An Incremental Ant Colony Algorithm with Local Search for Continuous Optimization

13th Annual Genetic and Evolutionary Computation Conference (GECCO) -- JUL 12-16, 2011 -- Dublin, IRELANDWOS: 000322137100016ACO(R) is one of the most popular ant colony optimization algorithms for tackling continuous optimization problems. In this paper, we propose IACO(R)-LS, which is a variant of ACO(R) that uses local search and that features a growing solution archive. We experiment with Powell's conjugate directions set, Powell's BOBYQA, and Lin-Yu Tseng's Mtsls1 methods as local search procedures. Automatic parameter tuning results show that IACO(R)-LS with Mtsls1 (IACO(R)-Mtsls1) is not only a significant improvement over ACO(R), but that it is also competitive with the state-of-the-art algorithms described in a recent special issue of the Soft Computing journal. Further experimentation with IACO(R)-Mtsls1 on an extended benchmark functions suite, which includes functions from both the special issue of Soft Computing and the IEEE 2005 Congress on Evolutionary Computation, demonstrates its good performance on continuous optimization problems.Assoc Comp Machinery, Special Interest Grp Genet & Evolutionary Computat (ACM SIGEVO)E-SWARM project; ERC Advanced Grant; French Community of BelgiumThis work was supported by the E-SWARM project, funded by an ERC Advanced Grant, and by the Meta-X project, funded by the Scientific Research Directorate of the French Community of Belgium. Thomas Stutzle and Marco Dorigo acknowledge support from the Belgian F.R.S.-FNRS, of which they are a Research Associate and a Research Director, respectively