Research Article
Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search
Algorithm 1
Pseudocode for the WSA algorithm.
Objective function f(x), x = (,…,)T | Initialize the population of wolves, xi(i = 1, 2, …, W) | Define and initialize parameters: | r = radius of the visual range | s = step size by which a wolf moves at a time | α = velocity factor of wolf | pa = a user-defined threshold [0..1], determines how frequently an enemy appears | WHILE (t < generations && stopping criteria not met) | FOR i = 1 : W // for each wolf | Prey_new_food_initiatively(); | Generate_new_location(); | // check whether the next location suggested by the random number generator is new. If | not, repeat generating random location. | IF (dist(, ) < r && xj is better as f() < f()) | xi moves towards xj // xj is a better than xi | ELSE IF | xi = Prey_new_food_passively; | END IF | Generate_new_location(); | IF (rand ()> pa) | xi = xi + rand () + v; // escape to a new pos. | END IF | END FOR | END WHILE |
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