Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering (eBook)

Gustavo Mendes Platt, Xin-She Yang, Antônio José Silva Neto (Herausgeber)

eBook Download: PDF

2018 | 1st ed. 2019
XXIX, 284 Seiten
Springer International Publishing (Verlag)
978-3-319-96433-1 (ISBN)

Infos

This book focuses on metaheuristic methods and its applications to real-world problems in Engineering. The first part describes some key metaheuristic methods, such as Bat Algorithms, Particle Swarm Optimization, Differential Evolution, and Particle Collision Algorithms. Improved versions of these methods and strategies for parameter tuning are also presented, both of which are essential for the practical use of these important computational tools. The second part then applies metaheuristics to problems, mainly in Civil, Mechanical, Chemical, Electrical, and Nuclear Engineering. Other methods, such as the Flower Pollination Algorithm, Symbiotic Organisms Search, Cross-Entropy Algorithm, Artificial Bee Colonies, Population-Based Incremental Learning, Cuckoo Search, and Genetic Algorithms, are also presented. The book is rounded out by recently developed strategies, or hybrid improved versions of existing methods, such as the Lightning Optimization Algorithm, Differential Evolution with Particle Collisions, and Ant Colony Optimization with Dispersion - state-of-the-art approaches for the application of computational intelligence to engineering problems.

The wide variety of methods and applications, as well as the original results to problems of practical engineering interest, represent the primary differentiation and distinctive quality of this book. Furthermore, it gathers contributions by authors from four countries - some of which are the original proponents of the methods presented - and 18 research centers around the globe.

Gustavo Mendes Platt obtained his PhD from the Federal University of Rio de Janeiro (UFRJ, Brazil, 2001), and is currently an Associate Professor at Universidade Federal do Rio Grande. His research interests include phase and chemical equilibrium, stochastic optimization methods and biomaterials. He has published papers in several high-impact journals. Further, he is a member of the Editorial Board of the International Review of Chemical Engineering (Testo Stampato). He is the former Coordinator of the Graduate Program in Computational Modeling (2015-2018) of the Polytechnic Institute (UERJ), and the former Head of the Optimization and Thermodynamics Laboratory (LTO) at the Polytechnic Institute (UERJ).

Xin She-Yang is a Reader in modeling and optimization at Middlesex University and an elected Bye-Fellow at Cambridge University's Downing College. His research interests include nature-inspired computation, swarm intelligence, modeling, and optimization. Yang received a DPhil (PhD) in applied mathematics from the University of Oxford. He is currently chair of IEEE Computational Intelligence Society's Task Force on Business Intelligence and Knowledge Management.

Antônio José da Silva Neto is a Mechanical and Nuclear Engineer (Universidade Federal do Rio de Janeiro - UFRJ, Brazil, 1983), holding an MSc in Nuclear Engineering (UFRJ, 1989), and a PhD in Mechanical Engineering (North Carolina State University, USA, 1993). He worked as engineer and researcher for the Brazilian National Nuclear Energy Commission - CNEN (1984-1986), and as consultant and engineer for Promon Engineering (1986-1997). In 1997 he joined the faculty of Universidade do Estado do Rio de Janeiro - UERJ, where he is currently a Full Professor at the Polytechnic Institute.  

Produkt-Details

• Erscheint lt. Verlag: 25.9.2018
• Zusatzinfo: XXIX, 284 p. 98 illus., 61 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Technik
• Schlagworte: bioinspired algorithms • Computational Intelligence • engineering applications • evolutionary algorithms • Metaheuristics • Nature-Inspired Algorithms • Optimization
• ISBN-10: 3-319-96433-X / 331996433X
• ISBN-13: 978-3-319-96433-1 / 9783319964331