| Title |
The q-G method
|
|---|---|
| Published in |
SpringerPlus, October 2015
|
| DOI | 10.1186/s40064-015-1434-4 |
| Pubmed ID | |
| Authors |
Aline C. Soterroni, Roberto L. Galski, Marluce C. Scarabello, Fernando M. Ramos |
| Abstract |
In this work, the q-Gradient (q-G) method, a q-version of the Steepest Descent method, is presented. The main idea behind the q-G method is the use of the negative of the q-gradient vector of the objective function as the search direction. The q-gradient vector, or simply the q-gradient, is a generalization of the classical gradient vector based on the concept of Jackson's derivative from the q-calculus. Its use provides the algorithm an effective mechanism for escaping from local minima. The q-G method reduces to the Steepest Descent method when the parameter q tends to 1. The algorithm has three free parameters and it is implemented so that the search process gradually shifts from global exploration in the beginning to local exploitation in the end. We evaluated the q-G method on 34 test functions, and compared its performance with 34 optimization algorithms, including derivative-free algorithms and the Steepest Descent method. Our results show that the q-G method is competitive and has a great potential for solving multimodal optimization problems. |
Mendeley readers
The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 2 | 50% |
| Professor | 1 | 25% |
| Unknown | 1 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 1 | 25% |
| Engineering | 1 | 25% |
| Unknown | 2 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 18 November 2015.
All research outputs
#14,828,686
of 22,833,393 outputs
Outputs from SpringerPlus
#835
of 1,850 outputs
Outputs of similar age
#157,597
of 284,650 outputs
Outputs of similar age from SpringerPlus
#54
of 131 outputs
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