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Multi-objective swarm intelligence =...

Dehuri, Satchidananda.

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Multi-objective swarm intelligence = theoretical advances and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multi-objective swarm intelligence/ edited by Satchidananda Dehuri, Alok Kumar Jagadev, Mrutyunjaya Panda.
其他題名:	theoretical advances and applications /
其他作者:	Dehuri, Satchidananda.
出版者:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2015.,
面頁冊數:	xiv, 201 p. :ill., digital ;24 cm.
內容註:	Introduction -- Behavior of Bacterial Colony -- E.coli Bacterial Colonies -- Optimization based on E.coli Bacterial Colony -- Classification of BFO Algorithm -- Multi-objective optimization based on BF -- An overview of BFO Applications -- Conclusion.
Contained By:	Springer eBooks
標題:	Swarm intelligence. -
電子資源:	http://dx.doi.org/10.1007/978-3-662-46309-3
ISBN:	9783662463093 (electronic bk.)

Multi-objective swarm intelligence = theoretical advances and applications /
Multi-objective swarm intelligencetheoretical advances and applications /[electronic resource] :edited by Satchidananda Dehuri, Alok Kumar Jagadev, Mrutyunjaya Panda. - Berlin, Heidelberg :Springer Berlin Heidelberg :2015. - xiv, 201 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.5921860-949X ;. - Studies in computational intelligence ;v.379..

Introduction -- Behavior of Bacterial Colony -- E.coli Bacterial Colonies -- Optimization based on E.coli Bacterial Colony -- Classification of BFO Algorithm -- Multi-objective optimization based on BF -- An overview of BFO Applications -- Conclusion.

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.

ISBN: 9783662463093 (electronic bk.)

Standard No.: 10.1007/978-3-662-46309-3doiSubjects--Topical Terms:

577800
Swarm intelligence.

LC Class. No.: Q337.3

Dewey Class. No.: 006.3824

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