This book explores multidimensional particle swarm optimization, a technique developed by the authors and presented in a well-defined algorithmic approach. All featured applications are supported with fully documented source code as well as sample datasets.

For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach.

After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterizedby strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets.

The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications.

Presents a new optimization technique Characterized by an emphasis on solving real-world problems Supported by source code and datasets Includes supplementary material: sn.pub/extras

Autorentext

Prof. Serkan Kiranyaz worked as a researcher in Nokia Research Center and later in Nokia Mobile Phones in Tampere, Finland. He received his Ph.D. in 2005 and qualified as a Docent in 2007 from the Inst. of Signal Processing of Tampere Univ. of Technology, where he is currently a professor. He is the architect and principal developer of the ongoing content-based multimedia indexing and retrieval framework, MUVIS. His interests include swarm intelligence, stochastic optimization techniques, evolutionary neural networks, content-based multimedia indexing, browsing and retrieval algorithms, audio analysis and audio-based multimedia retrieval, object extraction, and biomedical signal analysis.

Dr. Turker Ince received his Ph.D. from the Univ. of Massachusetts, Amherst, in 2001 in electrical engineering. He was a research assistant in the Microwave Remote Sensing Laboratory of UMass-Amherst from 1996 to 2001, and he worked as a design engineer at Aware, Inc., Boston from 2001 to 2004, and at Texas Instruments, Inc., Dallas from 2004 to 2006. He is currently an associate professor in the Dept. of Electrical and Electronics Engineering of Izmir University of Economics, Turkey. He teaches and conducts research in the areas of remote sensing, radar systems and signal processing, neural networks, and evolutionary optimization.

Prof. Moncef Gabbouj received his Ph.D. from Purdue University in 1989 in electrical engineering. He is an Academy Professor with the Academy of Finland (2011-2015), and a Professor in the Dept. of Signal Processing of Tampere University of Technology, Finland. He is a Fellow of the IEEE, he has chaired many research and education projects and technical committees, and he has edited related journal issues. His interests include multimedia content-based analysis, indexing and retrieval, swarm optimization, nonlinear signal and image processing and analysis, voice conversion, and video processing and coding. He has coauthoredover 500 publications.

Inhalt
Chap. 1 Introduction.- Chap. 2 Optimization Techniques.- Chap. 3 Particle Swarm Optimization.- Chap. 4 Multidimensional Particle Swarm Optimization.- Chap. 5 Improving Global Convergence.- Chap. 6 Dynamic Data Clustering.- Chap. 7 Evolutionary Artificial Neural Networks.- Chap. 8 Personalized ECG Classification.- Chap. 9 Image Classification Through a Collective Network of Binary Classifiers.- Chap. 10 Evolutionary Feature Synthesis for Image Retrieval.