Table 1 The best parameters setting given the best cost solution for all proposed algorithms
| GA parameters | PSO parameters |
|---|---|
| Maximum number of iterations = 200 | |
| Population size(nPop) = 100 | Constriction coefficients |
| Crossover percentage = 0.7 |
phi1 = 2.05, phi2 = 2.05 phi = phi1 + phi2 |
| Number of offsprings (nc) = 2*round(pc*nPop/2) | |
| Mutation percentage (pm) = 0.3 | chi = 2/(phi-2 + sqrt(phi^2-4*phi)) |
| Number of mutants (nm) = round (pm*nPop) | Inertia weight w = chi |
| Mutation rate (mu) = 0.02 | Inertia weight damping ratio (wdamp) = 1 |
| Selection pressure (beta) = 8 | Personal learning coefficient (c1) = chi*phi1 |
| Gamma = 0.2 | Global learning coefficient (c2) = chi*phi2 |
| Velocity maximal = 0.1*(VarMax-VarMin) | |
| Velocity minimal = − VelMax | |
| VarMin = − 10; VarMax = 10 | |
| DE parameters | |
| Maximum number of iterations (MaxIt) = 200 | |
| Population size (nPop) = 50 | |
| Lower bound of scaling factor (beta_min) C r min = 0.2 | |
| Upper bound of scaling factor (beta_max) C r max = 0.8 | |
| Crossover probability (pCR) = 0.2 | |
| TLBO parameters | |
| MaxIt = 1000; nPop = 50; T F = 1 | |