Improved ant colony algorithm for solving continuous space optimization problems

Ant colony algorithm is a novel category of bionic meta-heuristic algorithm, and parallel computation and positive feedback mechanism are adopted in this algorithm. The ant colony algorithm has strong robustness and easy to combine with other methods in optimization. Although the ant colony algorithm for the heuristic solution of discrete space optimization problems enjoys a rapidly growing popularity, but few are reported for the heuristic solution of continuous space optimization problems. Based on the introduction of the mechanism and mathematical model of basic ant colony algorithm, an improved ant colony algorithm for solving continuous space optimization problems was proposed. The solution vector of continuous space optimization problem was decomposed into finite grids. Meanwhile, the cost function related to the transition probability was constructed. In order to enhance the global convergence performance of the improved ant colony algorithm, meeting search strategy was adopted in the improved ant colony algorithm, and the range of possible pheromone trails on each solution component was limited to a maximum-minimum interval. The numerical simulation results demonstrate that the improved ant colony algorithm can find better global solution for continuous space optimization problems than the adaptive ant colony algorithm proposed in the literature [11], and this new algorithm presents a feasible and effective way to solve various continuous space optimization problems.