Solving Continuous Optimization Using Ant Colony Algorithm

One shortcoming of ant colony optimization is that it can not be applied on continuous optimization problems directly. In this paper we propose a new approach for solving continuous optimization problems using ant colony algorithm. While the method maintains the framework of the classical ant colony algorithm, it replaces the discrete frequency in the ant selecting probability by a continuous probability distribution formula using the continuous integral instead of discrete summation. We also use the direction towards the optimum in each dimension as the heuristic information guiding the ants' searching. Experimental results on benchmarks show that our algorithm not only has faster convergence speed than other similar methods, but also effectively improves the accuracy of solution and enhances its robustness.