Multi-Objective Lazy Ant Colony Optimization for Frequency Selective Surface Design

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Abstract

Recently, 3D Frequency Selective Surface (FSS) designs have become popular due to their enhanced performance at oblique incidence angles as compared to planar designs. While planar FSS design has been successfully performed using discrete nature-inspired evolutionary optimization algorithms such as the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), these algorithms are not suited for the problem of 3D FSS design, due to the generation of non-contiguous structures, making manufacturing difficult. One alternative to these algorithms is the Ant Colony Optimization (ACO) technique, which guarantees contiguous segments are generated. However, traditional ACO methods require meanders to continue until they are trapped, potentially generating a PEC unit cell. In this paper, a Multi-Objective Lazy Ant Colony Optimization (MOLACO) algorithm will be applied to the problem of FSS design. The introduction of lazy ants allows exploration of solutions previously inaccessible by traditional implementations of ACO. Simulated results will show that it is well suited to the problem of 3D FSS design.

Original languageEnglish (US)
Title of host publication2018 IEEE Antennas and Propagation Society International Symposium and USNC/URSI National Radio Science Meeting, APSURSI 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2035-2036
Number of pages2
ISBN (Electronic)9781538671023
DOIs
StatePublished - Jan 1 2018
Event2018 IEEE Antennas and Propagation Society International Symposium and USNC/URSI National Radio Science Meeting, APSURSI 2018 - Boston, United States
Duration: Jul 8 2018Jul 13 2018

Publication series

Name2018 IEEE Antennas and Propagation Society International Symposium and USNC/URSI National Radio Science Meeting, APSURSI 2018 - Proceedings

Conference

Conference2018 IEEE Antennas and Propagation Society International Symposium and USNC/URSI National Radio Science Meeting, APSURSI 2018
CountryUnited States
CityBoston
Period7/8/187/13/18

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Instrumentation
  • Radiation

Cite this

Zhu, D. Z., Werner, P. L., & Werner, D. H. (2018). Multi-Objective Lazy Ant Colony Optimization for Frequency Selective Surface Design. In 2018 IEEE Antennas and Propagation Society International Symposium and USNC/URSI National Radio Science Meeting, APSURSI 2018 - Proceedings (pp. 2035-2036). [8609246] (2018 IEEE Antennas and Propagation Society International Symposium and USNC/URSI National Radio Science Meeting, APSURSI 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APUSNCURSINRSM.2018.8609246