Name
Abstract | ||
|---|---|---|
The differential evolution, one of the most powerful nature inspired algorithm is used to solve the real world problems. This algorithm takes minimum number of function evaluations to reach near to global optimum solution. Although its performance is very good, yet it suffers from the problem of stagnation. In this paper, some new mutation strategies are proposed to improve the performance of differential evolution (DE). The proposed method adds one more vector named as Homeostasis mutation vector in the existing mutation vectors to provide more bandwidth for selecting effective mutant solutions. The proposed approach provides more promising solutions to guide the evolution and helps DE escaping the situation of stagnation. Performance of proposed algorithm is compared with other state-of-the-art algorithms on COCO (Comparing Continuous Optimizers) framework. The result verifies that our proposed Homeostasis mutation strategy outperform most of the state-of-the-art DE variants and other state-of-the-art population based optimization algorithms. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3233/JIFS-169289 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
Adaptation,optimization,evolutionary algorithm | Artificial intelligence,Computational biology,Machine learning,Differential evolution algorithm,Mathematics,Mutation | Journal |
Volume | Issue | ISSN |
32 | 5 | 1064-1246 |
Citations | PageRank | References |
1 | 0.35 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shailendra Pratap Singh | 1 | 4 | 1.09 |
| Anoj Kumar | 2 | 11 | 4.27 |