Engineering optimization: an introduction with metaheuristic applications

Optimization is applicable to many fields including engineering design, computer sciences, management science and scheduling, and economics. Modern optimization techniques are usually heuristic and metaheuristic, and this book introduces all of the main metaheuristic algorithms and their applications in optimization. The author has divided the book into three parts: Part I: Introductionand Fundamentals of Optimization and Algorithms; Part II: Metaheuristic Algorithms; and Part III: Other Metaheuristics and Mulitobjective Optimization. Part I provides a brief introduction to the nature of optimization and the commonprocedure to solve such problems. Random number generation and Monte Carlo Methods are also discussed. Part II introduces the algorithms in great details including Genetic Algorithms, Simulated Annealing, Ant Algorithms, Bee Algorithms, Bat Algorithms, Particle Swarm Optimization, and others. Part III briefly introduces other algorithms including Tabu Search, Markov chain Monte Carlo method, and multiobjective optimization. An appendix features the implementationof some of the important/popular algorithms in MATLAB and Octave so that readers can reference them for learning or solving other optimization problems.

• ISBN: 978-0-470-58246-6
• Editorial: John Wiley & Sons
• Encuadernacion: Tela
• Páginas: 376
• Fecha Publicación: 16/07/2010
• Nº Volúmenes: 1
• Idioma: Inglés