The Study And Realization About The Improvement Of Ant Colony Algorithm Based On Inver-over Operator

As a typical paradigm of swarm intelligence, ant colony algorithm had been paid more and more attention. It is another heuristic search algorithm applied in combinational optimized problem after simulated annealing algorithm, genetic algorithm, taboo search algorithm, ANN and so on. Not only can ant colony algorithm(ACO) realize intelligent search, intelligent optimize, but also has many characteristics such as robustness, positive feedback, distribute computing, easily combined with other algorithms and so forth. So it has been one of the most attractive researching subject in the field of swarm intelligence.As a new intelligent algorithm for optimization problem, it has some disadvantages, such as, it gets solution slowly, and gets into local optimization easily. According to these problems, the basic theory of ACO is analyzed deeply in the thesis. It summarizes the research development of ACO. And the disadvantages of ACO are analyzed. The effect of parameters on the performance of ACO is validated by experiments. The perfect value of parameters are given. After thorough research of the present improved model of ACO, it brings forwards an improved ACO based on inver-over operator. It can jump out the local optimization by the inver-over operator and make itself be adaptive. So it can increase the number of local solutions and enlarge the range of the optimum solution. The experiment results show that the improved algorithm accelerates the convergence speed of the optimum solution and improves the quality of the optimum solution. So it largely enhances the capability of ACO.