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Multidimensional particle swarm optimization for machine learning and pattern recognition
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...
Autores principales: | , , |
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Lenguaje: | eng |
Publicado: |
Springer
2013
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-3-642-37846-1 http://cds.cern.ch/record/1620167 |
Sumario: | 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 chal |
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