| Title |
A teaching learning based optimization based on orthogonal design for solving global optimization problems
|
|---|---|
| Published in |
SpringerPlus, March 2013
|
| DOI | 10.1186/2193-1801-2-130 |
| Pubmed ID | |
| Authors |
Suresh Chandra Satapathy, Anima Naik, K Parvathi |
| Abstract |
In searching for optimal solutions, teaching learning based optimization (TLBO) (Rao et al. 2011a; Rao et al. 2012; Rao & Savsani 2012a) algorithms, has been shown powerful. This paper presents an, improved version of TLBO algorithm based on orthogonal design, and we call it OTLBO (Orthogonal Teaching Learning Based Optimization). OTLBO makes TLBO faster and more robust. It uses orthogonal design and generates an optimal offspring by a statistical optimal method. A new selection strategy is applied to decrease the number of generations and make the algorithm converge faster. We evaluate OTLBO to solve some benchmark function optimization problems with a large number of local minima. Simulations indicate that OTLBO is able to find the near-optimal solutions in all cases. Compared to other state-of-the-art evolutionary algorithms, OTLBO performs significantly better in terms of the quality, speed, and stability of the final solutions. |
Mendeley readers
The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 4 | 4% |
| Chile | 1 | 1% |
| Malaysia | 1 | 1% |
| Unknown | 84 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 25 | 28% |
| Student > Master | 12 | 13% |
| Student > Doctoral Student | 7 | 8% |
| Researcher | 5 | 6% |
| Student > Postgraduate | 4 | 4% |
| Other | 14 | 16% |
| Unknown | 23 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 36 | 40% |
| Computer Science | 15 | 17% |
| Energy | 3 | 3% |
| Economics, Econometrics and Finance | 2 | 2% |
| Biochemistry, Genetics and Molecular Biology | 1 | 1% |
| Other | 6 | 7% |
| Unknown | 27 | 30% |
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 23 March 2013.
All research outputs
#20,185,720
of 22,701,287 outputs
Outputs from SpringerPlus
#1,461
of 1,852 outputs
Outputs of similar age
#172,715
of 197,511 outputs
Outputs of similar age from SpringerPlus
#68
of 135 outputs
Altmetric has tracked 22,701,287 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,852 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 1st percentile – i.e., 1% 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 197,511 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.