Research On The Ant Colony Optimization Algorithm And Its Applications

Ant Colony Optimization algorithm is a kind of evolution computation algorithm based on bionics, which has the character of positive feedback and parallel processing. It was first put forward to solve the famous traveling salesman problem(TSP) by Marco Dorigo and his colleagues. Now it has been successfully used to solve many kinds of combination optimization problems such as quadratic assignment, job-shop scheduling, vehicle routing, sequential ordering, graph coloring and so on.The research on ACO has been widely carried out in China since the birth of ACO. The work can be classified into two classes: principle research and application research. Application research is making the research on application in different circumstance, which has been widly used in many fields such as data mining and image processing and so on. Principle research is making the research on the algorithm's principle including the modification of algorithm and convergence research, which has fewer products than that of application research. Ant colony optimization algorithm, which is based on bionics, has been successfully used in many fields, especially on combinatorial optimization problems. While many parameters need to be adjusted in its application, it is inconvenient for rookies. In this paper a novel ant colony optimization algorithm based on real time model (TACO) is proposed and its proof of convergence isgiven. It is supposed that each ant's velocity is equal to d_min per time unit and all ants are crawling in full time. Ants communicate with others by the pheromone that is left on the road. After some time the ants trail will be on the optimal route between the food and the nest. It is testified by the experiment that the novel algorithm is as well as other ant colony algorithm and it is simpler to justify the parameters than before.Being a novel intelligent optimizing method, the TACO has some shortcomings as the basic CO. New TACO method combined with some mature intelligent optimizing method such as elitist ants strategy, grouping strategy and genetic...