Adaptive Differential Evolution With Two Populations of New and Best Individuals
Abstract
In this paper a novel algorithmic scheme for differential evolution is proposed with two populations and new mutation strategies. The populations contain latest and best individuals, and the sizes of these populations are controlled independently. The LL-NADE algorithm uses the parameter adaptation scheme proposed in L-SHADE, and the sizes of both populations are reduced at the same time. The experiments described in the paper are performed on the CEC 2022 benchmark suite. The total standard score is used for comparison of different algorithms on a set of benchmark problems. The experiments are performed with six different mutation strategies, which utilize individuals from both populations. The most efficient mutation strategy utilizes directed search from newest to top individuals. It is shown that LL-NADE is capable of demonstrating results comparable with state-of-the-art algorithms. Comparison on various test functions and dimensions have shown that LL-NADE performs in a way that is not similar to other differential evolution algorithms, achieving better results on some problems, but worse on other.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
About this article
Publication Date
27 February 2023
Article Doi
eBook ISBN
978-1-80296-960-3
Publisher
European Publisher
Volume
1
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-403
Subjects
Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
Cite this article as:
Stanovov, V., Akhmedova, S., & Semenkin, E. (2023). Adaptive Differential Evolution With Two Populations of New and Best Individuals. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 102-110). European Publisher. https://doi.org/10.15405/epct.23021.13