This chapter explores metaheuristics as effective strategies for single-objective optimization problems. These strategies are useful for complex, black-box, or non-convex problems where traditional exact methods may be impractical or become stuck in local optima. This chapter introduces and explains popular metaheuristics, starting with simulated annealing, Tabu search, and variable neighborhood search. It then explores population-based approaches, such as evolutionary algorithms. Techniques covered include genetic algorithms, evolution strategies, and differential evolution. Swarm intelligence techniques, such as particle swarm optimization and ant colony optimization, are also explored. The goal is to provide a clear understanding of the concepts behind each technique. Consequently, for each strategy, we explain its core principles, mechanisms, and key parameters. Given their complexity, analogies are often used to balance technical depth with a conceptual overview.