Optimization Method for Experimental Identification Capability Evaluation Model Based on Improved Plant Growth Algorithm

Abstract

The optimization problem of the experimental appraisal capability evaluation model can essentially be seen as a multi-attribute decision-making problem for decision optimization. The Analytic Hierarchy Process model and the Analytic Hierarchy Network model are the fundamental models for solving this problem. The Fuzzy Analytic Hierarchy Process model adds fuzziness to the basic model, while the Double Fuzzy Analytic Hierarchy Process model and Double Fuzzy Analytic Hierarchy Network model make greater use of fuzzy information, enhancing the applicability of the model. However, researchers have encountered issues such as the abuse of the Analytic Hierarchy Process model, failure to fully utilize fuzzy advantages, and mismatched problem characteristics in the process of evaluating experimental identification capabilities. To address the above issues, a method for selecting an experimental identification capability evaluation model is proposed based on an improved plant growth algorithm, which includes a target layer, a criterion layer, a key factor layer, and a scheme layer. The research content has important reference value for evaluating experimental identification capabilities.