Rapid multi-objective optimization of antennas using nested kriging surrogates and single-fidelity EM simulation models. Issue 4 (19th December 2019)

• Record Type: Journal Article
• Authors: Koziel, Slawomir; Pietrenko-Dabrowska, Anna
• Is Part Of: Engineering computations. Volume 37:Issue 4(2020)
• Page Start: 1491
• Page End: 1512
• Publication Date: 2019-12-19
• Subjects: Antenna design -- Multi-objective design -- Surrogate modeling -- Simulation-driven design -- Kriging interpolation -- Design trade-offs; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-05-2019-0200
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: This study aims to propose a computationally efficient framework for multi-objective optimization (MO) of antennas involving nested kriging modeling technology. The technique is demonstrated through a two-objective optimization of a planar Yagi antenna and three-objective design of a compact wideband antenna. Design/methodology/approach: The keystone of the proposed approach is the usage of recently introduced nested kriging modeling for identifying the design space region containing the Pareto front and constructing fast surrogate model for the MO algorithm. Surrogate-assisted design refinement is applied to improve the accuracy of Pareto set determination. Consequently, the Pareto set is obtained cost-efficiently, even though the optimization process uses solely high-fidelity electromagnetic (EM) analysis. Findings: The optimization cost is dramatically reduced for the proposed framework as compared to other state-of-the-art frameworks. The initial Pareto set is identified more precisely (its span is wider and of better quality), which is a result of a considerably smaller domain of the nested kriging model and better predictive power of the surrogate. Research limitations/implications: The proposed technique can be generalized to accommodate low- and high-fidelity EM simulations in a straightforward manner. The future work will incorporate variable-fidelity simulations to further reduce the cost of the training data acquisition. Originality/value: The fast MO optimization procedure with the use of the nested kriging modeling technology for approximation of the Pareto set has been proposed and its superiority over state-of-the-art surrogate-assisted procedures has been proved. To the best of the authors' knowledge, this approach to multi-objective antenna optimization is novel and enables obtaining optimal designs cost-effectively even in relatively high-dimensional spaces (considering typical antenna design setups) within wide parameter ranges.