TY - CHAP
T1 - Architecture and Design of the HeuristicLab Optimization Environment
AU - Wagner, Stefan
AU - Kronberger, Gabriel
AU - Beham, Andreas
AU - Kommenda, Michael
AU - Scheibenpflug, Andreas
AU - Pitzer, Erik
AU - Vonolfen, Stefan
AU - Kofler, Monika
AU - Winkler, Stephan
AU - Dorfer, Viktoria
AU - Affenzeller, Michael
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - HeuristicLab
KW - Heuristic Optimization Software Systems
KW - HeuristicLab
KW - Heuristic Optimization Software Systems
U2 - 10.1007/978-3-319-01436-4_10
DO - 10.1007/978-3-319-01436-4_10
M3 - Chapter
SN - 978-3-319-01435-7
SP - 197
EP - 261
BT - Advanced Methods and Applications in Computational Intelligence
PB - Springer
ER -