RLDMGO: a reinforcement learning-driven hybrid framework for complex optimization problems

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...

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

Experiments show that RLDOS achieves competitive and often improved performance compared with several well-known binary optimizers in terms of classification accuracy, number of selected features, and fitness value.

AutoPSO: A Meta-Framework for Automated Particle Swarm Optimization

Comprehensive experiments on numerical benchmarks and neuroevolution robotic control tasks demonstrate that AutoPSO consistently discovers novel PSO variants that significantly outperform strong baselines and confirm that AutoPSO achieves increasing performance gains with larger swarm sizes.

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

Quality-Diversity Reinforcement Learning using Behavior Regulated Policy Gradient

BRPG-MAP-Elites is introduced, a new QD-RL algorithm adopting a strictly Markovian actor-critic architecture within the MAP-Elites framework that demonstrates a 43% improvement in average QD-scores over DCRL-MAP-Elites and achieves higher robustness in the generated policies.

MLDGWO: a grey wolf optimizer with momentum, leader adjustment, and differential perturbation for global optimization problems

Applied to five engineering optimization problems, modified momentum–leader–differential grey wolf optimizer consistently achieves the lowest objective values, while analytically proving its ability to navigate heavily penalized boundaries and satisfy all constraints.