Collaborative Strategy for Grey Wolf Optimization Algorithm
| Autor: | Sandi N. Fakhouri, Esra Alzaghoul |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Collaborative strategy Multidisciplinary Optimization algorithm Computer science Particle swarm optimizer 02 engineering and technology Sine cosine algorithm Set (abstract data type) 020901 industrial engineering & automation Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | Modern Applied Science. 12:73 |
| ISSN: | 1913-1852 1913-1844 |
| DOI: | 10.5539/mas.v12n7p73 |
| Popis: | Grey wolf Optimizer (GWO) is one of the well known meta-heuristic algorithm for determining the minimum value among a set of values. In this paper, we proposed a novel optimization algorithm called collaborative strategy for grey wolf optimizer (CSGWO). This algorithm enhances the behaviour of GWO that enhances the search feature to search for more points in the search space, whereas more groups will search for the global minimal points. The algorithm has been tested on 23 well-known benchmark functions and the results are verified by comparing them with state of the art algorithms: Polar particle swarm optimizer, sine cosine Algorithm (SCA), multi-verse optimizer (MVO), supernova optimizer as well as particle swarm optimizer (PSO). The results show that the proposed algorithm enhanced GWO behaviour for reaching the best solution and showed competitive results that outperformed the compared meta-heuristics over the tested benchmarked functions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|