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2015 | Book

Introduction to Optimization Read chapter Read first chapter

Probability Collectives

A Distributed Multi-agent System Approach for Optimization

Authors: Anand Jayant Kulkarni, Kang Tai, Ajith Abraham

Publisher: Springer International Publishing

Book Series : Intelligent Systems Reference Library

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

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

Frontmatter
Chapter 1. Introduction to Optimization
Abstract
For almost all human activities and creations, there is a desire to do or be the best in some sense.
Anand Jayant Kulkarni, Kang Tai, Ajith Abraham
Chapter 2. Probability Collectives: A Distributed Optimization Approach
Abstract
An emerging Artificial Intelligence tool in the framework of Collective Intelligence (COIN) for modeling and controlling distributed Multi-agent System (MAS) referred to as Probability Collectives (PC) was first proposed by Dr. David Wolpert in 1999 in a technical report presented to NASA.
Anand Jayant Kulkarni, Kang Tai, Ajith Abraham
Chapter 3. Constrained Probability Collectives: A Heuristic Approach
Abstract
This chapter discusses an approach of handling the constraints in which the problem specific information is explicitly used.
Anand Jayant Kulkarni, Kang Tai, Ajith Abraham
Chapter 4. Constrained Probability Collectives with a Penalty Function Approach
Abstract
There are a number of traditional constraint handling methods available, such as gradient projection method, reduced gradient method, Lagrange multiplier method, aggregate constraint method, feasible direction based method, penalty based method, etc.
Anand Jayant Kulkarni, Kang Tai, Ajith Abraham
Chapter 5. Constrained Probability Collectives with Feasibility Based Rule I
Abstract
This chapter demonstrates further efforts to develop a generic constraint handling technique for PC in order to make it a more versatile optimization algorithm.
Anand Jayant Kulkarni, Kang Tai, Ajith Abraham
Chapter 6. Probability Collectives for Discrete and Mixed Variable Problems
Abstract
This chapter demonstrates the ability of PC for solving practically important discrete and mixed variable problems in structural and mechanical engineering domain. The truss structure problems such as 17-bar, 25-bar, 72-bar, 45-Bar, 10-Bar and 38-Bar, a helical compression spring design, a reinforced concrete beam design, stepped cantilever beam design and speed reducer were successfully solved.
Anand Jayant Kulkarni, Kang Tai, Ajith Abraham
Chapter 7. Probability Collectives with Feasibility-Based Rule II
Abstract
This rule is a variation of the Feasibility-based Rule I discussed in Chap. 5 and also allows the objective function and the constraint information to be considered separately.
Anand Jayant Kulkarni, Kang Tai, Ajith Abraham
Backmatter
Metadata
Title
Probability Collectives
Authors
Anand Jayant Kulkarni
Kang Tai
Ajith Abraham
Copyright Year
2015
Electronic ISBN
978-3-319-16000-9
Print ISBN
978-3-319-15999-7
DOI
https://doi.org/10.1007/978-3-319-16000-9

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