Evolutionary Multi-Objective System Design: Theory and Applications Contributor(s): Nedjah, Nadia (Editor), Lopes, Heitor Silverio (Editor), de Macedo Mourelle, Luiza (Editor) |
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ISBN: 1498780288 ISBN-13: 9781498780285 Publisher: CRC Press
Binding Type: Hardcover - See All Available Formats & Editions Published: October 2017 Click for more in this series: Chapman & Hall/CRC Computer and Information Science |
Additional Information |
BISAC Categories: - Computers | Machine Theory - Computers | Computer Engineering - Technology & Engineering | Electronics - General |
Dewey: 620.004 |
LCCN: 2017001007 |
Series: Chapman & Hall/CRC Computer and Information Science |
Physical Information: 0.7" H x 6.6" W x 9.3" L (1.15 lbs) 242 pages |
Features: Bibliography, Glossary, Illustrated |
Descriptions, Reviews, Etc. |
Publisher Description: Real-world engineering problems often require concurrent optimization of several design objectives, which are conflicting in cases. This type of optimization is generally called multi-objective or multi-criterion optimization. The area of research that applies evolutionary methodologies to multi-objective optimization is of special and growing interest. It brings a viable computational solution to many real-world problems. Generally, multi-objective engineering problems do not have a straightforward optimal design. These kinds of problems usually inspire several solutions of equal efficiency, which achieve different trade-offs. Decision makers' preferences are normally used to select the most adequate design. Such preferences may be dictated before or after the optimization takes place. They may also be introduced interactively at different levels of the optimization process. Multi-objective optimization methods can be subdivided into classical and evolutionary. The classical methods usually aim at a single solution while the evolutionary methods provide a whole set of so-called Pareto-optimal solutions. Evolutionary Multi-Objective System Design: Theory and Applications provides a representation of the state-of-the-art in evolutionary multi-objective optimization research area and related new trends. It reports many innovative designs yielded by the application of such optimization methods. It also presents the application of multi-objective optimization to the following problems:
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