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Mendeley readers
Attention Score in Context
| Title |
Linear antenna array optimization using flower pollination algorithm
|
|---|---|
| Published in |
SpringerPlus, March 2016
|
| DOI | 10.1186/s40064-016-1961-7 |
| Pubmed ID | |
| Authors |
Prerna Saxena, Ashwin Kothari |
| Abstract |
Flower pollination algorithm (FPA) is a new nature-inspired evolutionary algorithm used to solve multi-objective optimization problems. The aim of this paper is to introduce FPA to the electromagnetics and antenna community for the optimization of linear antenna arrays. FPA is applied for the first time to linear array so as to obtain optimized antenna positions in order to achieve an array pattern with minimum side lobe level along with placement of deep nulls in desired directions. Various design examples are presented that illustrate the use of FPA for linear antenna array optimization, and subsequently the results are validated by benchmarking along with results obtained using other state-of-the-art, nature-inspired evolutionary algorithms such as particle swarm optimization, ant colony optimization and cat swarm optimization. The results suggest that in most cases, FPA outperforms the other evolutionary algorithms and at times it yields a similar performance. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 24% |
| Student > Master | 6 | 21% |
| Student > Doctoral Student | 3 | 10% |
| Researcher | 2 | 7% |
| Student > Bachelor | 2 | 7% |
| Other | 2 | 7% |
| Unknown | 7 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 10 | 34% |
| Computer Science | 7 | 24% |
| Physics and Astronomy | 1 | 3% |
| Unspecified | 1 | 3% |
| Unknown | 10 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 April 2016.
All research outputs
#18,450,346
of 22,860,626 outputs
Outputs from SpringerPlus
#1,260
of 1,849 outputs
Outputs of similar age
#218,379
of 300,126 outputs
Outputs of similar age from SpringerPlus
#112
of 162 outputs
Altmetric has tracked 22,860,626 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,849 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 300,126 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 162 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.