Metaheuristic Optimization-Driven Information Management System for Enterprise Intelligence

Enterprise intelligence systems are finding it more difficult to deal with large-scale, diverse, information with dynamic operation conditions causing delay in insights and optimum decision support. To respond to these problems, the paper will recommend a new adaptive information intelligence framework that will integrate metaheuristic optimization into the information management enterprise lifecycle. This approach is called the Evolutionary Intelligence Regulation Mechanism (EIRM), and it brings in an automated control sphere to self-optimize in the choice of data relevancy, merging of knowledge and propagation of decisions within business settings. In contrast to traditional methods, which are based on a few rules fixed or separate optimization phases, EIRM develops enterprise intelligence management as a high-dimensional dynamic search problem, which allows autonomous reconfiguring in reaction to changes in the context and changing the organizations purposes. The structure combines the multi-source data integration and adaptive normalization and semantic relevance analysis to make sure only the information which is strategically questionable gets into the creation of intelligence. The evolutionary strategy is population-based and aims at exploring and refining the configurations of intelligence, tradeoffs between global adaptability and stability on the local scale. The proposed system is a tight relationship between optimization and management paradigms by which one can optimally contribute to the responsiveness of the decision-making process, decrease informational redundancy, and efficiencies of operations, without the additional computational load. The abstracted architecture of EIRM guarantees scalability, resilience to uncertainty, and easy integration with the existing enterprise infrastructures, and thus is viable as a solution to next generation enterprise intelligence infrastructures, which are in complex and data-heavy environments. The proposed solution attains an overall accuracy of 95.8 %, signifying exceptionally reliable enterprise intelligence creation across various operational scenarios.