Table 2.
Assigned values to the control parameters of competitor algorithms.
| Algorithm | Parameter | Value |
|---|---|---|
| AVOA | L1, L2 | 0.8, 0.2 |
| W | 2.5 | |
| P1, P2, P3 | 0.6, 0.4, 0.6 | |
| RSA | Sensitive parameter | β = 0.01 |
| Sensitive parameter | α = 0.1 | |
| Evolutionary Sense (ES) | ES: randomly decreasing values between 2 and − 2 | |
| MPA | Binary vector | U = 0 or 1 |
| Random vector | R is a vector of uniform random numbers in [0, 1] | |
| Constant number | P = 0.5 | |
| Fish Aggregating Devices (FADs) | FADs = 0.2 | |
| TSA | c1, c2, c3 | Random numbers lie in the interval [0,1] |
| Pmin | 1 | |
| Pmax | 4 | |
| WOA | l is a random number in [− 1,1] | |
| r is a random vector in [0, 1] | ||
| Convergence parameter (a) | a: Linear reduction from 2 to 0 | |
| GWO | Convergence parameter (a) | a: Linear reduction from 2 to 0 |
| Wormhole existence probability (WEP) | Min(WEP) = 0.2 and Max(WEP) = 1 | |
| MVO | Exploitation accuracy over the iterations (p) | p = 1 |
| TLBO | random number | rand is a random number from interval [0,1] |
| TF: teaching factor | TF = round [(1 + rand)] | |
| GSA | Alpha | 20 |
| G0 | 100 | |
| Rnorm | 2 | |
| Rnorm | 1 | |
| PSO | Velocity limit | 10% of dimension range |
| Topology | Fully connected | |
| Inertia weight | Linear reduction from 0.9 to 0.1 | |
| Cognitive and social constant | (C1, C2) = (2, 2) | |
| GA | Type | Real coded |
| Mutation | Gaussian (Probability = 0.05) | |
| Crossover | Whole arithmetic (Probability = 0.8) | |
| Selection | Roulette wheel (Proportionate) |