| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 412733 | Neurocomputing | 2010 | 11 Pages |
Abstract
In this paper, we use a recently proposed algorithm—novel global harmony search (NGHS) algorithm to solve unconstrained problems. The NGHS algorithm includes two important operations: position updating and genetic mutation with a low probability. The former can enhance the convergence of the NGHS, and the latter can effectively prevent the NGHS from being trapped into the local optimum. Based on a large number of experiments, the NGHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and SGHS).
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Dexuan Zou, Liqun Gao, Jianhua Wu, Steven Li,