Multi-objective Swarm Intelligence

Satchidananda Dehuri

ISBN 10: 3662523655 / ISBN 13: 9783662523650

Verlag: Springer Berlin Heidelberg, Springer Berlin Heidelberg Okt 2016, 2016

Beschreibung

Neuware -The aim of this book is to understand the state-of-the-art theoretical and practical advances of swarm intelligence. It comprises seven contemporary relevant chapters. In chapter 1, a review of Bacteria Foraging Optimization (BFO) techniques for both single and multiple criterions problem is presented. A survey on swarm intelligence for multiple and many objectives optimization is presented in chapter 2 along with a topical study on EEG signal analysis. Without compromising the extensive simulation study, a comparative study of variants of MOPSO is provided in chapter 3. Intractable problems like subset and job scheduling problems are discussed in chapters 4 and 7 by different hybrid swarm intelligence techniques. An attempt to study image enhancement by ant colony optimization is made in chapter 5. Finally, chapter 7 covers the aspect of uncertainty in data by hybrid PSO.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 216 pp. Englisch.

Bestandsnummer des Verkäufers 9783662523650

Bibliografische Details

• Titel: Multi-objective Swarm Intelligence
• Verlag: Springer Berlin Heidelberg, Springer Berlin Heidelberg Okt 2016
• Erscheinungsjahr: 2016
• Sprache: Englisch
• ISBN-10: 3662523655
• ISBN-13: 9783662523650
• Einband: Taschenbuch
• Zustand: Neu

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Inhaltsangabe

The aim of this book is to understand the state-of-the-art theoretical and practical advances of swarm intelligence. It comprises seven contemporary relevant chapters. In chapter 1, a review of Bacteria Foraging Optimization (BFO) techniques for both single and multiple criterions problem is presented. A survey on swarm intelligence for multiple and many objectives optimization is presented in chapter 2 along with a topical study on EEG signal analysis. Without compromising the extensive simulation study, a comparative study of variants of MOPSO is provided in chapter 3. Intractable problems like subset and job scheduling problems are discussed in chapters 4 and 7 by different hybrid swarm intelligence techniques. An attempt to study image enhancement by ant colony optimization is made in chapter 5. Finally, chapter 7 covers the aspect of uncertainty in data by hybrid PSO.