Leveraging Business-Inspired Computational Intelligence Techniques for Enhanced Data Analytics: Applications of Genetic Algorithms, Fuzzy Logic, and Swarm Intelligence

Data has become a crucial element for contemporary enterprises; however, deriving practical insights from its immense volume remains an intricate obstacle. This paper examines the capabilities of three bio- inspired computational intelligence (CI) methods - Genetic Algorithms (GAs), Fuzzy Logic (FL), and Swarm Intelligence (SI) - in improving data analytics for business optimization and decision-making. The researcher thoroughly examines the fundamental principles of each technique, emphasizing their inherent advantages and appropriateness for addressing practical business challenges. By reviewing recent research and real-world examples, the researcher illustrates how Genetic Algorithms (GAs) can enhance the efficiency of resource allocation, Fuzzy Logic (FL) can effectively handle uncertainty in risk assessment, and Swarm Intelligence (SI) can streamline logistics and scheduling processes. In conclusion, highlight the synergistic and hybrid methods emerging in this field. These approaches are leading to enhanced value extraction from data and pushing the limits of business intelligence. Keywords:- Data Analytics, Business Intelligence, Genetic Algorithms, Fuzzy Logic, Swarm Intelligence, Optimization, Enterprise Decision-Making, Case Studies.