Name
Abstract | ||
|---|---|---|
Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10462-017-9559-1 | Artificial Intelligence Review |
Keywords | Field | DocType |
Krill herd,Engineering optimization,Swarm intelligence,Metaheuristic,Nature-inspired algorithm | Data mining,Swarm behaviour,Computer science,Swarm intelligence,Krill herd algorithm,Metaheuristic optimization,Krill herd,Krill,Engineering optimization,Metaheuristic | Journal |
Volume | Issue | ISSN |
51.0 | 1.0 | 1573-7462 |
Citations | PageRank | References |
10 | 0.60 | 68 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gai-Ge Wang | 1 | 1251 | 48.96 |
| Amir Hossein Gandomi | 2 | 1836 | 110.25 |
| Amir Hossein Alavi | 3 | 1016 | 45.59 |
| Dunwei Gong | 4 | 88 | 8.63 |