Meta Heuristic

Metaheuristics are problem-solving techniques that employ intelligent exploration strategies to find near-optimal solutions for complex optimization problems where traditional methods fall short. Current research emphasizes adaptive operator selection, often incorporating machine learning (e.g., reinforcement learning) to dynamically adjust search parameters and improve efficiency, particularly in high-dimensional spaces. These methods are proving valuable across diverse fields, from scheduling and inventory management to deep learning optimization and even enhancing the reliability of machine learning models by addressing uncertainty and improving explainability. The impact lies in enabling efficient solutions for computationally challenging problems in various scientific and engineering domains.