Provides the core and underlying principles and analysis of the different concepts in the framework of Collective Intelligence for modeling and controlling distributed Multi-Agent Systems
Discusses in detail the modified Probability Collectives approach proposed by the authors
Emphasizes development of the fundamental results from basic concepts
Numerous examples/problems are worked out in the text allowing the reader to gain further insight into the associated concepts
Written for engineers, scientists and students in Optimization, Computational Intelligence or Artificial Intelligence and particularly involved in the Collective Intelligence field
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.
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.
"The book contains numerous overviews of the optimization literature, and each chapter has a comprehensive bibliography. The book will be of interest to both students who are interested in optimization and practitioners.” (J. P. E. Hodgson, Computing Reviews, June, 2015)
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.
"The book contains numerous overviews of the optimization literature, and each chapter has a comprehensive bibliography. The book will be of interest to both students who are interested in optimization and practitioners." (J. P. E. Hodgson, Computing Reviews, June, 2015)
Dr. Ajith Abraham is Director of the Machine Intelligence Research (MIR) Labs, a global network of research laboratories with headquarters near Seattle, WA, USA. He is an author/co-author of more than 750 scientific publications. He is founding Chair of the International Conference of Computational Aspects of Social Networks (CASoN), Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (since 2008), and a Distinguished Lecturer of the IEEE Computer Society representing Europe (since 2011).
Inhaltsverzeichnis
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.
Klappentext
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.
Provides the core and underlying principles and analysis of the different concepts in the framework of Collective Intelligence for modeling and controlling distributed Multi-Agent Systems
Discusses in detail the modified Probability Collectives approach proposed by the authors
Emphasizes development of the fundamental results from basic concepts
Numerous examples/problems are worked out in the text allowing the reader to gain further insight into the associated concepts
Written for engineers, scientists and students in Optimization, Computational Intelligence or Artificial Intelligence and particularly involved in the Collective Intelligence field
Includes supplementary material: sn.pub/extras
