An ACO algorithm benchmarked on the BBOB noiseless function testbed

Authors:
Tianjun Liao;Daniel Molina;Thomas Stutzle;Marco A. Montes de Oca;Marco Dorigo
Affiliations:
IRIDIA, CoDE, ULB, Brussels, Belgium;Dept. of Computer Engineering, University of Cadiz, Cadiz, Spain;IRIDIA, CoDE, ULB, Brussels, Belgium;Dept. of Mathematical Sciences, University of Delaware, Newark, DE, USA;IRIDIA, CoDE, ULB, Brussels, Belgium
Venue:
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Year:
2012

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Abstract

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.