國立高雄大學圖資館 |

Language: English

Back

Multi-objective swarm intelligenceth...

Dehuri, Satchidananda.

Multi-objective swarm intelligencetheoretical advances and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multi-objective swarm intelligenceedited by Satchidananda Dehuri, Alok Kumar Jagadev, Mrutyunjaya Panda.
Reminder of title:	theoretical advances and applications /
other author:	Panda, Mrutyunjaya.
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg :2015.
Description:	xiv, 201 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Swarm intelligence.
Online resource:	http://dx.doi.org/10.1007/978-3-662-46309-3
ISBN:	9783662463093 (electronic bk.)

Multi-objective swarm intelligencetheoretical 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. 216..

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:

237730
Swarm intelligence.

LC Class. No.: Q337.3

Dewey Class. No.: 006.3824

Multi-objective swarm intelligencetheoretical advances and applications /
LDR:02216nmm a2200325 a 4500 001 462699
003 DE-He213
005 20151027143245.0
006 m d
007 cr nn 008maaau
008 151119s2015 gw s 0 eng d
020 $a 9783662463093 (electronic bk.)
020 $a 9783662463086 (paper)
024 7 $a 10.1007/978-3-662-46309-3 $2 doi
035 $a 978-3-662-46309-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q337.3
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.3824 $2 23
090 $a Q337.3 $b .M961 2015
245 0 0 $a Multi-objective swarm intelligence $h [electronic resource] : $b theoretical advances and applications / $c edited by Satchidananda Dehuri, Alok Kumar Jagadev, Mrutyunjaya Panda.
260 $a Berlin, Heidelberg : $b Springer Berlin Heidelberg : $b Imprint: Springer, $c 2015.
300 $a xiv, 201 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.592
505 0 $a 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.
520 $a 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.
650 0 $a Swarm intelligence. $3 237730
650 0 $a Mathematical optimization. $3 183292
650 1 4 $a Engineering. $3 210888
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
700 1 $a Panda, Mrutyunjaya. $3 679696
700 1 $a Dehuri, Satchidananda. $3 276576
700 1 $a Jagadev, Alok Kumar. $3 715806
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v. 216. $3 380871
856 4 0 $u http://dx.doi.org/10.1007/978-3-662-46309-3
950 $a Engineering (Springer-11647)