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Adaptive Differential Evolution

A Robust Approach to Multimodal Problem Optimization

  • Jingqiao Zhang
  • Arthur C. Sanderson

Part of the Adaptation Learning and Optimization book series (ALO, volume 1)

Table of contents

  1. Front Matter
  2. Jingqiao Zhang, Arthur C. Sanderson
    Pages 1-4
  3. Jingqiao Zhang, Arthur C. Sanderson
    Pages 5-13
  4. Jingqiao Zhang, Arthur C. Sanderson
    Pages 15-38
  5. Jingqiao Zhang, Arthur C. Sanderson
    Pages 39-82
  6. Jingqiao Zhang, Arthur C. Sanderson
    Pages 83-93
  7. Jingqiao Zhang, Arthur C. Sanderson
    Pages 95-113
  8. Jingqiao Zhang, Arthur C. Sanderson
    Pages 115-125
  9. Jingqiao Zhang, Arthur C. Sanderson
    Pages 127-134
  10. Jingqiao Zhang, Arthur C. Sanderson
    Pages 135-145
  11. Jingqiao Zhang, Arthur C. Sanderson
    Pages 147-150
  12. Back Matter

About this book

Introduction

Optimization problems are ubiquitous in academic research and real-world applications wherever such resources as space, time and cost are limited. Researchers and practitioners need to solve problems fundamental to their daily work which, however, may show a variety of challenging characteristics such as discontinuity, nonlinearity, nonconvexity, and multimodality. It is expected that solving a complex optimization problem itself should easy to use, reliable and efficient to achieve satisfactory solutions.

Differential evolution is a recent branch of evolutionary algorithms that is capable of addressing a wide set of complex optimization problems in a relatively uniform and conceptually simple manner. For better performance, the control parameters of differential evolution need to be set appropriately as they have different effects on evolutionary search behaviours for various problems or at different optimization stages of a single problem. The fundamental theme of the book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. Topics covered in this book include:

  • Theoretical analysis of differential evolution and its control parameters
  • Algorithmic design and comparative analysis of parameter adaptive schemes
  • Scalability analysis of adaptive differential evolution
  • Adaptive differential evolution for multi-objective optimization
  • Incorporation of surrogate model for computationally expensive optimization
  • Application to winner determination in combinatorial auctions of E-Commerce
  • Application to flight route planning in Air Traffic Management
  • Application to transition probability matrix optimization in credit-decision making

Keywords

Evolutionary optimization Racter adaptive parameter control algorithms combinatorial auction differential evolution evolution evolutionary algorithm expensive optimization flight-route planning multi-objective optimization optimization transition probability matrix optimization

Authors and affiliations

  • Jingqiao Zhang
    • 1
  • Arthur C. Sanderson
    • 1
  1. 1.Electrical, Computer and System Engineering DepartmentRensselaer Polytechnic InstituteTroyUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-01527-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-01526-7
  • Online ISBN 978-3-642-01527-4
  • Series Print ISSN 1867-4534
  • Series Online ISSN 1867-4542
  • Buy this book on publisher's site