Name
Abstract | ||
|---|---|---|
Differential evolution (DE) is one of the most popular and powerful evolutionary algorithms for the real-parameter global continuous optimization problems. However, how to balance the exploration and exploitation is harder work to the researchers improving the performance of DE. Very often, we catch one and lose another. To overcome this problem, this paper presents a novel DE variant, called heterozygous DE with Taguchi local search (THDE), in which two new proposed methods (i.e., multiple schemes heterozygous evolution and Taguchi local search) are employed, with one as enhanced exploration ability and the other enhanced exploitation ability. The experimental studies have been conducted on 27 well-known test functions, including unimodal, multimodal and shifted test functions. Experimental results have verified the quality and effectiveness of THDE. Comparison with the state-of-the-art DE variants has proved that THDE is a type of new competitive algorithm. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s00500-014-1482-7 | Soft Computing - A Fusion of Foundations, Methodologies and Applications |
Keywords | Field | DocType |
Differential evolution, Global optimization, Heterozygous evolution, Taguchi local search | Continuous optimization,Mathematical optimization,Global optimization,Evolutionary algorithm,Computer science,Differential evolution,Taguchi methods,Competitive algorithm,Artificial intelligence,Local search (optimization) | Journal |
Volume | Issue | ISSN |
19 | 11 | 1433-7479 |
Citations | PageRank | References |
8 | 0.42 | 43 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hu Peng | 1 | 46 | 13.63 |
| Zhijian Wu | 2 | 247 | 18.55 |