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Architecture and Design of the HeuristicLab Optimization Environment

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Advanced Methods and Applications in Computational Intelligence

Part of the book series: Topics in Intelligent Engineering and Informatics ((TIEI,volume 6))

Abstract

Many optimization problems cannot be solved by classical mathematical optimization techniques due to their complexity and the size of the solution space. In order to achieve solutions of high quality though, heuristic optimization algorithms are frequently used. These algorithms do not claim to find global optimal solutions, but offer a reasonable tradeoff between runtime and solution quality and are therefore especially suitable for practical applications. In the last decades the success of heuristic optimization techniques in many different problem domains encouraged the development of a broad variety of optimization paradigms which often use natural processes as a source of inspiration (as for example evolutionary algorithms, simulated annealing, or ant colony optimization). For the development and application of heuristic optimization algorithms in science and industry, mature, flexible and usable software systems are required. These systems have to support scientists in the development of new algorithms and should also enable users to apply different optimization methods on specific problems easily. The architecture and design of such heuristic optimization software systems impose many challenges on developers due to the diversity of algorithms and problems as well as the heterogeneous requirements of the different user groups. In this chapter the authors describe the architecture and design of their optimization environment HeuristicLab which aims to provide a comprehensive system for algorithm development, testing, analysis and generally the application of heuristic optimization methods on complex problems.

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Author information

Authors and Affiliations

  1. Heuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communication and Media, University of Applied Sciences Upper Austria, Softwarepark 11, 4232, Hagenberg, Austria

    S. Wagner, G. Kronberger, A. Beham, M. Kommenda, A. Scheibenpflug, E. Pitzer, S. Vonolfen, M. Kofler, S. Winkler, V. Dorfer & M. Affenzeller

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  1. S. Wagner
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  2. G. Kronberger
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  3. A. Beham
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  4. M. Kommenda
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  5. A. Scheibenpflug
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  6. E. Pitzer
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  7. S. Vonolfen
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  8. M. Kofler
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  9. S. Winkler
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  10. V. Dorfer
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  11. M. Affenzeller
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Correspondence to S. Wagner .

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Editors and Affiliations

  1. Wroclaw University of Technology, Wybrzeze Wyspianskiego Street 27, Wroclaw, 50-370, Poland

    Ryszard Klempous

  2. Wroclaw University of Technology, Wybrzeze Wyspianskiego Street 27, Wroclaw, 50-370, Poland

    Jan Nikodem

  3. of Applied Sciences, Upper Austria University, Softwarepark 11, Hagenberg, 4232, Austria

    Witold Jacak

  4. , Bld 1 Level 22, University of Technology, Broadway, Ultimo/Syd, Sydney, 2007, New South Wales, Australia

    Zenon Chaczko

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Wagner, S. et al. (2014). Architecture and Design of the HeuristicLab Optimization Environment. In: Klempous, R., Nikodem, J., Jacak, W., Chaczko, Z. (eds) Advanced Methods and Applications in Computational Intelligence. Topics in Intelligent Engineering and Informatics, vol 6. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01436-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-01436-4_10

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