Hybrid teaching–learning-based optimization and neural network algorithm for engineering design optimization problems
作者：
Highlights：
• A novel hybrid algorithm called TLNNA is proposed based on TLBO and NNA, which is an algorithm without any effort for fine tuning initial parameters.
• TLNNA has excellent global optimization ability of NNA and fast convergence rate of TLBO by the designed dynamic grouping mechanism.
• TLNNA is examined using 30 well-known unconstrained benchmark test functions and 4 challenging constrained engineering design problems.
摘要
•A novel hybrid algorithm called TLNNA is proposed based on TLBO and NNA, which is an algorithm without any effort for fine tuning initial parameters.•TLNNA has excellent global optimization ability of NNA and fast convergence rate of TLBO by the designed dynamic grouping mechanism.•TLNNA is examined using 30 well-known unconstrained benchmark test functions and 4 challenging constrained engineering design problems.
论文关键词：Neural network algorithm,Artificial neural networks,Teaching–learning-based optimization,Engineering optimization
论文评审过程：Received 21 December 2018, Revised 4 July 2019, Accepted 6 July 2019, Available online 15 July 2019, Version of Record 18 November 2019.
论文官网地址：https://doi.org/10.1016/j.knosys.2019.07.007
