I did some post-processing after running the grid search. The following steps were carried out:

1) Selected those sets whose cost fun were less than a specific threshold (here 10000)

2) Next task was to see if the parameters of these good solutions had some pattern

3) I used a dimensionality reduction technique called t-SNE to project the 6 dimensional parameter space to 2 dim (for better visualization )

4) Made a scatter plot of these (see fig )

5) Used K-Means to find the clusters in this data

6) MarkerSize & Color reflect the cost fun. Bigger the marker size means better the solution.

7) Visual inspection implied cluster 5 had the best ranking points & more than any other cluster

8) These points had the following Parameter set: Workers {20,40}, SwarmSize {500}, MaxIter {500}, Self Adjustment {1}, Social Adjustment {1}, Tolerance {1e-3,1e-8} 

     See fig: for the box plot 

9) It looks like is a particular set of values rather than individual values that gives the best results.