Improved Ant Colony Algorithm for Continuous Function Optimization

摘要

As a new model of intelligent computing, ant colony optimization (ACO) is a great success on combinatorial optimization problems, however, but research is relatively less in solving problems on continuous space optimization. Based on the mechanism and mathematical model of ant colony algorithm, mutation operation is introduced. The global and local updating rules of ant colony algorithm are improved. The possibility of halting the ant system becomes much lower than the ever in the time arriving at local minimum. At last, this algorithm was tested by several benchmark functions. The simulation results indicate that improved ant colony algorithm can rapidly find superior global solution and the algorithm presents a new effective way for solving this kind of problem.