| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Numerical Optimization | CEC 2014 | Mean Fitness Error0 | 120 | |
| Black-box Optimization | CEC 30D 2017 | Mean Objective Value0 | 90 | |
| Function Optimization | CEC 100D 2017 | Mean Value2,093.826 | 80 | |
| Numerical Optimization | CEC 50 dimensions 2017 | Mean Objective Value0 | 37 | |
| Dynamic Multi-objective Optimization | CEC DF2 2018 | MHV0.8368 | 32 | |
| Numerical Optimization | CEC D=10 2022 | Friedman Rank1 | 30 | |
| Continuous Optimization | CEC D=30 2014 | Rank3.83 | 20 | |
| Global Optimization | CEC Functions F01-F30 D=50 2014 | Rank3.53 | 19 | |
| Numerical Optimization | CEC D=100 F01-F30 2014 | Rank4.27 | 19 | |
| Numerical Optimization | CEC F10 2022 | Average Value1.786 | 18 | |
| Numerical Optimization | CEC F3 2022 | Average Score6 | 18 | |
| Numerical Optimization | CEC F2 2022 | Average1.334 | 18 | |
| Numerical Optimization | CEC F1 2022 | Average Value3 | 18 | |
| Dynamic Multi-objective Optimization | CEC DF3 2018 | MHV Score50.2 | 16 | |
| Numerical Optimization | CEC D=20 2022 | Friedman Rank3.17 | 15 | |
| Global Optimization | CEC 30D 2017 | Mean Value300 | 14 | |
| Global Optimization | CEC 20D 2022 | FM-Rank (Friedman Ranking)2.75 | 14 | |
| Global Optimization | CEC C12 2022 | Mean Objective Value2,880 | 14 | |
| Global Optimization | CEC2022 C11 | Mean Result6,180 | 14 | |
| Global Optimization | CEC C10 2022 | Mean Value2,490 | 14 | |
| Global Optimization | CEC C9 2022 | Mean Score2,470 | 14 | |
| Global Optimization | CEC C8 2022 | Mean Objective Value2,220 | 14 | |
| Global Optimization | CEC 2022 C7 | Mean Value (C7)2,020 | 14 | |
| Global Optimization | CEC C6 2022 | Mean Score1,800 | 14 | |
| Global Optimization | CEC C5 2022 | Mean900 | 14 |