Constraint optimization is today a de facto practical approach to efficiently solving various types of NP-hard optimization problems arising from real-world settings. This is primarily due to significant advances in practical algorithms and implementation-level techniques, which have resulted in increasingly efficient and robust constraint optimization solvers for a range of NP-hard declarative languages. However, the need for even more efficient and robust constraint optimization solvers continues to grow, as in several contexts more and more complex optimization problems need to be solved.