Multi-objective Swarm Intelligence Theoretical Advances and Applications /

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 present...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Dehuri, Satchidananda (Επιμελητής έκδοσης), Jagadev, Alok Kumar (Επιμελητής έκδοσης), Panda, Mrutyunjaya (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2015.
Σειρά:Studies in Computational Intelligence, 592
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |a Multi-objective Swarm Intelligence  |h [electronic resource] :  |b Theoretical Advances and Applications /  |c edited by Satchidananda Dehuri, Alok Kumar Jagadev, Mrutyunjaya Panda. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2015. 
300 |a XIV, 201 p. 60 illus., 11 illus. in color.  |b online resource. 
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490 1 |a Studies in Computational Intelligence,  |x 1860-949X ;  |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 Engineering. 
650 0 |a Artificial intelligence. 
650 0 |a Computational intelligence. 
650 1 4 |a Engineering. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
700 1 |a Dehuri, Satchidananda.  |e editor. 
700 1 |a Jagadev, Alok Kumar.  |e editor. 
700 1 |a Panda, Mrutyunjaya.  |e editor. 
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776 0 8 |i Printed edition:  |z 9783662463086 
830 0 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 592 
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950 |a Engineering (Springer-11647)