\u003ci\u003eNeural Network Algorithms and Their Engineering Applications\u003c/i\u003e 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.\u003cbr\u003e\u003cbr\u003eThe 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.\u003cul\u003e\u003cli\u003eProvides 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\u003c/li\u003e\u003cli\u003eShows readers how to overcome the challenges faced in applying neural network algorithms to complex engineering optimization problems with multimodal properties\u003c/li\u003e\u003cli\u003eDemonstrates how to design new variants of neural network algorithms and how to apply machine learning methods to neural network algorithms\u003c/li\u003e\u003cli\u003eCovers source code to help readers solve engineering optimization problems \u003c/li\u003e\u003cli\u003eShows readers how to develop the offered source code to create innovative solutions to their problems\u003c/li\u003e\u003c/ul\u003e
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