A Hybrid-coded Human Learning Optimization for mixed-variable optimization problems

作者：

Highlights：

• This paper proposes a new hybrid-coded HLO (HcHLO) framework to tackle mix-coded problems more efficiently and effectively.

• A new continuous human learning optimization algorithm is presented based on the linear learning mechanism of humans.

• The results show that the HcHLO achieves the best-known overall performance so far on the tested mix-coded problems.

摘要

•This paper proposes a new hybrid-coded HLO (HcHLO) framework to tackle mix-coded problems more efficiently and effectively.•A new continuous human learning optimization algorithm is presented based on the linear learning mechanism of humans.•The results show that the HcHLO achieves the best-known overall performance so far on the tested mix-coded problems.

论文关键词：Human learning optimization,Meta-heuristic,Continuous human learning optimization,Hybrid-coded problems,Mixed-variable problems

论文评审过程：Received 17 September 2016, Revised 12 April 2017, Accepted 24 April 2017, Available online 26 April 2017, Version of Record 12 May 2017.

论文官网地址：https://doi.org/10.1016/j.knosys.2017.04.015