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Metaheuristic Optimization

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Abstract

Economic success frequently depends on a company’s ability to rapidly identify market changes and to adapt to them. Making optimal decisions within tight time constraints and under consideration of influential factors is one of the most challenging tasks in industry and applied computer science. Gaining expertise in solving optimization problems can therefore significantly increase efficiency and profitability of a company and lead to a competitive advantage. Unfortunately, many real-world optimization problems are notoriously difficult to solve due to their high complexity. For example, in the context of combinatorial optimization (where the search space tends to grow exponentially) or in nonlinear system identification (especially if no a-priori knowledge about the kind of nonlinearity is available) such applications are frequently found. Exact mathematical methods cannot solve these problems in relevant dimensions within reasonable time.

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  1. Heuristic and Evolutionary Algorithms Laboratory, Upper Austria University of Applied Sciences, Hagenberg, Austria

    Michael Affenzeller, Andreas Beham, Monika Kofler, Gabriel Kronberger, Stefan A. Wagner & Stephan Winkler

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    Bruno Buchberger , Michael Affenzeller , Alois Ferscha , Michael Haller , Tudor Jebelean , Erich Peter Klement , Peter Paule , Gustav Pomberger , Wolfgang Schreiner , Robert Stubenrauch , Roland Wagner , Gerhard Weiß  & Wolfgang Windsteiger , , , , , , , , , , ,  & 

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Affenzeller, M., Beham, A., Kofler, M., Kronberger, G., Wagner, S., Winkler, S. (2009). Metaheuristic Optimization. In: Buchberger, B., et al. Hagenberg Research. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02127-5_4

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