This book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. Differential evolution is a population-based meta-heuristic technique for global optimization capable of handling non-differentiable, non-linear and multi-modal objective functions. Many advances have been made recently in differential evolution, from theory to applications. This book comprises contributions which include theoretical developments in DE, performance comparisons of DE, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications. The book is useful for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research and natural computing.

This book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. Differential evolution is a population-based meta-heuristic technique for global optimization capable of handling non-differentiable, non-linear and multi-modal objective functions. Many advances have been made recently in differential evolution, from theory to applications. This book comprises contributions which include theoretical developments in DE, performance comparisons of DE, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications. The book is useful for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research and natural computing.

Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables. The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast. Packed with illustrations, computer code, new insights, and practical advice, this volume explores DE in both principle and practice. It is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.

Individuals and enterprises are looking for optimal solutions for the problems they face. Most problems can be expressed in mathematical terms, and so the methods of optimization render a significant aid. This book details the latest achievements in optimization. It offers comprehensive coverage on Differential Evolution, presenting revolutionary ideas in population-based optimization and shows the best known metaheuristics through the prism of Differential Evolution.

Differential evolution has proven itself a very simple while very powerful stochastic global optimizer. It has been applied to solve problems in many scientific and engineering fields. This book focuses on applications of differential evolution in electromagnetics to showcase its achievement and capability in solving synthesis and design problems in electromagnetics.Topics covered in this book include:• A comprehensive up-to-date literature survey on differential evolution• A systematic description of differential evolution• A topical review on applications of differential evolution in electromagnetics• Five new application examplesThis book is ideal for electromagnetic researchers and people in differential evolution community. It is also a valuable reference book for researchers and students in the optimization or electrical and electronic engineering field. In addition, managers and engineers in relevant fields will find it a helpful introductory guide.

Differential evolution is arguably one of the hottest topics in today's computational intelligence research. This book seeks to present a comprehensive study of the state of the art in this technology and also directions for future research. The fourteen chapters of this book have been written by leading experts in the area. The first seven chapters focus on algorithm design, while the last seven describe real-world applications. Chapter 1 introduces the basic differential evolution (DE) algorithm and presents a broad overview of the field. Chapter 2 presents a new, rotationally invariant DE algorithm. The role of self-adaptive control parameters in DE is investigated in Chapter 3. Chapters 4 and 5 address constrained optimization; the former develops suitable stopping conditions for the DE run, and the latter presents an improved DE algorithm for problems with very small feasible regions. A novel DE algorithm, based on the concept of "opposite" points, is the topic of Chapter 6. Chapter 7 provides a survey of multi-objective differential evolution algorithms. A review of the major application areas of differential evolution is presented in Chapter 8. Chapter 9 discusses the application of differential evolution in two important areas of applied electromagnetics. Chapters 10 and 11 focus on applications of hybrid DE algorithms to problems in power system optimization. Chapter 12 applies the DE algorithm to computer chess. The use of DE to solve a problem in bioprocess engineering is discussed in Chapter 13. Chapter 14 describes the application of hybrid differential evolution to a problem in control engineering.

This book is based on an International Conference on Trends in Theory and Practice of Nonlinear Differential Equations held at The University of Texas at Arlington. It aims to feature recent trends in theory and practice of nonlinear differential equations.

The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.

The aim of the book is to lay out the foundations and provide a detailed treatment of the subject. It will focus on two main elements in dual phase evolution: the relationship between dual phase evolution and other phase transition phenomena and the advantages of dual phase evolution in evolutionary computation and complex adaptive systems. The book will provide a coherent picture of dual phase evolution that encompasses these two elements and frameworks, methods and techniques to use this concept for problem solving.

Differential evolution is a very simple but very powerful stochastic optimizer. Since its inception, it has proved very efficient and robust in function optimization and has been applied to solve problems in many scientific and engineering fields. In Differential Evolution , Dr. Qing begins with an overview of optimization, followed by a state-of-the-art review of differential evolution, including its fundamentals and up-to-date advances. He goes on to explore the relationship between differential evolution strategies, intrinsic control parameters, non-intrinsic control parameters, and problem features through a parametric study. Findings and recommendations on the selection of strategies and intrinsic control parameter values are presented. Lastly, after an introductory review of reported applications in electrical and electronic engineering fields, different research groups demonstrate how the methods can be applied to such areas as: multicast routing, multisite mapping in grid environments, antenna arrays, analog electric circuit sizing, electricity markets, stochastic tracking in video sequences, and color quantization. Contains a systematic and comprehensive overview of differential evolution Reviews the latest differential evolution research Describes a comprehensive parametric study conducted over a large test bed Shows how methods can be practically applied to mobile communications grid computing circuits image processing power engineering Sample applications demonstrated by research groups in the United Kingdom, Australia, Italy, Turkey, China, and Eastern Europe Provides access to companion website with code examples for download Differential Evolution is ideal for application engineers, who can use the methods described to solve specific engineering problems. It is also a valuable reference for post-graduates and researchers working in evolutionary computation, design optimization and artificial intelligence. Researchers in the optimization field or engineers and managers involved in operations research will also find the book a helpful introduction to the topic.