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Advances in Differential Evolution

  • Book
  • © 2008

Overview

Editors:
  1. Uday K. Chakraborty
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  • This book seeks to present a comprehensive study of the state of the art in differential evolution

Part of the book series: Studies in Computational Intelligence (SCI, volume 143)

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Table of contents (14 chapters)

  1. Front Matter

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  2. Differential Evolution Research – Trends and Open Questions

    • Rainer Storn
    Pages 1-31

  3. Eliminating Drift Bias from the Differential Evolution Algorithm

    • Kenneth V. Price
    Pages 33-88

  4. An Analysis of the Control Parameters’ Adaptation in DE

    • Janez Brest, Aleš Zamuda, Borko Bošković, Sašo Greiner, Viljem Žumer
    Pages 89-110

  5. Stopping Criteria for Differential Evolution in Constrained Single-Objective Optimization

    • Karin Zielinski, Rainer Laur
    Pages 111-138

  6. Constrained Optimization by ε Constrained Differential Evolution with Dynamic ε-Level Control

    • Tetsuyuki Takahama, Setsuko Sakai
    Pages 139-154

  7. Opposition-Based Differential Evolution

    • Shahryar Rahnamayan, Hamid R. Tizhoosh, Magdy M. A. Salama
    Pages 155-171

  8. Multi-objective Optimization Using Differential Evolution: A Survey of the State-of-the-Art

    • Efrén Mezura-Montes, Margarita Reyes-Sierra, Carlos A. Coello Coello
    Pages 173-196

  9. A Review of Major Application Areas of Differential Evolution

    • V. P. Plagianakos, D. K. Tasoulis, M. N. Vrahatis
    Pages 197-238

  10. The Differential Evolution Algorithm as Applied to Array Antennas and Imaging

    • A. Massa, M. Pastorino, A. Randazzo
    Pages 239-255

  11. Applications of Differential Evolution in Power System Optimization

    • L. Lakshminarasimman, S. Subramanian
    Pages 257-273

  12. Self-adaptive Differential Evolution Using Chaotic Local Search for Solving Power Economic Dispatch with Nonsmooth Fuel Cost Function

    • Leandro dos Santos Coelho, Viviana Cocco Mariani
    Pages 275-286

  13. An Adaptive Differential Evolution Algorithm with Opposition-Based Mechanisms, Applied to the Tuning of a Chess Program

    • Borko Bošković, Sašo Greiner, Janez Brest, Aleš Zamuda, Viljem Žumer
    Pages 287-298

  14. Differential Evolution for the Offline and Online Optimization of Fed-Batch Fermentation Processes

    • Rui Mendes, Isabel Rocha, José P. Pinto, Eugénio C. Ferreira, Miguel Rocha
    Pages 299-317

  15. Worst Case Analysis of Control Law for Re-entry Vehicles Using Hybrid Differential Evolution

    • P. P. Menon, D. G. Bates, I. Postlethwaite, A. Marcos, V. Fernandez, S. Bennani
    Pages 319-333

  16. Back Matter

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About this book

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.

Reviews

From the reviews: “The book presents the state of the art in differential evolution in 14 chapters, written by different authors. … Since the differential evolution is a special topic within optimization, the book will be most interesting for the reader who is interested in optimizing his or her own special scientific problem. It introduces the theoretical and programming-oriented aspects of differential evolution.” (Gudula Runger, ACM Computing Reviews, March, 2010)

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