Metaheuristics
Metaheuristics are high-level problem-solving strategies that guide the search for optimal or near-optimal solutions in complex optimization problems, often where exact methods are computationally infeasible. They provide general frameworks that can be adapted to various domains, such as scheduling, routing, or machine learning, by exploring solution spaces efficiently without guaranteeing global optimality. Common examples include genetic algorithms, simulated annealing, and ant colony optimization.
Developers should learn metaheuristics when tackling NP-hard or large-scale optimization problems where traditional algorithms fail due to time or resource constraints, such as in logistics, finance, or artificial intelligence applications. They are particularly useful for finding good-enough solutions quickly in scenarios like vehicle routing, portfolio optimization, or hyperparameter tuning in machine learning, where exact solutions are impractical.