Optimization of Complex Systems with OptQuest

⎯ Optimization of complex systems has been for many years limited to problems that could be formulated as mathematical programming models of linear, nonlinear and integer types. Problemspecific heuristics that do not require a mathematical formulation of the problem have been also used for optimizing complex systems, however, they generally must be developed in a case-by-case basis. Recent research in the area of metaheuristics has placed the ambitious goal of building a general-purpose optimizer within reach. Specifically, population-based metaheuristics have made possible the development of general-purpose optimizers for which the solution process is context independent. Two of the best known population-based metaheuristics are genetic algorithms and scatter search. In this paper we described the implementation and functionality of OptQuest, a general-purpose optimizer that was developed using the scatter search methodology. We illustrate the system’s features with applications in stochastic, nonlinear and combinatorial optimization. 2 Optimization with OptQuest