Probability Collectives

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

Inhalt
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.