Name
Abstract | ||
|---|---|---|
This study presents an improved krill herd (IKH) approach to solve global optimization problems. The main improvement pertains to the exchange of information between top krill during motion calculation process to generate better candidate solutions. Furthermore, the proposed IKH method uses a new Lévy flight distribution and elitism scheme to update the KH motion calculation. This novel meta-heuristic approach can accelerate the global convergence speed while preserving the robustness of the basic KH algorithm. Besides, the detailed implementation procedure for the IKH method is described. Several standard benchmark functions are used to verify the efficiency of IKH. Based on the results, the performance of IKH is superior to or highly competitive with the standard KH and other robust population-based optimization methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neucom.2014.01.023 | Neurocomputing |
Keywords | Field | DocType |
Global optimization problem,Krill herd,Exchange information,Multimodal function | Convergence (routing),Population,Mathematical optimization,Multimodal function,Lévy flight,Krill herd algorithm,Krill herd,Robustness (computer science),Mathematics,Global optimization problem | Journal |
Volume | ISSN | Citations |
138 | 0925-2312 | 54 |
PageRank | References | Authors |
1.34 | 16 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lihong Guo | 1 | 294 | 11.57 |
| Gai-Ge Wang | 2 | 1251 | 48.96 |
| Amir Hossein Gandomi | 3 | 1836 | 110.25 |
| Amir Hossein Alavi | 4 | 1016 | 45.59 |
| Hong Duan | 5 | 185 | 7.79 |