Evolving Antennas From Building Blocks Using Evolutionary Algorithms

POSTER

Abstract

Evolutionary algorithms use concepts of biological evolution in order to find solutions to difficult optimization problems. This work presents a genetic algorithm to evolve arbitrary antenna designs towards the goal of either matching a desired radiation pattern or maximizing sensitivity in user-specified directions. The algorithm constructs antennas by combining geometric shapes like building blocks. We explore the results of evolutions towards both fitness metrics in single-frequency (300 MHz) and broadband (200 MHz to 800 MHz) applications. This work was initially inspired from astrophysics experiments that are reliant on sensitive detectors, and future work will include metrics from astrophysical simulation software as the measure of fitness.

*The research was carried out at the Jet Propulsion Laboratory, California Institute ofTechnology, under a contract with the National Aeronautics and Space Administration(80NM0018D0004)

Publication: Julie Rolla, Bryan Reynolds, Dylan Wells, Jacob Weiler, Amy Connolly, and Ryan Debolt. Design of Antennas from Primitive Shapes Using Genetic Algorithms. The Interplanetary Network Progress Report, Volume 42-237, pp. 1-47, May 15, 2024. https://ipnpr.jpl.nasa.gov/progress_report/42-237/42-237D.pdf

Julie Rolla, Bryan Reynolds, Jacob Weiler, Amy Connolly, Ryan Debolt, Alex Machtay, Ben Sipe, and Dylan Wells. Design of 3D Antenna Geometries Using Genetic Algorithms. The Interplanetary Network Progress Report, Volume 42-234, pp. 1-26, August 15, 2023. https://ipnpr.jpl.nasa.gov/progress_report/42-234/42-234A.pdf

Presenters

  • Dylan Wells

    • Ohio State University

Authors

  • Dylan Wells

    • Ohio State University

  • Julie Rolla

    • Ohio State University

  • Jacob Weiler

    • Ohio State University

  • Amy L Connolly

    • The Ohio State University

  • Bryan Reynolds

    • Ohio State Univ - Columbus