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A Multi-resolution GA-PSO Layered Encoding Cascade Optimization Model

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Innovations in Swarm Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 248))

Abstract

Many real-world problems involve optimization of multi-resolution parameters. In optimization problems, the higher the resolution, the larger the search space, and resolution affects the accuracy and performance of an optimization model. This article presents a genetic algorithm and particle swarm based cascade multi-resolution optimization model, and it is known as GA-PSO LECO. GA and PSO are combined in this research to integrate random as well as directional search to promote global exploration and local exploitation of solutions. The model is developed using the layered encoding representation structure, and is evaluated using two parameter optimization problems, i.e., the Tennessee Eastman chemical process optimization and the MMIC amplifier design interactive optimization.

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Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Science Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia

    Siew Chin Neoh, Arjuna Marzuki, Chee Peng Lim & Zalina Abdul Aziz

  2. School of Industrial Technology, University of Science Malaysia, 11800, Minden, Penang, Malaysia

    Norhashimah Morad

Authors
  1. Siew Chin Neoh
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  2. Norhashimah Morad
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  3. Arjuna Marzuki
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  4. Chee Peng Lim
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  5. Zalina Abdul Aziz
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Editor information

Editors and Affiliations

  1. University of South Australia, Adelaide, Australia

    Chee Peng Lim  & Lakhmi C. Jain  & 

  2. Soft Computing Laboratory, Department of computer Science, Yonsei University, 120-749, Seoul, Korea

    Satchidananda Dehuri

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© 2009 Springer-Verlag Berlin Heidelberg

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Neoh, S.C., Morad, N., Marzuki, A., Lim, C.P., Aziz, Z.A. (2009). A Multi-resolution GA-PSO Layered Encoding Cascade Optimization Model. In: Lim, C.P., Jain, L.C., Dehuri, S. (eds) Innovations in Swarm Intelligence. Studies in Computational Intelligence, vol 248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04225-6_7

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  • DOI: https://doi.org/10.1007/978-3-642-04225-6_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04224-9

  • Online ISBN: 978-3-642-04225-6

  • eBook Packages: EngineeringEngineering (R0)

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