In combinatorial optimization, the goal is to find an optimal solution, according to some objective function, from a discrete search space. These problems arise widely in industry and academia and, unfortunately, many of them are NP-hard and no polynomial time algorithm can guarantee their solution to a certified optimality unless . Therefore, in the last decades researchers have investigated the use of stochastic search algorithms to find near optimal solutions to these problems. In particular, great research efforts have been devoted to the development and application of metaheuristic algorithms to solve combinatorial optimization problems.
This special issue contains six high-quality articles addressing practical applications and theoretical developments of metaheuristic algorithms in the context of combinatorial optimization problems. The articles in this issue have been selected from among 23 submissions after a thorough peer review process. Their contents, outlined in the next paragraphs, reflect the diversity of the application domains...