Invasive Weed Optimization

Reference: Mehrabian A R, Lucas C. A novel numerical optimization algorithm inspired from weed colonization[J]. Ecological Informatics, 2006, 1(4): 355-366.

Variables	Meaning
ipop	The initial population size
mpop	The maximum population size
iter	The maximum number of iterations
smin	The minimum number of seeds
smax	The maximum number of seeds
isigma	The initial value of standard deviation
fsigma	The final value of standard deviation
lb	The lower bound (list)
ub	The upper bound (list)
pos	The position of seeds (list)
score	The score of seeds (list)
dim	Dimension
gbest	The score of the global best agent
iter_best	The global best score of each iteration (list)
con_iter	The last iteration number when "gbest" is updated

Test problem

$$ f(x)=\sum_{i=1}^{30}x_i^2,\qquad -10\leq x_i\leq10, \quad i=1,\cdots, 30. $$

Example

if __name__ == '__main__':
    ipop = 20
    mpop = 100
    iter = 2000
    smin = 0
    smax = 5
    isigma = 1
    fsigma = 1e-6
    lb = [-10] * 30
    ub = [10] * 30
    print(main(ipop, mpop, iter, smin, smax, isigma, fsigma, lb, ub))

Output:

The IWO converges at its 2000-th iteration, and the global best value is 1.4811243161587636e-06.

{
    'best score': 1.4811243161587636e-06, 
    'best solution': [0.0004018456981121767, -3.2682599447569955e-06, -9.455509392674457e-05, 5.188477312079762e-05, 0.00017137500067564303, -0.00012223522965487042, 0.00011623617641158009, -0.000124118982871797, 4.8569097120283375e-05, 0.0002976310355268008, 6.15705239072025e-05, 8.077446302055623e-05, 0.00020276163906065369, -0.0002811285829622663, -0.0003058244802800531, -0.00018882593646512292, -0.00031291318574091793, -7.098423681308358e-05, -0.000472572417336426, 0.00011401801538079769, 0.0002072860452717333, 0.00016774266470804766, 0.00016678967570886395, -0.00011968615297976984, -0.00038220692876286274, 0.00023839458936768734, -0.00026706592368278285, -0.0002001935540331373, -0.00033141460343734765, 8.664523587924919e-05], 
    'convergence iteration': 2000
}