Leveraging Local Optima Network Properties for Memetic Differential Evolution
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.
KeywordsGlobal 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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