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Principles in Noisy Optimization

Applied to Multi-agent Coordination

  • Pratyusha Rakshit
  • Amit Konar

Part of the Cognitive Intelligence and Robotics book series (CIR)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Pratyusha Rakshit, Amit Konar
    Pages 1-56
  3. Pratyusha Rakshit, Amit Konar
    Pages 57-88
  4. Pratyusha Rakshit, Amit Konar
    Pages 355-361
  5. Back Matter
    Pages 363-367

About this book

Introduction

Noisy optimization is a topic of growing interest for researchers working on mainstream optimization problems. Although several techniques for dealing with stochastic noise in optimization problems are covered in journals and conference proceedings, today there are virtually no books that approach noisy optimization from a layman’s perspective; this book remedies that gap.

Beginning with the foundations of evolutionary optimization, the book subsequently explores the principles of noisy optimization in single and multi-objective settings, and presents detailed illustrations of the principles developed for application in real-world multi-agent coordination problems. Special emphasis is given to the design of intelligent algorithms for noisy optimization in real-time applications. The book is unique in terms of its content, writing style and above all its simplicity, which will appeal to readers with a broad range of backgrounds.

The book is divided into 7 chapters, the first of which provides an introduction to Swarm and Evolutionary Optimization algorithms. Chapter 2 includes a thorough review of agent architectures for multi-agent coordination. In turn, Chapter 3 provides an extensive review of noisy optimization, while Chapter 4 addresses issues of noise handling in the context of single-objective optimization problems. An illustrative case study on multi-robot path-planning in the presence of measurement noise is also highlighted in this chapter. Chapter 5 deals with noisy multi-objective optimization and includes a case study on noisy multi-robot box-pushing. In Chapter 6, the authors examine the scope of various algorithms in noisy optimization problems. Lastly, Chapter 7 summarizes the main results obtained in the previous chapters and elaborates on the book’s potential with regard to real-world noisy optimization problems.

Keywords

Noisy Optimization Multi-Agent Co-ordination Measurement Noise Adaptive Sampling Robust Selection

Authors and affiliations

  • Pratyusha Rakshit
    • 1
  • Amit Konar
    • 2
  1. 1.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-10-8642-7
  • Copyright Information Springer Nature Singapore Pte Ltd. 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science
  • Print ISBN 978-981-10-8641-0
  • Online ISBN 978-981-10-8642-7
  • Series Print ISSN 2520-1956
  • Series Online ISSN 2520-1964
  • Buy this book on publisher's site