The defect of the Grey Wolf optimization algorithm and its verification method
| Autor: | Nan liu, Songpeng Niu, Lingfang Chang, Peifeng Niu |
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| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Information Systems and Management Optimization problem Optimization algorithm Computer science Structure (category theory) 02 engineering and technology Function (mathematics) Management Information Systems Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Software |
| Zdroj: | Knowledge-Based Systems. 171:37-43 |
| ISSN: | 0950-7051 |
| Popis: | Grey wolf optimization algorithm (GWO) is a new meta-heuristic optimization technology. Its principle is to imitate the behavior of grey wolves in nature to hunt in a cooperative way. GWO is different from others in terms of model structure. It is a large-scale search method centered on three optimal samples, and which is also the research object of many scholars. In the course of its research, this paper find that GWO is flawed. It has good performance for the optimization problem whose optimal solution is 0, however, for other problems, its advantage is not as obvious as before or even worse. Then it is further found that when GWO solves the same optimization function, the farther the function’s optimal solution is from 0, the worse its performance, and this flaw also appears in other optimization algorithms. Through the study of this defect, the analysis is carried out, and the reason is determined. Finally, although there is no way to make GWO normal, this paper provides a verification method to avoid the same problem, and hopes to help the development of the optimization algorithm. |
| Databáze: | OpenAIRE |
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