Evolutionary Centers Algorithm

Summary

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

• Parameters (suggested):

  • Objective function: fobj
  • Dimension: D
  • K-value: K = 7
  • Population size: N = K*D
  • stepsize: eta_max = 2.0
  • binomial probability: P_bin = 0.03
  • Exploit parameter: P_exploit = 0.95
  • Max. number of evaluations: max_evals = 10000*D

• Bounds:

  • Lower: low_bound
  • Upper: up_bound

• Search Type:

  • Maximize:
    • minimize = True
  • minimize:
    • minimize = False

Example

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

from ecapy import eca

# D-dimensional sphere function
def sphere(x):
    s = 0.0
    for xi in x:
        s += xi**2
    return s

x, fx = eca(sphere, D = 10, minimize=True)