The computationally efficient solution of multiobjective optimization problems (MOPs) arising in the design of modern electromagnetic (EM) systems, such as mm-wave automotive radar antennas, is addressed. Toward this end, a novel system-by-design (SbD) method is developed to effectively explore the solution space and to provide the decision-maker with a set of optimal tradeoff solutions minimizing multiple and (generally) contrasting objectives. The proposed MO-SbD method proves a high computational efficiency, with a remarkable time-saving with respect to a competitive state-of-the-art MOP solution strategy, thanks to the “smart” integration of evolutionary-inspired concepts and operators with artificial intelligence (AI) and machine learning (ML) techniques. Representative numerical results are reported to provide the interested users with useful insights and guidelines on the proposed optimization method as well as to assess its effectiveness in designing mm-wave automotive radar antennas.

Multiobjective System-by-Design for mm-Wave Automotive Radar Antennas / Rosatti, Pietro; Salucci, Marco; Poli, Lorenzo; Massa, Andrea. - In: IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION. - ISSN 0018-926X. - STAMPA. - 2023, 71:4(2023), pp. 2958-2973. [10.1109/tap.2022.3233432]

Multiobjective System-by-Design for mm-Wave Automotive Radar Antennas

Pietro Rosatti;Marco Salucci;Lorenzo Poli;Andrea Massa
2023-01-01

Abstract

The computationally efficient solution of multiobjective optimization problems (MOPs) arising in the design of modern electromagnetic (EM) systems, such as mm-wave automotive radar antennas, is addressed. Toward this end, a novel system-by-design (SbD) method is developed to effectively explore the solution space and to provide the decision-maker with a set of optimal tradeoff solutions minimizing multiple and (generally) contrasting objectives. The proposed MO-SbD method proves a high computational efficiency, with a remarkable time-saving with respect to a competitive state-of-the-art MOP solution strategy, thanks to the “smart” integration of evolutionary-inspired concepts and operators with artificial intelligence (AI) and machine learning (ML) techniques. Representative numerical results are reported to provide the interested users with useful insights and guidelines on the proposed optimization method as well as to assess its effectiveness in designing mm-wave automotive radar antennas.
2023
4
Rosatti, Pietro; Salucci, Marco; Poli, Lorenzo; Massa, Andrea
Multiobjective System-by-Design for mm-Wave Automotive Radar Antennas / Rosatti, Pietro; Salucci, Marco; Poli, Lorenzo; Massa, Andrea. - In: IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION. - ISSN 0018-926X. - STAMPA. - 2023, 71:4(2023), pp. 2958-2973. [10.1109/tap.2022.3233432]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/378388
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