Adaptive Differential Evolution: A Robust Approach to Multimodal Problem Optimization

✍ Scribed by Jingqiao Zhang, Arthur C. Sanderson (auth.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2009
• Tongue: English
• Leaves: 171
• Series: Adaptation Learning and Optimization 1
• Edition: 1
• Category: Library

✦ Synopsis

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

✦ Table of Contents

Front Matter....Pages -
Introduction....Pages 1-4
Related Work and Background....Pages 5-13
Theoretical Analysis of Differential Evolution....Pages 15-38
Parameter Adaptive Differential Evolution....Pages 39-82
Surrogate Model-Based Differential Evolution....Pages 83-93
Adaptive Multi-objective Differential Evolution....Pages 95-113
Application to Winner Determination Problems in Combinatorial Auctions....Pages 115-125
Application to Flight Planning in Air Traffic Control Systems....Pages 127-134
Application to the TPM Optimization in Credit Decision Making....Pages 135-145
Conclusions and Future Work....Pages 147-150
Back Matter....Pages -

✦ Subjects

Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics); Operations Research/Decision Theory; Applications of Mathematics