Table 2 The setup of the parameters.
From: Enhancing grasshopper optimization algorithm (GOA) with levy flight for engineering applications
Algorithm | Parameters | Value |
|---|---|---|
AHA | migration coefficient | 2n |
AO | Â | \(G_1=2\times rand-1\) \(G_2=2\times (1-\frac{t}{T})\) \(QF\left( t\right) =t^\frac{2\times r a n d-1}{{(1-T)}^2}\) |
quality function used to equilibrium | ||
the search strategies QF the slope from the | ||
first location (1) to the last location (t) | ||
DA | inertia weight w | \(w=max+t\times \left( \frac{max-min}{T}\right)\) |
\(min=0.4,\ max=0.9\) | ||
separation weight s | \(s=2\times rand\times [0.1-t(\frac{0.1}{T/2})]\) | |
alignment weight a | \(a=2\times rand\times [0.1-t(\frac{0.1}{T/2})]\) | |
the cohesion weight c | \(c=2\times rand\times [0.1-t(\frac{0.1}{T/2})]\) | |
food factor f | \(f=2\times rand\) | |
enemy factor e | \(e=0.1-t(\frac{0.1}{{T/2}})\) | |
DMOA | convergence constant a | \(a={(1-\frac{t}{T})}^{2\times \frac{t}{T}}\) |
GBO | \(\beta\) the most significant parameter | \(\beta =\beta _{min}+(\beta _{max}-\beta _{min})\times {(1-{(\frac{t}{T})}^3)}^2\) |
in the GBO to balance the exploration | Â | |
and exploitation searching processes | \(\beta _{min}=0.2,\beta _{max}=1.2,\ pr=0.5\) | |
HGS | convergence constant a | \(a=2\times (1-\frac{t}{T})\) |
HHO | \(E_0\) is the initial state of its energy, E indicates the escaping energy of the prey. | \(E=2E_0\times (1-\frac{t}{T})\) |
LFGOA | Â | \(C=C_{max}-t\times \frac{C_{max}-C_{min}}{T}\) |
| Â | convergence constant C | \(C_{max}=1, C_{min}=0.00001\) |
MVO | travelling distance rate (TDR) \(\in {[}0.6 1{]}\) | \(TDR=1-\frac{t^{1/p}}{T^{1/p}},\ p=6\) |
