Multi-Objective Optimization of RF Circuit Blocks via Surrogate Models and NBI and SPEA2 Methods

  • Luciano De Tommasi
  • Theo G. J. Beelen
  • Marcel F. Sevat
  • Joost Rommes
  • E. Jan W. ter Maten
Conference paper
Part of the Mathematics in Industry book series (MATHINDUSTRY, volume 17)


Multi-objective optimization techniques can be categorized globally into deterministic and evolutionary methods. Examples of such methods are the Normal Boundary Intersection (NBI) method and the Strength Pareto Evolutionary Algorithm (SPEA2), respectively. With both methods one explores trade-offs between conflicting performances. Surrogate models can replace expensive circuit simulations so enabling faster computation of circuit performances. As surrogate models of behavioral parameters and performance outcomes, we consider look-up tables with interpolation and Neural Network models.


Neural Network Model Pareto Front Global Optimization Problem Reverse Modeling Strength Pareto Evolutionary Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Luciano De Tommasi
    • 1
  • Theo G. J. Beelen
    • 2
  • Marcel F. Sevat
    • 3
  • Joost Rommes
    • 2
  • E. Jan W. ter Maten
    • 4
    • 5
  1. 1.United Technologies Research CenterCorkIreland
  2. 2.NXP Semiconductors, Central R&DEindhovenThe Netherlands
  3. 3.LPDHeerlenThe Netherlands
  4. 4.Department of Mathematics and Computer ScienceEindhoven University of Technology, CASAEindhovenThe Netherlands
  5. 5.Bergische Universität Wuppertal, FB C, AMNAWuppertalGermany

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