Chapter

Advanced Methods and Applications in Computational Intelligence

Volume 6 of the series Topics in Intelligent Engineering and Informatics pp 197-261

Architecture and Design of the HeuristicLab Optimization Environment

  • S. WagnerAffiliated withHeuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communication and Media, University of Applied Sciences Upper Austria Email author 
  • , G. KronbergerAffiliated withHeuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communication and Media, University of Applied Sciences Upper Austria
  • , A. BehamAffiliated withHeuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communication and Media, University of Applied Sciences Upper Austria
  • , M. KommendaAffiliated withHeuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communication and Media, University of Applied Sciences Upper Austria
  • , A. ScheibenpflugAffiliated withHeuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communication and Media, University of Applied Sciences Upper Austria
  • , E. PitzerAffiliated withHeuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communication and Media, University of Applied Sciences Upper Austria
  • , S. VonolfenAffiliated withHeuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communication and Media, University of Applied Sciences Upper Austria
  • , M. KoflerAffiliated withHeuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communication and Media, University of Applied Sciences Upper Austria
  • , S. WinklerAffiliated withHeuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communication and Media, University of Applied Sciences Upper Austria
    • , V. DorferAffiliated withHeuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communication and Media, University of Applied Sciences Upper Austria
    • , M. AffenzellerAffiliated withHeuristic and Evolutionary Algorithms Laboratory, School of Informatics, Communication and Media, University of Applied Sciences Upper Austria

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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.