Opt4J: a modular framework for meta-heuristic optimization

  • Authors:

  • Martin Lukasiewycz;Michael Glaß;Felix Reimann;Jürgen Teich

  • Affiliations:

  • TU Munich, Munich, Germany;University of Erlangen-Nuremberg, Erlangen, Germany;University of Erlangen-Nuremberg, Erlangen, Germany;University of Erlangen-Nuremberg, Erlangen, Germany

  • Venue:
  • Proceedings of the 13th annual conference on Genetic and evolutionary computation
  • Year:
  • 2011

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Abstract

This paper presents a modular framework for meta-heuristic optimization of complex optimization tasks by decomposing them into subtasks that may be designed and developed separately. Since these subtasks are generally correlated, a separate optimization is prohibited and the framework has to be capable of optimizing the subtasks concurrently. For this purpose, a distinction of genetic representation (genotype) and representation of a solution of the optimization problem (phenotype) is imposed. A compositional genotype and appropriate operators enable the separate development and testing of the optimization of subtasks by a strict decoupling. The proposed concept is implemented as open source reference OPT4J [6]. The architecture of this implementation is outlined and design decisions are discussed that enable a maximal decoupling and flexibility. A case study of a complex real-world optimization problem from the automotive domain is introduced. This case study requires the concurrent optimization of several heterogeneous aspects. Exemplary, it is shown how the proposed framework allows to efficiently optimize this complex problem by decomposing it into subtasks that are optimized concurrently.