Abstract
The results of evolutionary algorithms depends on population diversity that normally decreases by increasing the selection pressure from generation to generation. Usually, this can lead evolution process to get stuck in local optima. The study is focused on mechanisms to avoid this undesired phenomenon by introducing parallel differential evolution that decompose a monolithic population into more variable-sized sub-populations, which evolve independently of each other. The proposed parallel algorithm operates with individuals having some characteristics of agents, e.g., they act autonomously by selecting actions, with which they affect the state of environment. This incorporates two additional mechanisms: aging, and adaptive population growth, which direct the decision-making by individuals. The proposed parallel differential evolution was applied to the CEC’18 benchmark function suite, while the produced results were compared with some traditional stochastic nature-inspired population-based and state-of-the-art algorithms.
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Acknowledgment
Iztok Fister thanks the financial support from the Slovenian Research Agency (Research Core Funding No. P2-0042 - Digital twin). Iztok Fister Jr. thanks the financial support from the Slovenian Research Agency (Research Core Funding No. P2-0057). Andres Iglesias and Akemi Galvez thank the Computer Science National Program of the Spanish Research Agency and European Funds, Project #TIN2017-89275-R. (AEI/FEDER, UE), and the PDE-GIR project of the European Union’s Horizon 2020 programme, Marie Sklodowska-Curie Actions grant agreement #778035. Dušan Fister thanks the financial support from the Slovenian Research Agency (Research Core Funding No. P5-0027).
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Fister, I., Iglesias, A., Galvez, A., Fister, D., Fister, I. (2021). Parallel Differential Evolution with Variable Population Size for Global Optimization. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_9
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