Leveraging Local Optima Network Properties for Memetic Differential Evolution

  • Viktor Homolya
  • Tamás VinkóEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)


Population based global optimization methods can be extended by properly defined networks in order to explore the structure of the search space, to describe how the method performed on a given problem and to inform the optimization algorithm so that it can be more efficient. The memetic differential evolution (MDE) algorithm using local optima network (LON) is investigated for these aspects. Firstly, we report the performance of the classical variants of differential evolution applied for MDE, including the structural properties of the resulting LONs. Secondly, a new restarting rule is proposed, which aims at avoiding early convergence and it uses the LON which is built-up during the evolutionary search of MDE. Finally, we show the promising results of this new rule, which contributes to the efforts of combining optimization methods with network science.


Global optimization Memetic differential evolution Local optima network Network science 



This research has been partially supported by the project “Integrated program for training new generation of scientists in the fields of computer science”, no EFOP-3.6.3-VEKOP-16-2017-0002. The project has been supported by the European Union and co-funded by the European Social Fund. Ministry of Human Capacities, Hungary grant 20391-3/2018/FEKUSTRAT is acknowledged.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computational OptimizationUniversity of SzegedSzegedHungary

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