Ant colony algorithm is a reference to the nature of insects through own organization groups produced the cooperation capacity of swarm intelligence solve combinatorial optimization problem of typical examples. Ant colony algorithm is by Italian scholars such as Dorigo M in the early 1990s in nature, through the simulation optimization behavior of ants group proposed a new heuristic bionic evolution algorithm, it in resolving the traveling salesman problem of good results are obtained. In solving the traveling salesman problem (TSP), the first introduced pretreatment, crossover strategies for the specific map abstract to completely diagram of common without the TSP abstraction, namely, to seek out a completely figure without to Hamilton circuit, then use ant colony algorithm to solve. Experimental results show the feasibility and efficiency of this method. Introduces the principle of ant colony algorithm and characteristics, the paper introduces the parameter Settings through its changes and get more optimal solution this improvement, finally summarized the ant colony algorithm should have the characteristic and future research strategies and trends. (我不要钱，如果你执意要给，那我就收五块。)
游客2
Ant colony algorithm is a reference to the nature of insects through own organization groups produced the cooperation capacity of swarm intelligence solve combinatorial optimization problem of typical examples. Ant colony algorithm is by Italian scholars such as Dorigo M in the early 1990s in nature, through the simulation optimization behavior of ants group proposed a new heuristic bionic evolution algorithm, it in resolving the traveling salesman problem of good results are obtained.
In solving the traveling salesman problem (TSP), the first introduced pretreatment, crossover strategies for the specific map abstract to completely diagram of common without the TSP abstraction, namely, to seek out a completely figure without to Hamilton circuit, then use ant colony algorithm to solve. Experimental results show the feasibility and efficiency of this method. Introduces the principle of ant colony algorithm and characteristics, the paper introduces the parameter Settings through its changes and get more optimal solution this improvement, finally summarized the ant colony algorithm should have the characteristic and future research strategies and trends. 不能肯定全对，仅供参考
游客3
Ant colony algorithm is a reference in the nature of social insects through their own organization cooperation ability of swarm intelligence produced solve combinatorial optimization problem of typical examples. Ant colony algorithm is by Italian scholars Dorigo M people in the early 1990 s, through the simulation of the ant optimization nature group behavior and puts forward a new kind of heuristic bionic evolution algorithm, it in resolving the traveling salesman problem of good results are obtained.
In solving the traveling salesman problem (TSP), the first into preprocessing crossover strategies, the concrete to the abstract map of the common to completely figure, that is, without the TSP abstract for the sake of no to a completely figure Hamilton, then use the loop ant colony algorithm to solve. The experimental results show the feasibility and high efficiency. In the introduction of the principle of ant colony algorithm and characteristics, the paper introduces the parameters set by the changes and get more optimal solution the improvement, finally summarized the ant colony algorithm should have the characteristic and the future research strategies and development trend.
游客1
Ant colony algorithm is a reference to the nature of insects through own organization groups produced the cooperation capacity of swarm intelligence solve combinatorial optimization problem of typical examples. Ant colony algorithm is by Italian scholars such as Dorigo M in the early 1990s in nature, through the simulation optimization behavior of ants group proposed a new heuristic bionic evolution algorithm, it in resolving the traveling salesman problem of good results are obtained.
In solving the traveling salesman problem (TSP), the first introduced pretreatment, crossover strategies for the specific map abstract to completely diagram of common without the TSP abstraction, namely, to seek out a completely figure without to Hamilton circuit, then use ant colony algorithm to solve. Experimental results show the feasibility and efficiency of this method. Introduces the principle of ant colony algorithm and characteristics, the paper introduces the parameter Settings through its changes and get more optimal solution this improvement, finally summarized the ant colony algorithm should have the characteristic and future research strategies and trends.
(我不要钱，如果你执意要给，那我就收五块。)
游客2
Ant colony algorithm is a reference to the nature of insects through own organization groups produced the cooperation capacity of swarm intelligence solve combinatorial optimization problem of typical examples. Ant colony algorithm is by Italian scholars such as Dorigo M in the early 1990s in nature, through the simulation optimization behavior of ants group proposed a new heuristic bionic evolution algorithm, it in resolving the traveling salesman problem of good results are obtained.
In solving the traveling salesman problem (TSP), the first introduced pretreatment, crossover strategies for the specific map abstract to completely diagram of common without the TSP abstraction, namely, to seek out a completely figure without to Hamilton circuit, then use ant colony algorithm to solve. Experimental results show the feasibility and efficiency of this method. Introduces the principle of ant colony algorithm and characteristics, the paper introduces the parameter Settings through its changes and get more optimal solution this improvement, finally summarized the ant colony algorithm should have the characteristic and future research strategies and trends.
不能肯定全对，仅供参考
游客3
Ant colony algorithm is a reference in the nature of social insects through their own organization cooperation ability of swarm intelligence produced solve combinatorial optimization problem of typical examples. Ant colony algorithm is by Italian scholars Dorigo M people in the early 1990 s, through the simulation of the ant optimization nature group behavior and puts forward a new kind of heuristic bionic evolution algorithm, it in resolving the traveling salesman problem of good results are obtained.
In solving the traveling salesman problem (TSP), the first into preprocessing crossover strategies, the concrete to the abstract map of the common to completely figure, that is, without the TSP abstract for the sake of no to a completely figure Hamilton, then use the loop ant colony algorithm to solve. The experimental results show the feasibility and high efficiency. In the introduction of the principle of ant colony algorithm and characteristics, the paper introduces the parameters set by the changes and get more optimal solution the improvement, finally summarized the ant colony algorithm should have the characteristic and the future research strategies and development trend.