Hybrid-Box Tool for Grey Wolf Optimization for Preference Ranking

Optimizing data instances play a crucial role in dealing with ranking problems. In scenarios such as ranking instances in medical diagnosis, search engine optimization, and information retrieval, there is a need for models that can rank data instances based on the significance of their features within the datasets. This paper provides a hybrid-box tool which is an efficient Grey Wolf Optimizer technique that generates ranking models by utilizing training and validation datasets. These models are then assessed using unseen test data to produce the predictive results. This tool uses a Grey Wolf Optimizer with unified lower-bound and upper-bound parameters. Furthermore, it uses seven objective functions besides the wolves' genes and their updated bounds are hybridized by six probability distributions as random number generators. This tool is a novel hybrid-box tool in the continuous optimization research domain for ranking data instances. Regardless of the hidden details during the evolving procedure, it provides the best-evolved ranking model at the end of each run. This makes the tool is hybrid between Black and White Box tools.