Continuous Ant Colony Algorithm Based on Entity and Its Convergence

Ant colony algorithm (ACO) is very suitable for solving the optimization problem of discrete function. But its discrete nature restricts the application for continuous domains. In this paper, an improved ant colony optimization is proposed, which is based on the idea of entity. It is a stochastic search algorithm, and doesn\´t need continuous evaluation of derivatives for the object function. In the local search, improved ant colony approach is based on the idea of ACO that is used for discrete domains, but utilizes ant walk and ant diffusion operations in the global search. And while each generation accomplished, the new algorithm preserves the best individual to next generation by the idea of "elitist strategy". Then its convergence is analyzed theoretically, and is proved to converge to the optimization solution. Finally, this algorithm is also tested by several benchmark functions. The simulation results show that it can handle these optimization problems very well.