Research Article
An Improved Grey Wolf Optimization Strategy Enhanced SVM and Its Application in Predicting the Second Major
Algorithm 1
Pseudocode of the IGWO algorithm.
Begin | Initialize the parameters popsize, maxiter, n, pos, and flag where | popsize: size of population, | maxiter: maximum number of iterations, | : total number of features, | pos: position of grey wolf, | flag: mark vector of features; | Generate the initial positions of grey wolves using binary PSO; | Initialize , , and ; | for | for | if > 0.5 | ; | else | ; | end if | end for | end for | Calculate the fitness of grey wolves with selected features; | alpha = the grey wolf with the first maximum fitness; | beta = the grey wolf with the second maximum fitness; | delta = the grey wolf with the third maximum fitness; | while k < maxiter | for | Update the position of the current grey wolf; | end for | for | for | if | ; | else | ; | end if | end for | end for | Update a, , and ; | Calculate the fitness of grey wolves with selected features; | Update alpha, beta, and delta; | ; | end while | Return the selected features of alpha as the optimal feature subset; | End |
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