| Some upswings have been done based on the research about the traditional genetic algorithm and ant optimization algorithm. A new hybrid algorithm GAACO is brought forward in this thesis, which combines the ant colony optimization's positive character and genetic algorithm's global convergence for faster and better search capability. The algorithm is applied to the classic traveling salesman problem. Compared to the classic algorithm through the tool of MATLAB, the result shows that the feasibility and validity of the algorithm. The main research work in this paper is as follows. 1. Improve on the standard colony optimization algorithm. Three hypothesizes is added on the standard ACO. Ants can communicate with their antenna when they meet on the routine, which can be used to judge the next step. During the communication, the long-term memory and short-term memory is taken effected on the pheromone's global and local updating strategy. 2. Improve on the genetic algorithm. Different problems should adopt different coding scheme in order to optimize the data structure. The adaptation function is modified in order to decrease the computation. The top strengthening factor is given as to speed the convergence. The dynamic adaptation scheme is used to avoid the algorithm prematurely. 3. Put forward a new hybrid algorithm GAACO. Firstly, adopt the genetic algorithm to initial the population. Secondly, convert the population into the original pheromone matrix in order to speeding the convergence. Thirdly, use the routine of ant colony optimization algorithm to update the population of genetic algorithm. 4. Apply to traveling salesman problem. In this paper, the algorithms are compared with emulation. The result shows that the GAACO is better than any other of standard GA, improved GA, standard ACO, and improved ACO. The result shows that the GAACO has higher convergence probability and global convergence capability. It can reach the global optimal value fast within fewer iteration times.
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