Optimization problems exist in nearly every engineering field. Neural Network Algorithms and Their Engineering Applications presents the relevant techniques used to improve the global search ability of neural network algorithms in solving complex engineering problems with multimodal properties. The book provides readers with a complete study of how to use artificial neural networks to design a population-based metaheuristic algorithm, which in turn promotes the application of artificial neural networks in the field of engineering optimization. The authors provide a deep discussion for the potential application of machine learning methods in improving the optimization performance of the neural network algorithm, helping readers understand how to use machine learning methods to design improved versions of the algorithm. The book provides readers with a wealth of source code covering all the applied algorithms. The code applications enhance readers' understanding of the methods covered in the book and also facilitate readers' ability to apply the algorithms to their own research and development projects.

• Provides a comprehensive understanding of the development of metaheuristics, helping readers grasp the principle of employing artificial neural networks to design a population-based metaheuristic algorithm
• Shows readers how to overcome the challenges faced in applying neural network algorithms to complex engineering optimization problems with multimodal properties
• Demonstrates how to design new variants of neural network algorithm and how to apply machine learning methods to the neural network algorithm
• Provides source code to help readers solve engineering optimization problems and shows readers how to develop the offered source code to create innovative solutions to their problems