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Probability Collectives
A Distributed Multi-agent System Approach for Optimization
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Gewicht:
424 g
Format:
241x160x15 mm
Beschreibung:
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).
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
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.
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.