Heuristic Algorithm

Heuristic algorithms are approximate problem-solving methods designed to find good, but not necessarily optimal, solutions efficiently, particularly for computationally complex problems like those in operations research and combinatorial optimization. Current research emphasizes improving the efficiency and solution quality of these algorithms through techniques such as deep reinforcement learning, contrastive learning, and the integration of machine learning models to predict or guide the search process, often within frameworks like large neighborhood search or memetic algorithms. These advancements are impacting diverse fields, enabling faster and more effective solutions for problems in logistics, resource allocation, and scientific computing, where finding optimal solutions is often intractable.