Name
Abstract | ||
|---|---|---|
•OB-L-ALO is proposed to improve exploration and convergence rate of the original ALO algorithm.•Two strategies: Firstly, Laplace distributed random numbers. Secondly, Opposition based learning are employed.•A set of 27 benchmark problems have been used results verification.•The behavior analysis of proposed algorithm is extensively performed using wide variety of metrics.•The proposed algorithm is employed on real world engineering design problems. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.jocs.2017.10.007 | Journal of Computational Science |
Keywords | Field | DocType |
Ant Lion Optimizer,Optimization,Benchmark functions,Laplace distribution,Opposition based learning | Mathematical optimization,Premature convergence,Laplace distribution,Local optimum,Computer science,Random walk,Uniform distribution (continuous),Engineering design process,Rate of convergence,Optimization problem | Journal |
Volume | ISSN | Citations |
23 | 1877-7503 | 3 |
PageRank | References | Authors |
0.38 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shail Kumar Dinkar | 1 | 7 | 1.15 |
| Kusum Deep | 2 | 876 | 82.14 |