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.
