A Typical Knowledge Discovery Method Based on Gaussian Pigeon-inspired Optimization Algorithm

To discover a typical knowledge discovery and reuse process in discrete manufacturing systems, we propose a method of knowledge discovery based on Gaussian pigeon-inspired optimization algorithm. Based on the uniform encoding of process routes, we propose a new comprehensive indexto describe the similarity between process routes using the information of same process and the ordering information. Based on the comprehensive index, we construct the dissimilarity matrix. In addition, to optimize the clustering result, we propose the Gaussian pigeon-inspired optimization algorithm introduced by a Gauss term to achieve an intelligent clustering of process routes, and retrieve process knowledge from the clustering results to optimize the part machining process. Finally, we take a real manufacturing process as an example to verify the rationality and practicability of the similarity calculation method and Gaussian pigeon-inspired optimization algorithm.