Hyper-Heuristic Approach for Tuning Parameter Adaptation in Differential Evolution : научное издание

Stanovov Vladimir; Kazakovtsev Lev; Semenkin Eugene

Дополнительные ссылки: eLibrary

База данных: Каталог библиотеки СФУ

Библиографическое описание: Hyper-Heuristic Approach for Tuning Parameter Adaptation in Differential Evolution : научное издание / V. V. Stanovov, L. L. Kazakovtsev, E. E. Semenkin. - Текст : непосредственный // Axioms. - 2024. - Т. 13, № 1. - P. 59. - ISSN 2075-1680, DOI 10.3390/axioms13010059.

Аннотация: Differential evolution (DE) is one of the most promising black-box numerical optimization methods. However, DE algorithms suffer from the problem of control parameter settings. Various adaptation methods have been proposed, with success history-based adaptation being the most popular. However, hand-crafted designs are known to suffer from human perception bias. In this study, our aim is to design automatically a parameter adaptation method for DE with the use of the hyper-heuristic approach. In particular, we consider the adaptation of scaling factor F, which is the most sensitive parameter of DE algorithms. In order to propose a flexible approach, a Taylor series expansion is used to represent the dependence between the success rate of the algorithm during its run and the scaling factor value. Moreover, two Taylor series are used for the mean of the random distribution for sampling F and its standard deviation. Unlike most studies, the Student’s t distribution is applied, and the number of degrees of freedom is also tuned. As a tuning method, another DE algorithm is used. The experiments performed on a recently proposed L-NTADE algorithm and two benchmark sets, CEC 2017 and CEC 2022, show that there is a relatively simple adaptation technique with the scaling factor changing between 0.4 and 0.6, which enables us to achieve high performance in most scenarios. It is shown that the automatically designed heuristic can be efficiently approximated by two simple equations, without a loss of efficiency.

Год издания: 2024

Авторы: Stanovov Vladimir , Kazakovtsev Lev , Semenkin Eugene

Источник: Axioms

Выпуск: Т. 13, № 1

Номера страниц: 59

Держатель оригинала документа: Institute of Informatics and Telecommunication, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk 660037, Russia, Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, Krasnoyarsk 660074, Russia

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Hyper-Heuristic Approach for Tuning Parameter Adaptation in Differential Evolution : научное издание
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