ACO 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-theart algorithms described in a recent special issue of the Soft Computing journal. Further experimentation with IACO RMtsls1 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.
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