Table 4 HWGEA and comparative algorithm parameters.

From: A hybrid evolutionary algorithm for influence maximization in complex networks using invasive weed optimization and gravitational search

Algorithm

Parameters

HWGEA

\(\begin{gathered} G_{0} = 100, \beta = 15, \sigma_{initial} = 1.00 \to \sigma_{final} = 0.01 \left( {linear decay} \right), S_{min} = 2, \hfill \\ S_{max} = 8, mutation rate = 0.10, Top - K decay: K\left( t \right) = {\text{max}}\left( {3, 0.25N\cdot\left( {1 - t/T\_max } \right)} \right) \hfill \\ \end{gathered}\)

DHWGEA

Same adaptive mutation; discrete \({\sigma }_{t}=0.50\to 0.20\); neighborhood \(size=\text{deg}\left(v\right)\cup 2-hop\) boundary (dynamic)

SHADE

H = 5, \({p}_{best}=0.11\), archive rate = 1.40

LSHADE-SPACMA

\(H=5\), \({p}_{best}=0.11\), \(c=0.80\),\({F}_{cp}=0.50\)

GSA

\({R}_{power}=1\), \({R}_{norm}=2\),\({G}^{0}=100\)

GA

crossover \(p=0.80\), mutation \(p=0.05\)

GBO

\({\rho }_{1}=0.60\), \({\rho }_{2}\)=0.40, DRM = 0.50

PDO

foraging = 0.70, social = 0.30, digging = 0.20

RSA

\(\alpha =0.10\), \(\beta =0.50\), hunting = 0.80

GOA

chase = 0.60, escape = 0.40, step = 0.20