programming, branch and cut, semidefinite programming, and constraint programming * Metaheuristics, including local search, tabu search, simulated annealing, scatter search, GRASP, variable neighborhood search, ant colonies, genetic programming, evolution strategies, and genetic algorithms * Hybrid approaches, combining exact algorithms and metaheuristics * Multi-objective optimization algorithms The text not only presents parallel algorithms and applications, but also software frameworks and libraries that integrate parallel algorithms for combinatorial optimization. Among the well-known parallel and distributed frameworks covered are COIN, ParadisEO, BOB++, MW, and SDPARA. Numerous real-world examples of problems and solutions demonstrate how parallel combinatorial optimization is applied in such fields as telecommunications, logistics, genomics, networking, and transportation. Whether you are a practicing engineer, field researcher, or student, this text provides you with not only the theory of parallel combinatorial optimization, but the guidance and practical tools to solve complex problems using powerful algorithms.