June 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113617