mm-Wave Nonlinear IC and Complex Antenna Synthesis: Handling High Dimensionality

  • Bo LiuEmail author
  • Georges Gielen
  • Francisco V. Fernández
Part of the Studies in Computational Intelligence book series (SCI, volume 501)


Chapter 10 focuses on the cutting-edge problem in surrogate model assisted evolutionary algorithms: handling of high dimensionality. Two state-of-the-art techniques, dimension reduction and surrogate model-aware evolutionary search mechanism are introduced. The practical examples are the synthesis of mm-wave nonlinear integrated circuits and complex antennas.


Particle Swarm Optimization Original Space Power Gain Dimension Reduction Method Training Data Point 
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.


  1. 1.
    Liu B, Zhang Q, Gielen G (2013b) A gaussian process surrogate model assisted evolutionary algorithm for medium scale expensive black box optimization problems. IEEE Trans Evol Comput (To be published)Google Scholar
  2. 2.
    Lim D, Jin Y, Ong Y, Sendhoff B (2010) Generalizing surrogate-assisted evolutionary computation. IEEE Trans Evol Comput 14(3):329–355CrossRefGoogle Scholar
  3. 3.
    Emmerich M, Giannakoglou K, Naujoks B (2006) Single-and multiobjective evolutionary optimization assisted by gaussian random field metamodels. IEEE Trans Evol Comput 10(4):421–439CrossRefGoogle Scholar
  4. 4.
    Zhou Z, Ong Y, Nair P, Keane A, Lum K (2007) Combining global and local surrogate models to accelerate evolutionary optimization. IEEE Trans Syst Man Cybern Part C Appl Rev 37(1):66–76CrossRefGoogle Scholar
  5. 5.
    Jones D (2001) A taxonomy of global optimization methods based on response surfaces. J Global Optim 21(4):345–383MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Jin Y (2005) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput Fusion Found Methodologies Appl 9(1):3–12Google Scholar
  7. 7.
    Gorissen D, Couckuyt I, Demeester P, Dhaene T, Crombecq K (2010) A surrogate modeling and adaptive sampling toolbox for computer based design. J Mach Learn Res 11:2051–2055Google Scholar
  8. 8.
    Van Der Maaten L, Postma E, Van Den Herik H (2008) Dimensionality reduction: a comparative review. Published online 71(January), pp 2596–2603Google Scholar
  9. 9.
    Sammon J Jr (1969) A nonlinear mapping for data structure analysis. IEEE Trans Comput 100(5):401–409CrossRefGoogle Scholar
  10. 10.
    Henderson P (2007) Sammon mapping. Published online, http://wwwhomepagesinfedacuk pp 1–5
  11. 11.
    Avriel M (2003) Nonlinear programming: analysis and methods. Dover Publishers, New York, USAGoogle Scholar
  12. 12.
    Stein M (1987) Large sample properties of simulations using Latin hypercube sampling. Technometrics 29(2):143–151Google Scholar
  13. 13.
    Jones D, Schonlau M, Welch W (1998) Efficient global optimization of expensive black-box functions. J Global Optim 13(4):455–492MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Zhang Q, Liu W, Tsang E, Virginas B (2010) Expensive multiobjective optimization by MOEA/D with gaussian process model. IEEE Trans Evol Comput 14(3):456–474Google Scholar
  15. 15.
    Eiben A, Bäck T (1997) Empirical investigation of multiparent recombination operators in evolution strategies. Evol Comput 5(3):347–365CrossRefGoogle Scholar
  16. 16.
    Suganthan P, Hansen N, Liang J, Deb K, Chen Y, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Nanyang Technological University, Singapore, Technical Report 2005005Google Scholar
  17. 17.
    Price K, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global optimization. Springer-Verlag, New YorkGoogle Scholar
  18. 18.
    IEEE (2010) WiGig, MAC and specification, PHY v1. 0Google Scholar
  19. 19.
    IEEE 80215 Working Group (2009) Wireless PAN task group 3c. millimeter wave alternative PHY.
  20. 20.
    Agilent (2013) Agilent Technology homepage.
  21. 21.
    Vandenbosch G, Van de Capelle A (1992) Mixed-potential integral expression formulation of the electric field in a stratified dielectric medium-application to the case of a probe current source. IEEE Trans Antennas Propag 40(7):806–817CrossRefGoogle Scholar
  22. 22.
    Demuynck F, Vandenbosch G, Van de Capelle A (1998) The expansion wave concept. I. efficient calculation of spatial green’s functions in a stratified dielectric medium. IEEE Trans Antennas Propag 46(3):397–406Google Scholar
  23. 23.
    Schols Y, Vandenbosch G (2007) Separation of horizontal and vertical dependencies in a surface/volume integral equation approach to model quasi 3-D structures in multilayered media. IEEE Trans Antennas Propag 55(4):1086–1094MathSciNetCrossRefGoogle Scholar
  24. 24.
    Ma Z, Vandenbosch G (2012) Comparison of weighted sum fitness functions for PSO optimization of wideband medium-gain antennas. Radioengineering 21(1):504–511Google Scholar
  25. 25.
    Vlasits T, Korolkiewicz E, Sambell A, Robinson B (1996) Performance of a cross-aperture coupled single feed circularly polarised patch antenna. Electron Lett 32(7):612–613CrossRefGoogle Scholar
  26. 26.
    Lin J, Wu H, Su Y, Gao L, Sugavanam A, Brewer J et al (2007) Communication using antennas fabricated in silicon integrated circuits. IEEE J Solid-State Circuits 42(8):1678–1687CrossRefGoogle Scholar
  27. 27.
    Kim K, Floyd B, Mehta J, Yoon H, Hung C, Bravo D, Dickson T, Guo X, Li R, Trichy N et al (2005) On-chip antennas in silicon ICs and their application. IEEE Trans Electron Devices 52(7):1312–1323CrossRefGoogle Scholar
  28. 28.
    Drost R, Hopkins R, Ho R, Sutherland I (2004) Proximity communication. IEEE J Solid-State Circuits 39(9):1529–1535CrossRefGoogle Scholar
  29. 29.
    Volski V, Delmotte P, Vandenbosch G (2004) Compact low-cost 4 elements microstrip antenna array for WLAN. In: Proceedings of 7th european conference on wireless technology, pp 277–280Google Scholar
  30. 30.
    Gao J, Li H, Jiao Y (2009) Modified differential evolution for the integer programming problems. In: Proceedings of international conference on artificial intelligence and computational intelligence, vol 1. pp 213–219Google Scholar
  31. 31.
    Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, New York, USAGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Bo Liu
    • 1
    Email author
  • Georges Gielen
    • 2
  • Francisco V. Fernández
    • 3
  1. 1.Department of ComputingGlyndwr UniversityWrexham, WalesUK
  2. 2.Department of Elektrotechniek ESAT-MICASKatholieke Universiteit LeuvenLeuvenBelgium
  3. 3.IMSE-CNMUniversidad de Sevilla and CSICSevillaSpain

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