Novel Methodology for Designing Antenna Arrays with Reduced Number of Elements Based on Differential Evolution with Ranking-Based Mutation Operators

Novel Methodology for Designing Antenna Arrays with Reduced Number of Elements Based on Differential Evolution with Ranking-Based Mutation Operators. Goudos, S., K., Boursianis, A., Siakavara, K., Kantartzis, N., & Psannis, K., E. In 2018 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2018, 2019.
doi abstract bibtex

This paper addresses the problem of designing linear arrays with reduced number of elements and design constraints including the minimum and maximum distance between two adjacent elements. The proposed design method is based on Differential Evolution (DE) with ranking-based mutation operators. In this case, some of the vectors in the mutation operator are probabilistically selected according to their ranking in the current population instead of recurring to a random selection as in the original DE. A comparative study among different DE algorithms is carried out to define the optimum element geometry and set of excitations that produce the desired pattern with a reduced number of elements. The results show that DE enhanced with ranking-based mutation operator is highly efficient and outperforms the common DE variants.

@inproceedings{
 title = {Novel Methodology for Designing Antenna Arrays with Reduced Number of Elements Based on Differential Evolution with Ranking-Based Mutation Operators},
 type = {inproceedings},
 year = {2019},
 id = {2caa4d5a-cb3a-3407-9bbe-741dfc596ec5},
 created = {2020-02-29T16:57:43.831Z},
 file_attached = {false},
 profile_id = {c69aa657-d754-373c-91b7-64154b7d5d91},
 last_modified = {2023-02-11T18:54:04.347Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Goudos2019b},
 private_publication = {false},
 abstract = {This paper addresses the problem of designing linear arrays with reduced number of elements and design constraints including the minimum and maximum distance between two adjacent elements. The proposed design method is based on Differential Evolution (DE) with ranking-based mutation operators. In this case, some of the vectors in the mutation operator are probabilistically selected according to their ranking in the current population instead of recurring to a random selection as in the original DE. A comparative study among different DE algorithms is carried out to define the optimum element geometry and set of excitations that produce the desired pattern with a reduced number of elements. The results show that DE enhanced with ranking-based mutation operator is highly efficient and outperforms the common DE variants.},
 bibtype = {inproceedings},
 author = {Goudos, Sotirios K. and Boursianis, Achilles and Siakavara, Katherine and Kantartzis, Nikolaos and Psannis, Kostas E.},
 doi = {10.23919/ACESS.2018.8669304},
 booktitle = {2018 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2018}
}

Downloads: 0