A FOA-Optimized RBF Algorithm-Based Evaluation Research on E-commerce Websites

By applying the Expert Grading Method and considering the features of e-commerce websites and properties of the indicators, this paper constructs a multi-indicator hierarchical structure for the competitiveness index evaluation of e-commerce website as well as built an indicator system for the evaluation. This system can be used to measure the competitiveness index of such a website and quantify its competitiveness. Then Radial Basis Function (RBF) Neural Network Algorithm (NNA) is adopted to evaluate and research the competitiveness indexes of e-commerce websites. Against the problems therein, this paper tries to improve the RBF NNA with Fruit Fly Optimization Algorithm (FOA). Through the simulation and contrast of examples, FOA–RBF algorithm obviously works better than RBF NNA in measuring and evaluating the competitiveness indexes of such websites. Therefore, it is verified that the algorithm proposed by this paper is both effective and reliable.