Ensemble of the nature-inspired algorithms with success-history based position adaptation : доклад, тезисы доклада

Akhmedova Sh; Stanovov V; Erokhin D; Semenkina O

Дополнительные ссылки: eLibrary · Источник статьи

База данных: Каталог библиотеки СФУ

Библиографическое описание: Ensemble of the nature-inspired algorithms with success-history based position adaptation : доклад, тезисы доклада / S. S. Akhmedova [et al.]. - [S. l. : s. n.], 2020. - Текст : непосредственный // IOP Conference Series: Materials Science and Engineering / II International Scientific Conference "Advanced Technologies in Aerospace, Mechanical and Automation Engineering" (2019 ; 18.11 - 21.11 ; Krasnoyarsk) : Institute of Physics and IOP Publishing Limited, 2020. - 734. - P. 12089. - This research is performed with the financial support of the Ministry of Education and Science of the Russian Federation within state assignment № 2.6757.2017/ BCh. - DOI 10.1088/1757-899X/734/1/012089.

Аннотация: Previously, a meta-heuristic approach called Co-Operation of Biology Related Algorithms, or COBRA for short, based on a fuzzy logic controller for solving real-valued optimization problems was introduced and described. The basic idea of the originally proposed approach consists in a cooperative work of six well-known biology-inspired algorithms (components) with similar schemes. Furthermore, the fuzzy logic controller determines which biology-inspired algorithms should be included in the co-operative work and their population sizes at a given moment for solving optimization problems using the COBRA approach. In this study a new modification of the COBRA approach based on an alternative way of generating potential solutions is proposed. The stated technique uses a historical memory of successful positions found by individuals to guide them in different directions and thus to improve their exploration and exploitation abilities. The proposed method was applied to the components of the COBRA approach and to its basic procedures. The modified meta-heuristic as well as other variants of the COBRA algorithm and components (with and without the proposed modification) were evaluated on three sets of low- and high-dimensional benchmark problems. The experimental results obtained by all algorithms are presented and compared. It was concluded that the fuzzy-controlled COBRA with success-history based position adaptation allows better solutions to be found than the other mentioned biology-inspired algorithms with the same computational effort. Thus, the usefulness of the proposed position adaptation technique was demonstrated.

Год издания: 2020

Авторы: Akhmedova Sh , Stanovov V , Erokhin D , Semenkina O

Издательство: Institute of Physics and IOP Publishing Limited

Источник: IOP Conference Series: Materials Science and Engineering

Выпуск: 734

Мероприятие: II International Scientific Conference "Advanced Technologies in Aerospace, Mechanical and Automation Engineering" (Krasnoyarsk, 2019 ; 18.11 - 21.11)

Номера страниц: 12089

Держатель оригинала документа: Reshetnev Siberian State University of Science and Technology

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Ensemble of the nature-inspired algorithms with success-history based position adaptation : доклад, тезисы доклада
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