Leveraging Business-Inspired Computational Intelligence Techniques for Enhanced Data Analytics: Applications of Genetic Algorithms, Fuzzy Logic, and Swarm Intelligence (Global Journal of Computer Science and Technology, 2024)
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 researcherillustrates 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