Advances, Systems and Applications
Table 1 Simulation parameters for experimentation
From: Job scheduling problem in fog-cloud-based environment using reinforced social spider optimization
Parameters | Values |
|---|---|
Number of processors | 4, 8, 12, 16, 20, 24 |
Number of Tasks | 20, 40, 80, 160, 320, 400 |
Number of fog nodes | 5, 10, 15, 20 |
DOLSSO (Proposed) | Max_iter: 100, Population size: 60, Hopping rate factor (δ): 0.5, Learning weight (z): 0.1, Upper limit (λ): 1 |
SSO [22] | Learning weight (z): 0.1 Coefficient parameter: [0,2] |
GOA [39] | Attraction force: [2.079,4], Repulsion factor: [0, 2.079], coefficient value: [1, 0.0001] |
SSA [40] | Step size: 10, Fitness function constant: 0.9 |
GWO [41] | coefficient parameter (a): [2,0] |
WOA [42] | Parameter (A): [−1, 1] Random probability (p): 0.5 |