Evolutionary Centers Algorithm

ECA is a physics-inspired algorithm based on the center of mass concept on a D-dimensional space for real-parameter single-objective optimization. The general idea is to promote the creation of an irregular body using K mass points in the current population, then the center of mass is calculated to get a new direction for the next population... read more.

• Parameters (suggested):

  • Dimension: D
  • K-value: K = 7
  • Population size: N = K*D
  • stepsize: eta_max = 2.0
  • binomial probability: P_bin = 0.03
  • Max. number of evaluations: max_evals = 10000*D

• Bounds:

  • Lower: low_bound
  • Upper: up_bound

• Search Type:

  • Maximize:
    • searchType = 1
  • minimize:
    • searchType = 0

You can write C code to use ECA in your project:

#include "eca.c"
#include "test_functions.c"


int main(int argc, char const *argv[])
{
    srand(time(NULL));

    // ECA parameters
    int D = 10;
    int K = 7;
    int N = K*D;
    double eta_max = 2.0;
    double P_bin = 0.03;
    int    max_evals = 10000*D;
    double low_bound = -10;
    double up_bound = 10;
    int    searchType = 1; // maximize

    // optimize
    double* result = eca(gauss, D, N, K,
                        eta_max,
                        P_bin,
                        max_evals,
                        low_bound,
                        up_bound,
                        searchType);

    // x = result[0 to D - 1]
    // f(x) = result[D]

    return 0;
}

Also, you can build the ECA library by running Make