.png){kind=icon}
返回
Hybrid teaching-learning-based optimization and neural network algorithm for engineering design optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2019.07.007.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Neural network algorithm
Artificial neural networks
Teaching-learning-based optimization
Engineering optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
.png){kind=icon}
A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm受生物神经系统启发的动态元启发式优化模型: 神经网络算法
PREAI
分享收藏Binary dragonfly optimization for feature selection using time-varying transfer functions使用时变传递函数进行特征选择的二进制蜻蜓优化
OAAI
OAAI分享收藏分享收藏Constraint-handling in genetic algorithms through the use of dominance-based tournament selection
PREAI
分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏
查看更多内容