A Comparison of Particle Swarm Optimization and its Normalized Variant Information retrieval from search engine using normalized variant PSO

Abstract

Particle Swarm Optimization is a metaheuristic optimization algorithm inspired by the social behavior of bird flocks and fish schools, which has been widely used to solve complex optimization problems in various fields, including engineering, science, and technology. Over the years, numerous variants of the PSO algorithm have been proposed to address specific limitations or improve the performance of the original algorithm. This paper focuses on a different approach for efficiently mining and searching web content to provide users with meaningful results. We plan to apply support vector machine techniques and a variant of the Particle Swarm Optimization algorithm for web content search and retrieval, aiming to deliver efficient and high-quality outcomes. Here, we compare the results of the standard PSO method with the variant PSO technique and analyze the quality of the results in terms of accuracy. The normalized particle swarm optimization algorithm demonstrates superior performance in terms of accuracy compared to the standard particle swarm optimization approach.