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

D. W. Boeringer, Douglas Henry Werner

Research output: Contribution to journalArticle

31 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
JournalAP-S International Symposium (Digest) (IEEE Antennas and Propagation Society)
Volume1
StatePublished - 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, {Douglas Henry}",
year = "2003",
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, Douglas Henry

PY - 2003

Y1 - 2003

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 - Article

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 -