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Metaheuristics: Heuristic Techniques for Combinatorial Optimization Problems
Abstract
Decision support systems (DSSs) provide modern solution techniques that help the decision maker to find the best solution to a problem. These embedded solution techniques include and combine, but are not limited to, simulation, exact optimization methods, and heuristics. Especially in the field of heuristics, recent advances in metaheuristic methods have proved to be remarkably effective so that metaheuristics are nowadays the preferred way for solving many types of complex problems, particularly those of combinatorial nature. Some of these problems are, for example, the well-known “traveling salesman” problem, the generalized assignment problem, the set-covering problem, and vehicle and network routing applications. Most of all, metaheuristics allow us to solve real-world problems with a notably high level of complexity. This is where exact methods are often incapable of finding solutions whose qualities are close to that obtained by the leading metaheuristics. Metaheuristic applications with world-class performance can be found in all kinds of areas such as economics, engineering, and natural sciences.
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