## Nomenclature
### Artificial Electric Field Algorithm  
[PERSON] and [PERSON] [37] were inspired by [PERSON]'s law of electrostatic force to create a novel artificial electric field algorithm. The concepts of electric field and charged particles provide us a strong theory for the working force of attraction or repulsion between two charged particles. The pseudo code of the AEFA algorithm is proposed in Algorithm A2. ```
Initialize a set of random population \(X_{B}^{i}=\big{(}X_{B}^{1},\ X_{B}^{2},\ldots,\ X_{B}^{N}\big{)}\) of N size, within the limits \(X_{min}^{i}\leq X_{B}^{i}\leq X_{max}^{i}\). Initialize the velocity to a random value  Evaluate the fitness of whole population  Set the iteration to zero  Reproduction and Updating  While criteria not satisfied do  Calculate K (t), best (t) and worst (t)  for i = 1: N do  Evaluate the fitness values  Calculate the total force in each direction  Calculate the acceleration \(\text{V}_{i}\) (t + 1) = rand () \(\times\)\(V_{i}\) (t) + a\({}_{i}\) (t) \(\text{X}_{i}\) (t + 1) = X\({}_{i}\) (t) + \(\text{V}_{i}\) (t + 1)  end for  end while
```  
**Algorithm A2**Pseudo code of AEFA  
While viteria not satisfied do  Calculate K (t), best (t) and worst (t)  for i = 1: N do  Evaluate the fitness values  Calculate the total force in each direction  Calculate the acceleration \(\text{V}_{i}\) (t + 1) = rand () \(\times\)\(V_{i}\) (t) + a\({}_{i}\) (t) \(\text{X}_{i}\) (t + 1) = X\({}_{i}\) (t) + \(\text{V}_{i}\) (t + 1)  end for  end while ```
**Algorithm A3**Pseudo code of AEFA  
Invasive weed optimization is a numerical stochastic optimization algorithm inspired by colonizing weeds, which was introduced in 2006 by [PERSON] and [PERSON] [38]. In IWO, a certain number of weeds make up the whole population, and each weed comprises a set of decision variables. Weeds are a serious threat to desirable plants because they are plants that are invasive and hardy. Weeds are plants which are vigorous and invasive; they pose a serious threat to desirable, cultivated plants in agriculture. Weeds have shown to be very robust and adaptive to change in the environment. The IWO optimization algorithm has been modeled based on four steps: initialization, reproduction, spatial dispersal and competitive exclusion. * _Initialization and Production_ Firstly, the population is spread over the research space randomly; afterwards, each plant is allowed to produce seeds depending on its own fitness; the production of seeds is not only allowed for the better plants' fitness as in the other evolutionary algorithms, but the reproduction step of IWO is also proposed to give a chance to infeasible individuals to survive and reproduce similar to the mechanism which occurs in nature. The weeds producing seeds can be formulated as follows: \[Wed_{n}=\frac{f-f_{min}}{f_{max}-f_{min}}\left(s_{max}-s_{min}\right)+s_{min}\] (1)  
where in each iteration, \(f\) is the current weed's fitness. \(f_{max}\) and \(f_{min}\) represent the max and min fitness values, respectively. \(s_{max}\) and \(s_{min}\) represent the max and min values of the weeds, respectively. * _Spatial Dispersal_ The generated seeds are being randomly distributed over the search space such that they abode near the parent plant. However, the standard deviation (\(\sigma\)) of the random function will be reduced in every iteration, the nonlinear alteration equation of the standard deviation at each iteration is presented as follows: \[\sigma_{inter}=\frac{\left(iter_{max}-iter\right)^{n}}{\left(iter_{max}\right)^ {n}}\Big{(}\sigma_{initial}-\sigma_{final}\Big{)}+\sigma_{final}\] (2)  
where \(iter_{max}\) is the maximum iteration, \(n\) is the nonlinear modulation index, \(\sigma_{initial}\) and \(\sigma_{final}\) are the initial and final values of standard deviation, respectively. * _Competitive Exclusion_ In a colony, the maximum number allowed of plants is limited; for that, competitive exclusion is applied. The plant that leave no offspring would go extinct; otherwise, they can survive.