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A Memetic Artificial Bee Colony Algorithm for High Dimensional Problems.
- Source :
- International Journal of Computational Intelligence & Applications; Mar2020, Vol. 19 Issue 1, pN.PAG-N.PAG, 14p
- Publication Year :
- 2020
-
Abstract
- This work proposed a memetic version of Artificial Bee Colony algorithm, or called LSABC, which employed a "shrinking" local search strategy. By gradually shrinking the local search space along with the optimization process, the proposed LSABC algorithm randomly explores a large space in the early run time. This helps to avoid premature convergence. Then in the later evolution process, the LSABC finely exploits a small region around the current best solution to achieve a more accurate output value. The optimization behavior of the LSABC algorithm was studied and analyzed in the work. Compared with the classic ABC and several other state-of-the-art optimization algorithms, the LSABC shows a better performance in terms of convergence rate and quality of results for high-dimensional problems. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14690268
- Volume :
- 19
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- International Journal of Computational Intelligence & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 143357330
- Full Text :
- https://doi.org/10.1142/S146902682050008X