Name
Title | ||
|---|---|---|
Opposition-based antlion optimizer using Cauchy distribution and its application to data clustering problem |
Abstract | ||
|---|---|---|
This paper proposes an improved version of antlion optimizer (ALO) to solve data clustering problem. In this work, Cauchy distribution-based random walk is employed in place of uniform distribution to jump out of local optima as a first strategy. Then opposition-based learning model is utilized in conjunction with acceleration coefficient to overcome the slow convergence of classical ALO as second strategy to propose opposition-based ALO using Cauchy distribution (OB-C-ALO). The performance of the proposed OB-C-ALO is evaluated over a set of benchmark problems of different varieties of characteristics and analysed statistically by performing Wilcoxon rank-sum test. The proposed version then utilizes K-means clustering by refining the clusters formed using K-means as objective function. The algorithm is evaluated on six data sets of UCI machine learning repository and compared with classical ALO and recently developed version of ALO, namely OB-L-ALO, over benchmark test problems as well as data clustering problem and proved to be better in terms of performance achieved. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00521-019-04174-0 | Neural Computing and Applications |
Keywords | DocType | Volume |
Optimization, Cauchy distribution, Opposition-based learning, Data clustering, Intra-cluster variance | Journal | 32 |
Issue | ISSN | Citations |
11 | 0941-0643 | 1 |
PageRank | References | Authors |
0.35 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shail Kumar Dinkar | 1 | 7 | 1.15 |
| Kusum Deep | 2 | 876 | 82.14 |