Ant colony optimization to continuous domains and its convergence

As a new model of intelligent computing, ant colony optimization (ACO) is a great success on combinatorial optimization problems, however, it is restricted to settle the problem of continuous domains because of its discrete nature. An improved ant colony optimization was proposed. In the local search, the improved ant colony approach is based on the idea of ACO that is used for discrete domains, but utilizes Ant Walk and Ant Diffusion operation in the global search, and while each generation accomplished, preserves the best individual to next generation by the idea of 'Elitist Strategy'. Then its convergence was analyzed theoretically, and was proved to converge to the optimization solution rapidly. This algorithm was tested by several benchmark functions, and could handle these optimization problems very well.