Name
Abstract | ||
|---|---|---|
A multi-stage krill herd (MSKH) algorithm is presented to fully exploit the global and local search abilities of the standard krill herd (KH) optimization method. The proposed method involves exploration and exploitation stages. The exploration stage uses the basic KH algorithm to select a good candidate solution set. This phase is followed by fine-tuning a good candidate solution in the exploitation stage with a focused local mutation and crossover (LMC) operator in order to enhance the reliability of the method for solving global numerical optimization problems. Moreover, the elitism scheme is introduced into the MSKH method to guarantee the best solution. The performance of MSKH is verified using twenty-five standard and rotated and shifted benchmark problems. The results show the superiority of the proposed algorithm to the standard KH and other well-known optimization methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1142/S021821301550030X | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS |
Keywords | Field | DocType |
Global optimization problem, multi-stage, krill herd, local mutation and crossover operator, multimodal function | Mathematical optimization,Crossover,Computer science,Krill herd algorithm,Krill herd,Operator (computer programming),Solution set,Local search (optimization),Optimization problem,Global optimization problem | Journal |
Volume | Issue | ISSN |
25 | 2 | 0218-2130 |
Citations | PageRank | References |
15 | 0.49 | 35 |
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 |
| Suash Deb | 4 | 1926 | 82.86 |