<span>Biogeography can be broken down into bio and geography, which would imply the geography, i.e., the dispersion of biological organisms. The entire field of biology inspired algorithm is inclined towards providing the most optimal solution for a given problem set. Computer science experts want to always learn from the surroundings. Nature is sporadic and spontaneous and the erratic nature of a habitat is the very differentiating factor between a real world and an ideal world problem. Things change and that nothing remains constant. The diversification of a certain habitat is bound to change through external influences, some for the better, and some for the worse. This paper tries to mimic the natural influences in a habitat in a python environment and try to come up with a minimal objective value after iterating through the given metaheuristic algorithm.</span>

Text keywords have huge variance and to bridge the gap between the country business segment which provides negligible information and the keywords that have a huge longtail it is imperative for us to categorize the queries that provide a middle ground and also serve a few other purposes. The paper will present those in-depth. Query categorization falls into the segment of 'Multi-Class Classification' in the domain of natural language processing (NLP). However, business requirements require the implementation of any technique that could provide as accurate results as possible. So, to solve this problem the paper discusses an amalgamation of approaches like TF-IDF (term frequency-inverse document frequency), neural networks, cosine similarity, transformers-all of which fix specific issues.