Name
Title | ||
|---|---|---|
Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results. |
Abstract | ||
|---|---|---|
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously. The relationship between tasks varies between different test problems, which would be helpful to have a comprehensive evaluation of the MO-MFO algorithms. It is expected that the proposed test problems will germinate progress the field of the MTMOO research. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Neural and Evolutionary Computing | Continuous optimization,Mathematical optimization,Computer science,Test functions for optimization,Performance metric,Multi-objective optimization,Artificial intelligence,Single objective,Human multitasking,Machine learning |
DocType | Volume | Citations |
Journal | abs/1706.03470 | 3 |
PageRank | References | Authors |
0.47 | 7 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingshui Da | 1 | 15 | 2.00 |
| Yew-Soon Ong | 2 | 4205 | 224.11 |
| Liang Feng | 3 | 601 | 48.54 |
| A. K. Qin | 4 | 3496 | 146.50 |
| Abhishek Gupta | 5 | 351 | 20.59 |
| Zexuan Zhu | 6 | 3 | 0.47 |
| Chuan-Kang Ting | 7 | 4 | 0.86 |
| Tang Ke | 8 | 2798 | 139.09 |
| Xin Yao | 9 | 362 | 82.87 |