Hybrid teaching–learning-based optimization and neural network algorithm for engineering design optimization problems - Zhang, Yiying; Jin, Zhigang; Chen, Ye (Knowledge-based systems, 2020)
Neural network algorithm (NNA) is one of the newest meta-heuristic algorithms, which is inspired by biological nervous systems and artificial neural networks. Benefiting from the unique structure of artificial neural networks, NNA has good global search ability. However, slow convergence is its drawback that restricts its practical application. Teaching–learning-based optimization (TLBO) is an algorithm without any effort for fine tuning initial parameters, which has fast convergence speed while it is easy to fall into local optimum in solving complex global optimization problems. Considering the features of NNA and TLBO, an effective hybrid method based on TLBO and NNA, named TLNNA, is proposed for solving engineering optimization problems. The performance of TLNNA for 30 well-known unconstrained benchmark functions and 4 challenging engineering optimization problems is examined and the optimization results are compared with other competitive meta-heuristic algorithms. Such comparisons suggest that TLNNA has not only good global search ability of NNA but also fast convergence speed of TLBO and is more successful for most test problems in terms of solution quality and computational efficiency. •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.