Comparative Study ofSearch Engine Optimization Algorithms On Retrieval Time andPrecise Data Entry for Websites

Abstract

A search engine is a complex software in that a finder visits numerous websites and their pages to find essential data. It is the primary source to find content on the World Wide Web. Search Engine Optimization (SEO) methods have been invented to make user searching smoother. Although search engines are intelligent, sometimes they also provide irrelevant data. As a result, researchers have found a solution to this problem by implementing SEO techniques. SEO techniques make websites more visible and produce organic search results. Thus, this study proposed the implementation of Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) for SEO problems on online shopping websites and educational websites. These algorithms are evaluated based on retrieval time and precise data entry by using precision and recall. Moreover, PageSpeed Insights is used to check the speed index of the websites. The research outcome found that PSO outperformed ANN for both shopping and educational websites. PSO has a minimal retrieval time, which is 0.04 seconds for an online shopping website and 0.10 seconds for an educational website. As for precision and recall, online shopping websites have been proven to have the highest precision score of 0.67 and 0.63 recall score. The simulation analysis results show thatfuture researchers should concentrate more on determining the significance of each SEO approach and the best blend for various sectors.