Название: An Introduction to Metaheuristics for Optimization, Second Edition
Автор: Bastien Chopard, Marco Tomassini
Издательство: Springer
Серия: Natural Computing Series
Год: 2026
Страниц: 309
Язык: английский
Формат: pdf (true), epub
Размер: 34.4 MB
This book proposes an introduction to metaheuristics, combining a theoretical understanding with the practical skill to use and develop these methods. Optimization is central to most domains of science, whether academic or industrial. The solution to many real life problems rely on our ability to find the maximum or minimum of some quantity of interest. However, many of these problems are referred to as “hard optimization” problems, meaning that they quickly become numerically intractable and cannot be solved by traditional optimization techniques. Metaheuristics are methods, inspired by physical processes, Darwinian evolution, animal behaviors, and other phenomena observed in Nature, which usually find optimal values of satisfactory quality within acceptable computing resources. This textbook is suitable for advanced undergraduates in Computer Science and engineering, as well as for students and researchers from other disciplines looking for a concise and clear introduction to metaheuristic methods for optimization. Finally, given that computational methods must be programmed or, at least, researchers must be able to understand and make good use of the many existing packages, we have included an appendix that proposes a simple code architecture to implement oneself metaheuristics, by exploiting their generic computational structure. This programming model is developed in C++, but the proposed concepts are simple and can easily be translated to other languages.
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