Ant Colony Algorithm And Its Application Research In Case-Based Reasoning

Case-based reasoning (CBR) as a reinforcement of great importance to the rule-based reasoning has aroused intensive attention of the researchers in the artificial intelligence domain. It is a hot topic and cutting-edge in the area of artificial intelligence and machine learning. The CBR system solves the current problem by retrieving the similar problems from the case base, it has overcame the low efficiency in dealing with the problems outside the bound and matching collision and other defects in the traditional knowledge process system.In the CBR system, the case base is becoming increasingly larger with the incremental learning which results in the decline of case retrieval efficiency and its weaker performance. So the case retrieval is the bottleneck for the system. This thesis proposes the ant colony algorithm based case retrieval method and successfully put it into realization.In the first place, the thesis proposes an improvement on ant colony algorithm employing the random insertion algorithm and improved 3-opt algorithm, to settle the defect of low efficiency for the lack of information pheromone in the initial stage. Then, the thesis uses ant colony algorithm which is based on the elicitation of ant's feeding to solve the clustering problem. Finally, the thesis employs the optimized ant colony algorithm to solve the case retrieval problem of which the decline of efficiency occurs when the case library is of massive size. The thesis sets up the case retrieval system based on ant colony algorithm and test twice with the data obtained from international database. The experiment shows that the system has got well performance both in the aspects of time and accuracy.