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

by Anand Jayant Kulkarni, Kang Tai, Ajith Abraham

Overview

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.

Product Details

ISBN-13:	9783319159997
Publisher:	Springer International Publishing
Publication date:	03/23/2015
Series:	Intelligent Systems Reference Library , #86
Edition description:	2015
Pages:	157
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

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.

Editorial Reviews

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)

From the Publisher