Multi-objective Swarm Intelligence : Theoretical Advances and Applications

Satchidananda Dehuri

ISBN 10: 3662463083 / ISBN 13: 9783662463086

Published by Springer Berlin Heidelberg, 2015

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783662463086

Bibliographic Details

• Title: Multi-objective Swarm Intelligence : Theoretical Advances and Applications
• Publisher: Springer Berlin Heidelberg
• Publication Date: 2015
• Language: English
• ISBN 10: 3662463083
• ISBN 13: 9783662463086
• Binding: Buch
• Condition: Neu

About this title

Synopsis

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.