سال انتشار:

2020

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عنوان انگلیسی مقاله:

Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problems

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ترجمه فارسی عنوان مقاله:

الگوریتم تکاملی چند منظوره ترکیبی مبتنی بر چارچوب مدیر جستجو برای مسائل بهینه سازی داده های بزرگ

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منبع:

Sciencedirect - Elsevier - Applied Soft Computing Journal, 87 (2020) 105991: doi:10:1016/j:asoc:2019:105991

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نویسنده:

Yousef Abdi ∗, Mohammad-Reza Feizi-Derakhshi

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چکیده انگلیسی:

Big Data optimization (Big-Opt) refers to optimization problems which require to manage the properties of big data analytics. In the present paper, the Search Manager (SM), a recently proposed framework for hybridizing metaheuristics to improve the performance of optimization algorithms, is extended for multi-objective problems (MOSM), and then five configurations of it by combination of different search strategies are proposed to solve the EEG signal analysis problem which is a member of the big data optimization problems class. Experimental results demonstrate that the proposed configurations of MOSM are efficient in this kind of problems. The configurations are also compared with NSGA-III with uniform crossover and adaptive mutation operators (NSGA-III UCAM), which is a recently proposed method for Big-Opt problems.
Keywords: Big Data optimization | Hybrid multi-objective evolutionary algorithm | Search Manager framework | Evolutionary operators

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قیمت: رایگان

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توضیحات اضافی:

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