Soft Computing

, Volume 21, Issue 17, pp 4859–4872 | Cite as

Multi-stage evolution of single- and multi-objective MCLP

Successive placement of charging stations
  • Helge SpiekerEmail author
  • Alexander Hagg
  • Adam Gaier
  • Stefanie Meilinger
  • Alexander Asteroth


Maximal covering location problems have efficiently been solved using evolutionary computation. The multi-stage placement of charging stations for electric cars is an instance of this problem which is addressed in this study. It is particularly challenging, because a final solution is constructed in multiple steps, stations cannot be relocated easily and intermediate solutions should be optimal with respect to certain objectives. This paper is an extended version of work published in Spieker et al. (Innovations in intelligent systems and applications (INISTA), 2015 international symposium on. IEEE, pp 1–7, 2015). In this work, it was shown that through problem decomposition, an incremental genetic algorithm benefits from having multiple intermediate stages. On the other hand, a decremental strategy does not profit from reduced computational complexity. We extend our previous work by including multi-objective optimization of multi-stage charging station placement, allowing us to not only optimize toward (weighted) demand location coverage, but also to include a second objective, taking into account traffic density. It is shown that the reachable part of the full Pareto front at each stage is bound by the solution that was chosen from the respective previous front. By careful choice of the selection strategy, a particular focus can be set. This can be exploited to comply with concrete implementation goals and to adjust the evolved strategy to both static and dynamic changes in requirements.


Genetic algorithm Optimization Maximal covering location problem Multi-stage Single-objective Multi-objective Electric mobility 



The authors would like to thank the ”Arbeitskreis Starthilfekonzepte Elektromobilität Bonn-Rhein-Sieg” for providing data and financing. Namely, we would like to thank the City of Bonn, the county Rhein-Sieg-Kreis and the regional energy infrastructure providers, Energie- und Wasserversorgung Bonn/Rhein-Sieg GmbH, RWE Deutschland AG, Rhein Energie AG, Rhenag—Rheinische Energie AG and Stadtwerke Troisdorf GmbH for sponsoring the underlying project.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Helge Spieker
    • 1
    Email author
  • Alexander Hagg
    • 1
  • Adam Gaier
    • 1
  • Stefanie Meilinger
    • 1
  • Alexander Asteroth
    • 1
  1. 1.University of Applied Sciences Bonn-Rhein-SiegSankt AugustinGermany

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