Principles in Noisy Optimization: Applied to Multi-agent Coordination

✍ Scribed by Pratyusha Rakshit, Amit Konar

• Publisher: Springer Singapore
• Year: 2018
• Tongue: English
• Leaves: 379
• Series: Cognitive Intelligence and Robotics
• Edition: 1st ed.
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xvi
Foundation in Evolutionary Optimization (Pratyusha Rakshit, Amit Konar)....Pages 1-56
Agents and Multi-agent Coordination (Pratyusha Rakshit, Amit Konar)....Pages 57-88
Recent Advances in Evolutionary Optimization in Noisy Environment—A Comprehensive Survey (Pratyusha Rakshit, Amit Konar)....Pages 89-169
Learning Automata and Niching-Induced Noisy Optimization for Multi-robot Path-Planning (Pratyusha Rakshit, Amit Konar)....Pages 171-242
Noisy Multi-objective Optimization for Multi-robot Box-Pushing Application (Pratyusha Rakshit, Amit Konar)....Pages 243-305
Enhancing Noise-Tolerant Behavior of Traditional Evolutionary and Swarm Algorithms (Pratyusha Rakshit, Amit Konar)....Pages 307-353
Conclusions and Future Directions (Pratyusha Rakshit, Amit Konar)....Pages 355-361
Back Matter ....Pages 363-367

✦ Subjects

Computer Science; Optimization; Theory of Computation