One of the most popular Ant Colony Optimization (ACO) algorithms for continuous optimization problems is ACOR. In this paper, we propose an incremental ACOR with local search (IACOR-LS) that obtains better results than the original ACOR on a number of continuous optimization problems. We first present a mechanism that improves the search diversification of ACOR. This mechanism consists of a growing solution archive with a special initialization rule applied to entrant solutions. The resulting algorithm, called IACOR, is then hybridized with a local search procedure in order to enhance its search intensification. 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 IACOR-LS with Mtsls1 (IACOR-Mtsls1) is not only a significant improvement over ACOR, but that it is also competitive with the state-of-the-art algorithms described in a recent special issue of the journal Soft Computing. Further experimentation with IACOR-Mtsls1 on the combined benchmark functions suite from both the special issue of Soft Computing and the IEEE 2005 Congress on Evolutionary Computation (CEC 2005) demonstrates its good performance in continuous optimization.
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