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Metaheuristics are a kind of aproximate procedures that have been efectively proved in hard optimization problems mainly by their efficiency, efficacy and flexibility. Metaheuristics can be defined as a master strategy that guides and modifies other heuristics to produce solutions beyond those that are normally generated in a quest for local optimality.
Specialized heuristics were typically developed to solve complex combinatorial optimization problems. With the emergence of more general solution schemes (Glover coined in 1986 the term metaheuristics for
such methods), the picture drastically changed. Now, the challenge is to adapt a metaheuristic to a particular problem or problem class, which usually requires much less work than developing a specialized heuristic from scratch. Furthermore, a good metaheuristic implementation is likely to provide near-
optimal solutions in reasonable computation times. With the advent of increas ingly powerful computers and parallel platforms, metaheuristics have even been successfully applied to real-time problems with stringent response time requirements.
Alternatively, exact methods, such us Branch & Bound, solve hard optimization problems to proven optimality. They usually limit themselves to small and medium sized instances and require longer running times than metaheuristic methods, but they certify the optimality of the obtained solution.
My publications focus on the development of solution procedures for hard optimization problems. I have mainly developed metaheuristic procedures for well-known combinatorial and continuous optimization problems althought I have also worked on exact methods for some selected problems.
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