Ice-Inspired Algorithm Gets Smarter, Boosting Monkeypox Prediction Accuracy

Optimization algorithms are the quiet workhorses of modern data science, quietly searching vast mathematical landscapes for the best possible solutions to problems that would overwhelm human intuition. When those landscapes grow to thousands of dimensions, as they routinely do in genomics, medical imaging, and large-scale sensor networks, even the most cleverly designed search strategies can stall. A team of researchers led by Mohamed Elhosseny of the University of Sharjah and Mansoura University, working with colleagues at Princess Nourah bint Abdulrahman University and Hokkaido University, now reports a substantial upgrade to one of the more poetic entries in the optimization toolbox: an algorithm that mimics the way rime ice forms on surfaces. Their enhanced version, called AIRIME, is described in the Journal of Big Data and demonstrates both stronger benchmark performance and a striking real-world payoff in the classification of monkeypox, achieving a reported accuracy of 95.52 percent.

The original RIME algorithm belongs to the family of physics-based metaheuristics, computational techniques that borrow metaphors from natural phenomena to guide a population of candidate solutions through a search space. In RIME, each candidate solution is treated conceptually as a droplet that can crystallize into either soft rime or hard rime, two forms of ice that deposit differently on surfaces depending on environmental conditions. Soft rime corresponds to broad, exploratory movement across the search space, while hard rime corresponds to fine-grained refinement around promising regions. By balancing these two crystallization behaviors, RIME aims to avoid the twin failure modes of optimization: wandering forever without converging, and converging too quickly on a mediocre solution that merely looks good locally.

That balance, however, is exactly where the original algorithm struggled. According to the authors, RIME suffers from limited exploratory capability, slow convergence, and an imbalance between exploration and exploitation, the technical terms for searching widely versus digging deeply. In high-dimensional big data problems, these weaknesses compound: a search that explores too timidly may never reach the region containing the true optimum, while one that exploits too eagerly becomes trapped in local optima that are far from the global best. For feature selection tasks, where an algorithm must decide which subset of potentially thousands of variables actually matters for a prediction, such failures translate directly into bloated models, longer training times, and degraded accuracy.

AIRIME addresses these shortcomings through four coordinated modifications, each targeting a distinct stage of the search. The first, Adaptive k-Average-Best Mutation (AKAB), introduces a mutation mechanism that draws on the average of the k best solutions found so far, with the parameter k adapting over the course of the run. By anchoring mutations to a consensus of strong candidates rather than a single best individual, the strategy reduces the risk of the search collapsing prematurely around one lucky solution. The second enhancement, the Adaptive Searching Process (ASP), dynamically adjusts how the population moves through the search space as iterations progress, shifting the emphasis from broad exploration early on to focused exploitation later, which the authors identify as key to accelerating convergence without sacrificing coverage.

The remaining two mechanisms concentrate on solution quality itself. Enhanced Solution Quality (ESQ) applies refinement steps designed to improve the fitness of individual candidates between generations, effectively polishing intermediate results rather than waiting for the population-level dynamics to do all the work. Gaussian-Based Optimal Solution Mutation (GBOM) then perturbs the current best solution using Gaussian-distributed random variations, a technique that exploits the well-behaved statistical properties of the normal distribution to generate perturbations that are usually small but occasionally large. This gives the algorithm a principled way to escape local optima around the incumbent best solution while still refining it most of the time. Together, the four strategies form an adaptive wrapper around the core rime-ice dynamics, allowing the algorithm to tune its own behavior to the problem at hand rather than relying on fixed parameters chosen by trial and error.

Benchmarking a new metaheuristic credibly requires more than a handful of test functions, and the team put AIRIME through an unusually thorough evaluation. The algorithm was tested on 41 functions drawn from the CEC2017 and CEC2022 test suites, standardized collections of numerical optimization problems designed by the evolutionary computation community specifically to expose weaknesses in search algorithms. These suites include shifted, rotated, and hybrid composition functions that defeat algorithms tuned to simple convex landscapes. AIRIME was compared against a range of classical, recent, and advanced competitors, and the results were subjected to formal statistical scrutiny. Friedman rank tests, which rank algorithms across all test functions and assess whether the rankings differ significantly, and Wilcoxon rank-sum tests, which compare pairwise performance, both validated the algorithm’s superiority and robustness. Sensitivity analyses further examined how each of the four proposed strategies contributed to the overall result, demonstrating the adaptability and efficiency of the individual components rather than attributing success to the ensemble as an undifferentiated whole.

Optimization benchmarks measure performance on continuous mathematical functions, but many of the most consequential applications are discrete. To bridge that gap, the researchers developed a binary version of AIRIME, in which candidate solutions take the form of binary vectors where each bit indicates whether a particular feature should be included or excluded from a predictive model. This binary variant was applied to feature selection tasks across 14 datasets, a scale sufficient to reveal how the algorithm behaves on problems of genuinely different structure and dimensionality. Across these tasks, the binary AIRIME achieved promising results compared with various established binary metaheuristics, the specialized algorithms that practitioners typically reach for when wrapping optimization around classification pipelines. Feature selection matters enormously in practice: removing irrelevant or redundant variables reduces overfitting, cuts computational cost, and often improves the interpretability of the resulting model, a consideration that is especially acute in clinical settings where physicians need to understand why a model produces its predictions.

The most eye-catching demonstration came in the healthcare domain. The researchers embedded AIRIME within a novel Monkeypox disease prediction framework, using the algorithm to select the most informative features from high-dimensional data before feeding them to a classifier. Monkeypox, a viral disease that gained global attention during the 2022 outbreak, presents diagnostic challenges in which rapid, accurate computational triage tools could support clinical decision-making, particularly in settings with limited laboratory capacity. In this framework, AIRIME’s feature selection distilled the input data down to a significantly smaller set of variables while the overall pipeline achieved a classification accuracy of 95.52 percent. The combination of high accuracy and aggressive feature reduction is the crucial point: it suggests the algorithm identified a compact, information-dense signature within the data rather than simply memorizing a large number of weakly informative inputs. For high-dimensional healthcare data, where the ratio of variables to samples is often unfavorable, that efficiency is precisely what determines whether a model generalizes beyond its training set.

The authors frame AIRIME as a significant advancement in big data-driven optimization, combining scalability, adaptability, and robustness to address complex and high-dimensional challenges across domains that extend well beyond healthcare. The design philosophy on display here reflects a broader trend in computational intelligence: rather than inventing entirely new metaphors, researchers are increasingly investing in adaptive mechanisms that let existing algorithms self-tune across problem types. The four AIRIME strategies, mutation anchored to top-k averages, adaptive search dynamics, explicit solution refinement, and Gaussian perturbation of the best solution, are each individually well-motivated, and the sensitivity analyses suggest their benefits are separable and transferable. That modularity could make the enhancements attractive to researchers working with other population-based optimizers.

Caveats remain, as they do with any newly proposed metaheuristic. Benchmark suites, however demanding, are proxies for real problems, and the monkeypox result, while impressive, comes from a single application whose clinical value will depend on prospective validation with independent patient data. The algorithm’s performance across other discrete and mixed-integer problem classes remains to be mapped. Still, the study offers a methodologically careful case: standardized benchmarks, statistical hypothesis testing, sensitivity analysis, and a concrete application demonstrating end-to-end value. Published open access in the Journal of Big Data, the work gives the optimization community both a stronger tool and a template for how adaptive enhancements to nature-inspired algorithms can be evaluated rigorously. If the rime-ice metaphor continues to mature along this trajectory, the frost forming on the surfaces of high-dimensional search spaces may prove to be one of the more productive crystallizations in the field.

Subject of Research: An adaptive improved rime-ice optimization algorithm for feature selection and monkeypox disease prediction

Article Title: AIRIME: a scalable adaptive RIME algorithm for feature optimization and data-driven monkeypox disease prediction

Article References: Elhosseny, M., Abdel-salam, M., Khafaga, D. S., Aldakheel, E. A., Zhong, R., & El-Hasnony, I. M. (2026). AIRIME: a scalable adaptive RIME algorithm for feature optimization and data-driven monkeypox disease prediction. Journal of Big Data. https://doi.org/10.1186/s40537-026-01482-w

Image Credits: AI Generated

DOI: 10.1186/s40537-026-01482-w

Keywords: AIRIME, RIME algorithm, metaheuristics, global optimization, feature selection, big data, machine learning, monkeypox, disease prediction, CEC2017, CEC2022, computational intelligence