RLDOS: a novel binary metaheuristic framework with reinforcement learning-driven operator selection for effective feature selection in high-dimensional biomedical data

A hybrid parameter-tuning for adaptive variable-length particle swarm optimisation in cancer feature selection

Overall, VL-APSO-LS provides a robust, adaptive, and statistically validated optimisation framework for high-dimensional biomedical classification and achieves perfect classification accuracy on the Leukaemia dataset.

A Hybrid QPSCO Metaheuristic for Joint Feature Selection and Hyperparameter Tuning in Early Stage Diabetes Prediction

—Type 2 Diabetes Mellitus (T2DM) continues to pose a serious health challenge. Although Machine Learning has been widely employed for disease prediction, its effectiveness depends heavily on feature selection (FS) and hyperparameter tuning (HPT). This study proposes Quantum Particle Swarm + Single Candidate Optimizer (...

METAHEURISTIC HYPERPARAMETER OPTIMIZATION FOR DEEP LEARNING: A COMPARATIVE STUDY

A comparative analysis of two metaheuristic algorithms — Particle Swarm Optimization and Dwarf Mongoose Optimization — as advanced alternatives for hyperparameter tuning in deep learning models trained on the CIFAR-10 dataset reinforces the potential of metaheuristic-based optimization as a robust framework for hyperpa...

QLOA: A Self-Adaptive Q-Learning-Based Optimization Algorithm with Dynamic Mathematical Formulation Selection

Exploratory Landscape Analysis for Meta-Learning-Based Algorithm Selection in Continuous Optimization

Continuous optimization now draws on a large and growing number of competing metaheuristic families, including differential-evolution lineages, covariance-matrix adaptation strategies, swarm-intelligence methods, and simulated annealing, yet no single method dominates across problem classes, a consequence of the No Fre...

A reinforcement-learning-guided memetic Narwhal Optimization Algorithm for global and engineering optimization

The Narwhal Optimization Algorithm is a recent swarm metaheuristic that, like most population-based optimisers, is prone to premature convergence, is sensitive to random initialisation, and relies on a rigid, schedule-driven exploration–exploitation balance. This paper develops and rigorously evaluates two enhanced v...