The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization
Abstract
This paper describes the synthesis method of linear array geometry with minimum sidelobe level and null control using the particle swarm optimization (PSO) algorithm. The PSO algorithm is a newly discovered, high-performance evolutionary algorithm capable of solving general N-dimensional, linear and nonlinear optimization problems. Compared to other evolutionary methods such as genetic algorithms and simulated annealing, the PSO algorithm is much easier to understand and implement and requires the least of mathematical preprocessing. The array geometry synthesis is first formulated as an optimization problem with the goal of sidelobe level (SLL) suppression and/or null placement in certain directions, and then solved by the PSO algorithm for the optimum element locations. Three design examples are presented that illustrate the use of the PSO algorithm, and the optimization goal in each example is easily achieved. The results of the PSO algorithm are validated by comparing with results obtained using the quadratic programming method (QPM).
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Cites
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Particle swarm optimization: developments, applications and resources
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The particle swarm optimization algorithm: convergence analysis and parameter selection
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Particle swarm optimization in electromagnetics
Robinson, J., & Rahmat-Samii, Y. (2004) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2004.823969 · Cited by 2,265
Recent approaches to global optimization problems through Particle Swarm Optimization
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Electromagnetic Optimization by Genetic Algorithms
Rahmat‐Samii, Y., & Michielssen, E. (1999) · Microwave journal · Cited by 1,021
Particle Swarm Optimization Versus Genetic Algorithms for Phased Array Synthesis
Boeringer, D. W., & Werner, D. H. (2004) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2004.825102 · Cited by 945
Genetic algorithms in the design and optimization of antenna array patterns
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Use of intelligent-particle swarm optimization in electromagnetics
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Cited by
The Wind Driven Optimization Technique and its Application in Electromagnetics
Bayraktar, Z., Komurcu, M., Bossard, J. A., & Werner, D. H. (2013) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2013.2238654 · Cited by 323
A Review of Multibeam Phased Array Antennas as LEO Satellite Constellation Ground Station
He, G., Gao, X., Sun, L., & Zhang, R. (2021) · IEEE Access · doi:10.1109/access.2021.3124318 · Cited by 108
Metadata
- Authors
- Khodier, M. M., & Christodoulou, C. G.
- Venue
- IEEE Transactions on Antennas and Propagation
- Published
- 2005-08
- Volume
- 53
- Issue
- 8
- Pages
- 2674-2679
- ISSN
- 0018-926X (ISSN-L)
- OpenAlex
- W2122806537
- Cited by
- 686
- References
- 12
- FWCI
- 56.89
- Type
- journal article
- Language
- en
- Open access
- closed
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