Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
