Kulkarni / Tai / Abraham Probability Collectives

A Distributed Multi-agent System Approach for Optimization
1. Auflage 2015
ISBN: 978-3-319-16000-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 86, 157 Seiten, eBook

Reihe: Intelligent Systems Reference Library

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.

Zielgruppe

Research

Autoren/Hrsg.

Kulkarni, Anand Jayant

Tai, Kang

Abraham, Ajith

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.