A Distributed Modular Scalable and Generic Framework for Parallelizing Population-Based Metaheuristics

  • Hatem KhalloofEmail author
  • Phil Ostheimer
  • Wilfried Jakob
  • Shadi Shahoud
  • Clemens Duepmeier
  • Veit Hagenmeyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12043)


In the present paper, microservices, container virtualization and the publish/subscribe messaging paradigm are exploited to develop a distributed, modular, scalable and generic framework for parallelizing population-based metaheuristics. The proposed approach paves the way for an easy deployment of existing metaheuristic algorithms such as Evolutionary Algorithms (EAs) on a scalable runtime environment with full runtime automation. Furthermore, it introduces simple mechanisms to work efficiently with other components like forecasting frameworks and simulators. In order to analyze the feasibility of the design, the EA GLEAM (General Learning Evolutionary Algorithm and Method) is integrated and deployed on a cluster with 4 nodes and 128 cores for benchmarking. The overhead of the framework is measured and the obtained results show not only low values but also a small increase with growing number of computing nodes.


Parallel EAs Microservices Virtualization Container Cluster Parallel computing Scalability Coarse-Grained Model 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hatem Khalloof
    • 1
    Email author
  • Phil Ostheimer
    • 1
  • Wilfried Jakob
    • 1
  • Shadi Shahoud
    • 1
  • Clemens Duepmeier
    • 1
  • Veit Hagenmeyer
    • 1
  1. 1.Institute of Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT)KarlsruheGermany

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