Advances in Particle Swarm Optimization for Antenna Designs: Real-Number, Binary, Single-Objective and Multiobjective Implementations
The particle swarm optimization (PSO) is a recently developed evolutionary algorithm (EA) based on the swarm behavior in the nature. This paper presents recent advances in applying a versatile PSO engine to real-number, binary, single-objective and multiobjective optimizations for antenna designs, w...
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| Vydané v: | IEEE transactions on antennas and propagation Ročník 55; číslo 3; s. 556 - 567 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York, NY
IEEE
01.03.2007
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 0018-926X, 1558-2221 |
| On-line prístup: | Získať plný text |
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| Abstract | The particle swarm optimization (PSO) is a recently developed evolutionary algorithm (EA) based on the swarm behavior in the nature. This paper presents recent advances in applying a versatile PSO engine to real-number, binary, single-objective and multiobjective optimizations for antenna designs, with a randomized Newtonian mechanics model developed to describe the swarm behavior. The design of aperiodic (nonuniform and thinned) antenna arrays is presented as an example for the application of the PSO engine. In particular, in order to achieve an improved peak sidelobe level (SLL), element positions in a nonuniform array are optimized by real-number PSO (RPSO). On the other hand, in a thinned array, the on/off state of each element is determined by binary PSO (BPSO). Optimizations for both nonuniform arrays and thinned arrays are also expanded to multiobjective cases. As a result, nondominated designs on the Pareto front enable one to achieve other design factors than the peak SLL. Optimized antenna arrays are compared with periodic arrays and previously presented aperiodic arrays. Selected designs fabricated and measured to validate the effectiveness of PSO in practical electromagnetic problems |
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| AbstractList | The particle swarm optimization (PSO) is a recently developed evolutionary algorithm (EA) based on the swarm behavior in the nature. This paper presents recent advances in applying a versatile PSO engine to real-number, binary, single-objective and multiobjective optimizations for antenna designs, with a randomized Newtonian mechanics model developed to describe the swarm behavior. The design of aperiodic (nonuniform and thinned) antenna arrays is presented as an example for the application of the PSO engine. In particular, in order to achieve an improved peak sidelobe level (SLL), element positions in a nonuniform array are optimized by real-number PSO (RPSO). On the other hand, in a thinned array, the on/off state of each element is determined by binary PSO (BPSO). Optimizations for both nonuniform arrays and thinned arrays are also expanded to multiobjective cases. As a result, nondominated designs on the Pareto front enable one to achieve other design factors than the peak SLL. Optimized antenna arrays are compared with periodic arrays and previously presented aperiodic arrays. Selected designs fabricated and measured to validate the effectiveness of PSO in practical electromagnetic problems The particle swarm optimization (PSO) is a recently developed evolutionary algorithm (EA) based on the swarm behavior in the nature. This paper presents recent advances in applying a versatile PSO engine to real- number, binary, single-objective and multiobjective optimizations for antenna designs, with a randomized Newtonian mechanics model developed to describe the swarm behavior. The design of aperiodic (nonuniform and thinned) antenna arrays is presented as an example for the application of the PSO engine. In particular, in order to achieve an improved peak sidelobe level (SLL), element positions in a nonuniform array are optimized by real-number PSO (RPSO). On the other hand, in a thinned array, the on/off state of each element is determined by binary PSO (BPSO). Optimizations for both nonuniform arrays and thinned arrays are also expanded to multiobjective cases. As a result, nondominated designs on the Pareto front enable one to achieve other design factors than the peak SLL. Optimized antenna arrays are compared with periodic arrays and previously presented aperiodic arrays. Selected designs fabricated and measured to validate the effectiveness of PSO in practical electromagnetic problems. The particle swarm optimization (PSO) is a recently developed evolutionary algorithm (EA) based on the swarm behavior in the nature. |
| Author | Rahmat-Samii, Y. Nanbo Jin |
| Author_xml | – sequence: 1 surname: Nanbo Jin fullname: Nanbo Jin organization: Dept. of Electr. Eng., California Univ., Los Angeles, CA – sequence: 2 givenname: Y. surname: Rahmat-Samii fullname: Rahmat-Samii, Y. organization: Dept. of Electr. Eng., California Univ., Los Angeles, CA |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18599323$$DView record in Pascal Francis |
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| Keywords | Performance evaluation Lateral lobe single-objective Electromagnetism Evolutionary algorithm Evolutionary computation Multiobjective programming optimization methods Binary number Implementation Aperiodic array antenna Aperiodic antenna array Biomimetics real-number optimization binary optimization multiobjective Swarm intelligence particle swarm optimization (PSO) Antenna array |
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| References | ref35 ref13 haupt (ref39) 1999 ref34 ref12 ref37 ref15 chellapilla (ref38) 1998 ref36 ref14 ref31 harrington (ref30) 1961; 9 ref33 ref11 ref32 ref10 ref2 ref1 ref16 jin (ref24) 2001 ref19 ref18 fieldsend (ref22) 2002 ref23 steinberg (ref40) 1983 ref26 ref20 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 parsopoulos (ref21) 2002; 1 eberhart (ref17) 1998 weile (ref25) 1999 |
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| SubjectTerms | Algorithm design and analysis Algorithms Antenna arrays Antenna measurements Antennas Aperiodic antenna array Applied classical electromagnetism Applied sciences binary optimization Design optimization Electromagnetic wave propagation, radiowave propagation Electromagnetism; electron and ion optics Engines Evolutionary computation Exact sciences and technology Flowcharts Fundamental areas of phenomenology (including applications) multiobjective Optimization methods Particle swarm optimization particle swarm optimization (PSO) Physics Radiocommunications real-number optimization Reflector antennas single-objective Studies Telecommunications Telecommunications and information theory |
| Title | Advances in Particle Swarm Optimization for Antenna Designs: Real-Number, Binary, Single-Objective and Multiobjective Implementations |
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