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Constrained multi-objective antenna design optimization using surrogates

Prashant Singh (UGent) , Marco Rossi (UGent) , Ivo Couckuyt (UGent) , Dirk Deschrijver (UGent) , Hendrik Rogier (UGent) and Tom Dhaene (UGent)
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Abstract
A novel surrogate-based constrained multi-objective optimization algorithm for simulation-driven optimization is proposed. The evolutionary algorithms usually applied in antenna design optimization typically require a large number of objective function evaluations to converge. The efficient constrained multiobjective optimization algorithm described in this paper identifies Pareto-optimal solutions satisfying the required constraints using very few function evaluations. This leads to substantial savings in time and drastically reduces the time to market for expensive antenna design optimization problems. The efficiency of the approach is demonstrated on the design of an L1-band GPS antenna. The algorithm automatically optimizes the antenna geometry, parametrized by 5 design variables with performance constraints on three objectives. The results are compared with well-established multiobjective optimization evolutionary algorithms.
Keywords
IBCN, antenna optimization, Bayesian optimization, multiobjective optimization, model-based optimization, surrogate-based optimization

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Citation

Please use this url to cite or link to this publication:

Chicago
Singh, Prashant, Marco Rossi, Ivo Couckuyt, Dirk Deschrijver, Hendrik Rogier, and Tom Dhaene. 2017. “Constrained Multi-objective Antenna Design Optimization Using Surrogates.” International Journal of Numerical Modelling-electronic Networks Devices and Fields 30 (6).
APA
Singh, Prashant, Rossi, M., Couckuyt, I., Deschrijver, D., Rogier, H., & Dhaene, T. (2017). Constrained multi-objective antenna design optimization using surrogates. INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS, 30(6).
Vancouver
1.
Singh P, Rossi M, Couckuyt I, Deschrijver D, Rogier H, Dhaene T. Constrained multi-objective antenna design optimization using surrogates. INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS. 2017;30(6).
MLA
Singh, Prashant, Marco Rossi, Ivo Couckuyt, et al. “Constrained Multi-objective Antenna Design Optimization Using Surrogates.” INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 30.6 (2017): n. pag. Print.
@article{8537108,
  abstract     = {A novel surrogate-based constrained multi-objective optimization algorithm for simulation-driven optimization is proposed. The evolutionary algorithms usually applied in antenna design optimization typically require a large number of objective function evaluations to converge. The efficient constrained multiobjective optimization algorithm described in this paper identifies Pareto-optimal solutions satisfying the required constraints using very few function evaluations. This leads to substantial savings in time and drastically reduces the time to market for expensive antenna design optimization problems. The efficiency of the approach is demonstrated on the design of an L1-band GPS antenna. The algorithm automatically optimizes the antenna geometry, parametrized by 5 design variables with performance constraints on three objectives. The results are compared with well-established multiobjective optimization evolutionary algorithms.},
  articleno    = {e2248},
  author       = {Singh, Prashant and Rossi, Marco and Couckuyt, Ivo and Deschrijver, Dirk and Rogier, Hendrik and Dhaene, Tom},
  issn         = {0894-3370},
  journal      = {INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS},
  keyword      = {IBCN,antenna optimization,Bayesian optimization,multiobjective optimization,model-based optimization,surrogate-based optimization},
  language     = {eng},
  number       = {6},
  title        = {Constrained multi-objective antenna design optimization using surrogates},
  url          = {http://dx.doi.org/10.1002/jnm.2248},
  volume       = {30},
  year         = {2017},
}

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