Name
Abstract | ||
|---|---|---|
This paper describes a variant of the Incremental Ant Colony Optimization algorithm for continuous optimization (IACO(R)). The original IACO(R) approach estimates the Probability Density Function (PDF) using Gaussians constructed around candidate solutions to generate new solutions. We use Support Vector Regression (SVR) to fit a regressor to the candidate solutions. The minima of the fitted regressor are found using a variant of EigenAnt. This approach is based on the observation that minima tend to be clustered in real problems, and estimating the landscape of minima is more efficient than estimating the landscape of the original function. We present results on two fronts. We demonstrate the effect of the use of SVR and modified EigenAnt. Further, we also demonstrate the performance of our approach on the Soft Computing (SOCO) benchmark functions for global optimization, and obtain appreciable results. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | IACO(R), Support Vector Regression, Gradient Descent, EigenAnt, Local Search, SOCO Benchmarks |
Field | DocType | ISSN |
Global optimization,Computer science,Control engineering,Artificial intelligence,Machine learning | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Udit Kumar | 1 | 1 | 1.36 |
| sumit soman | 2 | 20 | 7.53 |
| jayadeva | 3 | 67 | 10.50 |