M. Laguna
March 1997
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. Problem-specific 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.
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