An ACO algorithm benchmarked on the BBOB noiseless function testbed
Tianjun Liao, Daniel Molina, Thomas Stützle, Marco A. Montes de, Marco Dorigo · 2012
ACOR is an ant colony optimization algorithm for continuous domains. In this article, we benchmark ACOR on the BBOB noiseless function testbed, and compare its performance to PSO, ABC and GA algorithms from previous BBOB workshops. Our experiment shows that ACOR performs better than PSO, ABC and GA on the moderate functions, ill-conditioned functions and multi-modal functions. Among 24 functions, ACOR solved 19 in dimension 5, 9 in dimension 20, and 7 across dimensions from 2 to 40. Furthermore, in dimension 5, we present the results of the ACOR when it uses variable correlation handling. The latter version is competitive on the five dimensional functions to (1+1)-CMA-ES and BIPOP-CMA-ES.