Focuses on solving large combinatorial optimization problems. Metaheuristic search aims to find a "very good" solution that satisfies the problem constraints. Describes multiple metaheuristic search methods such as simulated annealing (SA), tabu search (TS), genetic algorithms (GA), particle swarm optimization (PSO), and multiobjective methods. Uses algorithms to find values of discrete and/or continuous variables that optimize a system’s performance. Discusses the application of metaheuristics to a variety of different problems, including hub location allocation, parallel machine scheduling, travelling salesman problem (TSP), curve fitting, clustering, n-queen, min one, etc. Incorporates practical experiments to demonstrate the advantages and disadvantages of metaheuristic search methods for different applications.