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. Users will find a wealth of source code that covers all applied algorithms. Code applications enhance readers'' understanding of methods covered and 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 algorithms and how to apply machine learning methods to neural network algorithms- Covers source code to help readers solve engineering optimization problems- Shows readers how to develop the offered source code to create innovative solutions to their problems

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. Users will find a wealth of source code that covers all applied algorithms. Code applications enhance readers'' understanding of methods covered and 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 algorithms and how to apply machine learning methods to neural network algorithms- Covers source code to help readers solve engineering optimization problems- Shows readers how to develop the offered source code to create innovative solutions to their problems