9783662463086-3662463083-Multi-objective Swarm Intelligence: Theoretical Advances and Applications (Studies in Computational Intelligence, 592)

Multi-objective Swarm Intelligence: Theoretical Advances and Applications (Studies in Computational Intelligence, 592)

ISBN-13: 9783662463086
ISBN-10: 3662463083
Edition: 2015
Author: Satchidananda Dehuri, Mrutyunjaya Panda, Alok Kumar Jagadev
Publication date: 2015
Publisher: Springer
Format: Hardcover 215 pages
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Book details

ISBN-13: 9783662463086
ISBN-10: 3662463083
Edition: 2015
Author: Satchidananda Dehuri, Mrutyunjaya Panda, Alok Kumar Jagadev
Publication date: 2015
Publisher: Springer
Format: Hardcover 215 pages

Summary

Multi-objective Swarm Intelligence: Theoretical Advances and Applications (Studies in Computational Intelligence, 592) (ISBN-13: 9783662463086 and ISBN-10: 3662463083), written by authors Satchidananda Dehuri, Mrutyunjaya Panda, Alok Kumar Jagadev, was published by Springer in 2015. With an overall rating of 4.5 stars, it's a notable title among other AI & Machine Learning (Computer Science) books. You can easily purchase or rent Multi-objective Swarm Intelligence: Theoretical Advances and Applications (Studies in Computational Intelligence, 592) (Hardcover) from BooksRun, along with many other new and used AI & Machine Learning books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.34.

Description

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.       
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