Back to Search
Start Over
A new Kriging-Bat Algorithm for solving computationally expensive black-box global optimization problems.
- Source :
-
Engineering Optimization . Feb2019, Vol. 51 Issue 2, p265-285. 21p. - Publication Year :
- 2019
-
Abstract
- Many global optimization (GO) algorithms have been introduced in recent decades to deal with the Computationally Expensive Black-Box (CEBB) optimization problems. The high number of objective function evaluations, required by conventional GO methods, is prohibitive or at least inconvenient for practical design applications. In this work, a new Kriging-Bat algorithm (K-BA) is introduced for solving CEBB problems with further improved search efficiency and robustness. A Kriging surrogate model (SM) is integrated with the Bat Algorithm (BA) to find the global optimum using substantially reduced number of evaluations of the computationally expensive objective function. The new K-BA algorithm is tested and compared with other well-known GO algorithms, using a set of standard benchmark problems with 2 to 16 design variables, as well as a real-life engineering optimization application, to determine its search capability, efficiency and robustness. Results of the comprehensive tests demonstrated the suitability and superior capability of the new K-BA. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0305215X
- Volume :
- 51
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Engineering Optimization
- Publication Type :
- Academic Journal
- Accession number :
- 133508406
- Full Text :
- https://doi.org/10.1080/0305215X.2018.1461853