Abstract
This paper addresses the problem of finding the global minimum of a nonconvex function. A new hybrid algorithm for this problem based on tabu search is developed. It is hybrid in the sense that search directions are generated using tabu search strategy and then they are used in an optimization algorithm. The algorithm is tested on some standard test functions and its performance is compared with existing algorithms. Computational results show that the proposed algorithm is very efficient and robust.
| Original language | English |
|---|---|
| Pages (from-to) | 198-208 |
| Number of pages | 11 |
| Journal | European Journal of Operational Research |
| Volume | 103 |
| Issue number | 1 |
| DOIs | |
| State | Published - 16 Nov 1997 |
Keywords
- Global optimization
- Random search directions
- Tabu search
ASJC Scopus subject areas
- General Computer Science
- Modeling and Simulation
- Management Science and Operations Research
- Information Systems and Management