Name
Abstract | ||
|---|---|---|
Differential evolution (DE) is a robust algorithm of global optimization which has been used for solving many of the real-world applications since it was proposed. However, binomial crossover does not allow for a sufficiently effective search in local space. DE's local search performance is therefore relatively poor. In particular, DE is applied to solve the complex optimization problem. In this case, inefficiency in local research seriously limits its overall performance. To overcome this disadvantage, this paper introduces a new local search scheme based on Hadamard matrix (HLS). The HLS improves the probability of finding the optimal solution through producing multiple offspring in the local space built by the target individual and its descendants. The HLS has been implemented in four classical DE algorithms and jDE, a variant of DE. The experiments are carried out on a set of widely used benchmark functions. For 20 benchmark problems, the four DE schemes using HLS have better results than the corresponding DE schemes, accounting for 80%, 75%, 65%, and 65% respectively. Also, the performance of jDE with HLS is better than that of jDE on 50% test problems. The experimental results and statistical analysis have revealed that HLS could effectively improve the overall performance of DE and jDE. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1155/2021/8930980 | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE |
DocType | Volume | ISSN |
Journal | 2021 | 1687-5265 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changshou Deng | 1 | 39 | 10.80 |
| Xiaogang Dong | 2 | 0 | 0.34 |
| Yucheng Tan | 3 | 0 | 0.34 |
| Hu Peng | 4 | 46 | 13.63 |