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

✍ Scribed by Anand Jayant Kulkarni, Kang Tai, Ajith Abraham (auth.)

Publisher: Springer International Publishing
Year: 2015
Tongue: English
Leaves: 162
Series: Intelligent Systems Reference Library 86
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-ix
Introduction to Optimization....Pages 1-13
Probability Collectives: A Distributed Optimization Approach....Pages 15-35
Constrained Probability Collectives: A Heuristic Approach....Pages 37-60
Constrained Probability Collectives with a Penalty Function Approach....Pages 61-72
Constrained Probability Collectives with Feasibility Based Rule I....Pages 73-93
Probability Collectives for Discrete and Mixed Variable Problems....Pages 95-125
Probability Collectives with Feasibility-Based Rule II....Pages 127-144
Back Matter....Pages 145-157

✦ Subjects

Computational Intelligence; Artificial Intelligence (incl. Robotics); Statistical Physics, Dynamical Systems and Complexity

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