From: Development of particle swarm clustered optimization method for applications in applied sciences
Method | Parameter | |
---|---|---|
PSO | Population | ANFIS and MLPNN: 1000, REs: 100 |
Iteration | 1000 | |
Search space range | [− 10, 10] | |
Momentum factor (w) | 0.99 | |
Personal learning coefficient (c1) | 1 | |
Global learning coefficient (c2) | 2 | |
PSCO | Population | ANFIS and MLPNN: 1000, REs: 100 |
Iteration | 1000 | |
Clusters iteration (Im) | 300 | |
Number of clusters (m) | 10 | |
Search space range | [− 10, 10] | |
Momentum factor (w) | 0.99 | |
Personal learning coefficient (c1) | 1 | |
Global learning coefficient (c2) | 2 | |
AGPSO | Population | ANFIS and MLPNN: 1000, REs: 100 |
Iteration | 1000 | |
Search space range | [− 10, 10] | |
Momentum factor (w) | 0.99 | |
INFO | Population | ANFIS and MLPNN: 1000, REs: 100 |
Iteration | 1000 | |
Search space range | [− 10, 10] | |
DMOA | Population | ANFIS and MLPNN: 1000, REs: 100 |
Number of baby sister | 10 | |
Iteration | 1000 | |
Search space range | [− 10, 10] | |
δ (Eq. 11) | 2 |