A comparison of particle swarm optimization and genetic algorithms for a phased array synthesis problem

D. W. Boeringer, D. H. Werner

Research output: Contribution to journalConference article

30 Citations (Scopus)

Abstract

Particle swarm optimization is a recently invented high-performance optimizer that possesses several highly desirable attributes, including the fact that the basic algorithm is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but generally requires only a few lines of code. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for phased array synthesis of a far field sidelobe notch, using amplitude-only, phase-only, and complex tapering. The results show that some optimization scenarios are better suited to one method versus the other (i.e. particle swarm optimization performs better in some cases while genetic algorithms perform better in others), which implies that the two methods traverse the problem hyperspace differently. Although simple, the particle swarm optimizer shows good possibilities for electromagnetic optimization.

Original languageEnglish (US)
Pages (from-to)181-184
Number of pages4
JournalIEEE Antennas and Propagation Society, AP-S International Symposium (Digest)
Volume1
StatePublished - Sep 1 2003
Event2003 IEEE International Antennas and Propagation Symposium and USNC/CNC/URSI North American Radio Science Meeting - Columbus, OH, United States
Duration: Jun 22 2003Jun 27 2003

Fingerprint

Particle swarm optimization (PSO)
Genetic algorithms
Evolutionary algorithms

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

@article{ae91f5bb8c8540babf7746c57041376e,
title = "A comparison of particle swarm optimization and genetic algorithms for a phased array synthesis problem",
abstract = "Particle swarm optimization is a recently invented high-performance optimizer that possesses several highly desirable attributes, including the fact that the basic algorithm is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but generally requires only a few lines of code. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for phased array synthesis of a far field sidelobe notch, using amplitude-only, phase-only, and complex tapering. The results show that some optimization scenarios are better suited to one method versus the other (i.e. particle swarm optimization performs better in some cases while genetic algorithms perform better in others), which implies that the two methods traverse the problem hyperspace differently. Although simple, the particle swarm optimizer shows good possibilities for electromagnetic optimization.",
author = "Boeringer, {D. W.} and Werner, {D. H.}",
year = "2003",
month = "9",
day = "1",
language = "English (US)",
volume = "1",
pages = "181--184",
journal = "AP-S International Symposium (Digest) (IEEE Antennas and Propagation Society)",
issn = "0272-4693",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - A comparison of particle swarm optimization and genetic algorithms for a phased array synthesis problem

AU - Boeringer, D. W.

AU - Werner, D. H.

PY - 2003/9/1

Y1 - 2003/9/1

N2 - Particle swarm optimization is a recently invented high-performance optimizer that possesses several highly desirable attributes, including the fact that the basic algorithm is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but generally requires only a few lines of code. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for phased array synthesis of a far field sidelobe notch, using amplitude-only, phase-only, and complex tapering. The results show that some optimization scenarios are better suited to one method versus the other (i.e. particle swarm optimization performs better in some cases while genetic algorithms perform better in others), which implies that the two methods traverse the problem hyperspace differently. Although simple, the particle swarm optimizer shows good possibilities for electromagnetic optimization.

AB - Particle swarm optimization is a recently invented high-performance optimizer that possesses several highly desirable attributes, including the fact that the basic algorithm is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but generally requires only a few lines of code. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for phased array synthesis of a far field sidelobe notch, using amplitude-only, phase-only, and complex tapering. The results show that some optimization scenarios are better suited to one method versus the other (i.e. particle swarm optimization performs better in some cases while genetic algorithms perform better in others), which implies that the two methods traverse the problem hyperspace differently. Although simple, the particle swarm optimizer shows good possibilities for electromagnetic optimization.

UR - http://www.scopus.com/inward/record.url?scp=0042474337&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0042474337&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0042474337

VL - 1

SP - 181

EP - 184

JO - AP-S International Symposium (Digest) (IEEE Antennas and Propagation Society)

JF - AP-S International Symposium (Digest) (IEEE Antennas and Propagation Society)

SN - 0272-4693

ER -