Name
Abstract | ||
|---|---|---|
Social emotional optimisation algorithm SEOA has been successfully applied in a variety of real-world applications. However, it may suffer from slow convergence rate when solving complex optimisation problems. In order to improve the performance of SEOA on complex optimisation problems, in this paper, an enhanced social emotional optimisation algorithm with elite multi-parent crossover MCSEOA is proposed. In MCSEOA, it employs the elite multi-parent crossover operator to exploit the neighbourhood solutions of the current population. The numerical experiments are conducted on 13 classical test functions. Comparison results demonstrate that MCSEOA can significantly improve the performance of the traditional SEOA. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1504/IJCSM.2016.081694 | IJCSM |
Keywords | Field | DocType |
evolutionary algorithms, global optimisation, social emotional optimisation, multi-parent crossover | Population,Crossover,Evolutionary algorithm,Elite,Computer science,Algorithm,Exploit,Neighbourhood (mathematics),Operator (computer programming),Rate of convergence,Artificial intelligence | Journal |
Volume | Issue | ISSN |
7 | 6 | 1752-5055 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaolu Guo | 1 | 83 | 9.11 |
| Shenwen Wang | 2 | 7 | 0.76 |
| xuezhi yue | 3 | 36 | 2.81 |
| Baoyong Yin | 4 | 1 | 0.69 |
| Changshou Deng | 5 | 39 | 10.80 |
| Zhijian Wu | 6 | 247 | 18.55 |