Classifier Accuracy with Grid Search is not similar to accuracy without Grid Search

Hello guys I'm doing Grid Search for tuning Random Forest Parameters when the process ends it gives me a set of best parameters also the accuracy of the best parameters for RF, now my question is when I run the process without Grid Search by setting Random Forest parameters that i got from Grid Search I notice I get a downgrade accuracy??? Can anyone explain the difference because both approaches are the same the only difference is that the first approach is with Grid Search and the second time without Grid Search?
 I have includes screenshots of my process 

my dataset is Glass Type with 214 samples it contains 1 duplicate row, 6 class Unbalance Data, I run my process as following
send dataset into Optimize Parameters (Grid) operator
inside Optimize Parameters (Grid) operator:
1- remove duplicates 
2- Normalize
3- split Data into 80:20
4- use Smote on Training data only
5- Train RF
6- Evaluate Model