Probability Collectives A Distributed Multi-agent System Approach for Optimization /

This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniqu...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Kulkarni, Anand Jayant (Συγγραφέας), Tai, Kang (Συγγραφέας), Abraham, Ajith (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2015.
Σειρά:Intelligent Systems Reference Library, 86
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Kulkarni, Anand Jayant.  |e author. 
245 1 0 |a Probability Collectives  |h [electronic resource] :  |b A Distributed Multi-agent System Approach for Optimization /  |c by Anand Jayant Kulkarni, Kang Tai, Ajith Abraham. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2015. 
300 |a IX, 157 p. 68 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a Intelligent Systems Reference Library,  |x 1868-4394 ;  |v 86 
505 0 |a Introduction to Optimization -- Probability Collectives: A Distributed Optimization Approach -- Constrained Probability Collectives: A Heuristic Approach -- Constrained Probability Collectives with a Penalty Function Approach -- Constrained Probability Collectives With Feasibility-Based Rule I -- Probability Collectives for Discrete and Mixed Variable Problems -- Probability Collectives with Feasibility-Based Rule II. 
520 |a This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts. 
650 0 |a Engineering. 
650 0 |a Artificial intelligence. 
650 0 |a Statistical physics. 
650 0 |a Dynamical systems. 
650 0 |a Computational intelligence. 
650 1 4 |a Engineering. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Statistical Physics, Dynamical Systems and Complexity. 
700 1 |a Tai, Kang.  |e author. 
700 1 |a Abraham, Ajith.  |e author. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783319159997 
830 0 |a Intelligent Systems Reference Library,  |x 1868-4394 ;  |v 86 
856 4 0 |u http://dx.doi.org/10.1007/978-3-319-16000-9  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)