This chapter examines Tabu Search (TS), a powerful metaheuristic that enhances local search with adaptive memory structures. TS overcomes limitations of simpler optimization methods by using memory to avoid cycling and escape local optima. The chapter begins with fundamental concepts of short-term memory and tabu lists. It then explores advanced features including aspiration criteria, intensification strategies, and diversification mechanisms. Implementation details focus on efficient data structures and practical applications in vehicle routing, job scheduling, and network design. Python code examples demonstrate both basic implementations.