The rapid development of the internet and web publishing techniques create numerous information sources published as HTML pages on World Wide Web. WWW is now a popular medium by which people all around the world can spread and gather the information of all kinds. The importance of these Web-specific features and algorithms, describe the state-of-the-art practices, and the following hypothesis. This work is for a better description of Web page classification problem. Since Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, which simulates the flash patterns and characteristics of fireflies. Clustering is a popular data analysis technique to identify homogeneous groups of objects based on the values of their attributes. Here is used for clustering on benchmarks which is more suitable than Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and other nine methods used. The webpage optimization using Naïve Bayes classifier is an improved optimized web page classification using firefly algorithm with NB classifier. The inclusion of Naïve Bayes is an expert in the field of firefighting. Current classification techniques use word consistency and grouping techniques for classifying web pages. These Techniques use an ad hoc approach to review and reconcile whole keywords on a website for classification. These methods are effective, but not without problems like slow Processing, word meaning differences, poor identification of sentences also disregard the homonymy of the words. Hence this work is better, in the accuracy, precision, etc. parameters with respect to existing concepts.

Accuracy, Artificial Bee, Classification, Clustering, Colony, Firefly, Features, Homogeneous, HTML, Information, Optimization, Precision, Web, etc

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