A GREY WOLF ALGORITHM FOR INDEX OPTIMIZATION IN RELATIONAL DATABASES

Optimizing indexes in relational databases is crucial for enhancing information system performance, productivity, and decision-making. Indexes are structures that expedite data retrieval, offering swift access to records. Optimization yields faster data access, reducing query delays and enhancing application responsiveness, crucial in competitive enterprise settings. It also eases server load and resource use, resulting in long-term cost savings. Proper index management preserves database integrity, ensuring accurate, consistent data retrieval. Poor design can lead to locking and performance issues, affecting system reliability. The Grey Wolf Optimization (GWO) algorithm, introduced by Seyedali Mirjalili in 2014, is a metaheuristic inspired by grey wolf social behavior. It aids in solving optimization problems, including data management. This algorithm models the social hierarchy and hunting dynamics of grey wolf packs, including four wolf types: alpha, beta, delta, and omega, representing the pack's dominant individuals. This paper presents a GWO algorithm to solve the optimization problem in the indexing of large relational databases.