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AuthorAbdel-Nabi, Heba
AuthorAli, Mostafa Z.
AuthorAwajan, Arafat
AuthorAlazrai, Rami
AuthorDaoud, Mohammad I.
AuthorSuganthan, Ponnuthurai N.
Available date2023-02-08T06:12:34Z
Publication Date2023-05-01
Publication NameInformation Sciences
Identifierhttp://dx.doi.org/10.1016/j.ins.2023.01.065
CitationAbdel-Nabi, H., Ali, M. Z., Awajan, A., Alazrai, R., Daoud, M. I., & Suganthan, P. N. (2023). An Iterative cyclic Tri-Strategy Hybrid Stochastic fractal with Adaptive Differential Algorithm for Global Numerical Optimization. Information Sciences.‏
ISSN00200255
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85147090338&origin=inward
URIhttp://hdl.handle.net/10576/39731
AbstractMany real-life problems can be formulated as numerical optimization problems. Such problems pose a challenge for researchers when designing efficient techniques that are capable of finding the desired solution without suffering from premature convergence. This paper proposes a novel evolutionary algorithm that blends the exploitative and explorative merits of two main evolutionary algorithms, namely the Stochastic Fractal Search (SFS) and a Differential Evolution (DE) variant. This amalgam has an effective interaction and cooperation of an ensemble of diverse strategies to derive a single framework called Iterative Cyclic Tri-strategy with adaptive Differential Stochastic Fractal Evolutionary Algorithm (Ic3-aDSF-EA). The component algorithms cooperate and compete to enhance the quality of the generated solutions and complement each other. The iterative cycles in the proposed algorithm consist of three consecutive phases. The main idea behind the cyclic nature of Ic3-aDSF-EA is to gradually emphasize the work of the best-performing algorithm without ignoring the effects of the other inferior algorithm during the search process. The cooperation of component algorithms takes place at the end of each cycle for information sharing and the quality of solutions for the next cycle. The algorithm's performance is evaluated on 43 problems from three different benchmark suites. The paper also investigates the application to a set of real-life problems. The overall results show that the proposed Ic3-aDSF-EA has a propitious performance and a reliable scalability behavior compared to other state-of-the-art algorithms.
Languageen
PublisherElsevier Inc.
SubjectDifferential evolution
Evolutionary algoirithms
Memetic search
Optimization
TitleAn iterative cyclic tri-strategy hybrid stochastic fractal with adaptive differential algorithm for global numerical optimization
TypeArticle
Volume Number628
dc.accessType Abstract Only


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