Enhanced Artificial Bee Colony Algorithm with Pretrained Model Functional Weight and Modified Selection Strategy for Text Classification

Previous works have proposed various techniques to address the premature convergence problem, where candidate solutions get trapped in local optima instead of reaching the global optimum.This has been tackled using different selection methods in metaheuristic search algorithms.However, while much of the literature focuses on either the search operators or the creation of algorithm variants, research indicates that the effectiveness of the search procedure depends on both the search operators and the selection methods.Incorporating problem-specific functional weights enhances dynamic adaptation to data patterns, reflects data relevance, and improves generalization.This paper offers an enhanced Artificial Bee Colony algorithm including functional weights and a modified selection strategy (ABC-FWMSS) to prioritize features, aiming to achieve an optimal solution and a dynamic balance between exploration and exploitation.The exploration ability of the Artificial Bee Colony is enhanced using pretrained model functional weights during the employed bee phase, while its exploitative capabilities are boosted using tournament selection and employed bee index during the onlooker bee phase.This approach dynamically balances exploration and exploitation.The proposed method achieved 96% precision on the 20 Newsgroups dataset, with the highest fitness score and a 48.8% drop in the number of selected features.