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Heterogeneous Evolutionary Swarms with Partial Redundancy Solving Multi-objective Tasks

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Evolutionary Multi-Criterion Optimization (EMO 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10173))

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Abstract

Consider a self-organized system of heterogeneous reconfigurable agents solving a multi-objective task. In this paper we analyze an evolutionary approach to make such a system adaptable. In principle, this system is comparable to a multi-objective genetic algorithm, however, requires asynchronous generations and decentralized evaluation- and selection processes. The primary objective of this paper is to introduce the proposed system, to provide several interesting theoretic properties and a primary experimental analysis. The heritable material (genes) compromises a parameter set that encodes an agents configuration and can be communicated between agents. We introduce partial redundancy into the system by supplying a certain number of agents with two parameter sets instead of one. These agents are denoted as redundant and are free to chose which of their two parameter sets is applied. A special focus lies on two strategies for the agents to derive a fitness value based on their property set(s) and the respective objective functions of the multi-objective task suitable for decentralized systems. A slightly more sophisticated approach with weights for each of the objectives performs just as good as a simple method where agents pick the best or respectively worst objective value. The results show that systems with low redundancy tend to lose a lot of diversity, however, redundant systems are slower in their adaptive process.

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Authors and Affiliations

  1. Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

    Ruby L. V. Moritz & Sanaz Mostaghim

Authors
  1. Ruby L. V. Moritz
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  2. Sanaz Mostaghim
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Corresponding author

Correspondence to Ruby L. V. Moritz .

Editor information

Editors and Affiliations

  1. University of Münster, Münster, Germany

    Heike Trautmann

  2. TU Dortmund University, Dortmund, Germany

    Günter Rudolph

  3. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  4. CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

  5. Clemson University, Clemson, South Carolina, USA

    Margaret Wiecek

  6. University of Surrey, Guildford, United Kingdom

    Yaochu Jin

  7. University of Münster, Münster, Germany

    Christian Grimme

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Moritz, R.L.V., Mostaghim, S. (2017). Heterogeneous Evolutionary Swarms with Partial Redundancy Solving Multi-objective Tasks. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_31

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  • DOI: https://doi.org/10.1007/978-3-319-54157-0_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54156-3

  • Online ISBN: 978-3-319-54157-0

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