Evolutionary Large-Scale Multi-Objective Optimization and Applications (eBook)
352 Seiten
Wiley (Verlag)
978-1-394-17842-1 (ISBN)
Tackle the most challenging problems in science and engineering with these cutting-edge algorithms
Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach.
Evolutionary Large-Scale Multi-Objective Optimization and Applications constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it's a must-read for students and researchers facing these famously complex but crucial optimization problems.
The book's readers will also find:
- Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more
- Discussion of benchmark problems and performance indicators for LSMOPs
- Presentation of a new taxonomy of algorithms in the field
Evolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.
Xingyi Zhang, PhD, is a Professor in the School of Computer Science and Technology at Anhui University, Hefei, China. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, and a member of the editorial board for Complex and Intelligent Systems.
Ran Cheng, PhD, is an Associate Professor in the Department of Computer Science and Engineering at the Southern University of Science and Technology, China. He is an Associate Editor for the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Artificial Intelligence, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Transactions on Cognitive and Developmental Systems, and ACM Transactions on Evolutionary Learning and Optimization.
Ye Tian, PhD, is an Associate Professor in School of Computer Science and Technology at Anhui University, Hefei, China. He also serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation.
Yaochu Jin, PhD, is a Chair Professor of Artificial Intelligence, Head of the Trustworthy and General Artificial Intelligence Laboratory, Westlake University, China. He was an Alexander von Humboldt Professor of Artificial Intelligence at the Bielefeld University, Germany, and Distinguished Chair in Computational Intelligence at the University of Surrey, United Kingdom.
Tackle the most challenging problems in science and engineering with these cutting-edge algorithms Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach. Evolutionary Large-Scale Multi-Objective Optimization and Applications constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it s a must-read for students and researchers facing these famously complex but crucial optimization problems. The book s readers will also find: Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more Discussion of benchmark problems and performance indicators for LSMOPs Presentation of a new taxonomy of algorithms in the field Evolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.
| Erscheint lt. Verlag | 22.7.2024 |
|---|---|
| Sprache | englisch |
| Themenwelt | Technik ► Bauwesen |
| Technik ► Elektrotechnik / Energietechnik | |
| ISBN-10 | 1-394-17842-5 / 1394178425 |
| ISBN-13 | 978-1-394-17842-1 / 9781394178421 |
| Haben Sie eine Frage zum Produkt? |
PDF (Adobe DRM)Größe: 6,9 MB
Kopierschutz: Adobe-DRM
Adobe-DRM ist ein Kopierschutz, der das eBook vor Mißbrauch schützen soll. Dabei wird das eBook bereits beim Download auf Ihre persönliche Adobe-ID autorisiert. Lesen können Sie das eBook dann nur auf den Geräten, welche ebenfalls auf Ihre Adobe-ID registriert sind.
Details zum Adobe-DRM
Dateiformat: PDF (Portable Document Format)
Mit einem festen Seitenlayout eignet sich die PDF besonders für Fachbücher mit Spalten, Tabellen und Abbildungen. Eine PDF kann auf fast allen Geräten angezeigt werden, ist aber für kleine Displays (Smartphone, eReader) nur eingeschränkt geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen eine
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen eine
Geräteliste und zusätzliche Hinweise
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
aus dem Bereich
