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AuthorMashwani, W.K.
AuthorKhan, A.
AuthorGokta?, A.
AuthorUnvan, Y.A.
AuthorYaniay, O.
AuthorHamdi, A.
Available date2023-09-24T07:55:31Z
Publication Date2021
Publication NameCommunications in Statistics - Theory and Methods
ResourceScopus
URIhttp://dx.doi.org/10.1080/03610926.2020.1783559
URIhttp://hdl.handle.net/10576/47871
AbstractEvolutionary algorithms (EAs) is a family of population-based nature optimization methods. In contrast to classical optimization techniques, EAs provide a set of approximated solutions for different test suites of optimization and real-world problems in single simulation. In the last few years, hybrid EAs have received much attention by utilizing the valuable aspects of different nature of search strategies. Hybrid EAs are quite efficient in handling various optimization and search problems having had high complexity, noisy environment, imprecision, uncertainty and vagueness. In this article, a hybrid differential evolutionary strawberry algorithm (HDEA) is suggested to utilize the propagating behavior of the strawberry plant and perturbation process of differential evolution (DE) algorithm in order to evolve their population set of solutions. The proposed algorithm employs DE as a substitute while replacing the runners of the strawberry plant to effectively explore and exploit the search space of the problem at hand. The numerical results found by the proposed algorithm over most benchmark functions after extensive experiments are much promising in terms of proximity and diversity. 2020 Taylor & Francis Group, LLC.
Languageen
PublisherBellwether Publishing, Ltd.
Subjectcomputing
evolutionary computation
Optimization
TitleHybrid differential evolutionary strawberry algorithm for real-parameter optimization problems
TypeArticle
Pagination1685-1698
Issue Number7
Volume Number50
dc.accessType Abstract Only


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