Application of ant colony algorithm for continuous space optimization

Abstract

Aim to the disadvantages that ant colony optimization is not applied to continuous optimization problems and easy to get into local optimum, a fast global ant colony algorithm is proposed. In this algorithm the searching way that searches near the best solution and makes the best solution as the initial solution is adopted in order to widen searching scope to avoid getting into local optimum, and then it is applied to test some typical functions. The result that compares with other optimizations on testing these functions showed that the improved algorithm is not only applied to continuous optimization problems, but also has fast global optimization, fast searching rate and high optimizing precision.