Abstract
In this paper we examined how the population size affects the performance of the differential evolution algorithm. First, we tested the original differential evolution algorithm, and then the improved self-adaptive differential evolution algorithm, on ten benchmark functions, that have been proposed for the CEC 2019 competition. We used six different population sizes. Afterwards, we tested the newly created algorithm with population reinitialization. The results show that the population size affects the algorithm’s efficiency, and that we need to tune it to obtain the best results. In the paper, we demonstrate that the newly created algorithm with reinitialization gives better, or at least comparable, results than the two algorithms without reinitialization.
The authors acknowledge the financial support from the Slovenian Research Agency (research core funding No. P2-0041), and the investment co-financed by the Republic of Slovenia and the European Union, European Regional Development Fund, implemented under the Operational Program for the Implementation of the EU Cohesion Policy in the period 2014–2020.
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Alić, A., Berkovič, K., Bošković, B., Brest, J. (2020). Population Size in Differential Evolution. In: Zamuda, A., Das, S., Suganthan, P., Panigrahi, B. (eds) Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing. SEMCCO FANCCO 2019 2019. Communications in Computer and Information Science, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-37838-7_3
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