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

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...

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Vydáno v:Applied soft computing Ročník 87; s. 105991
Hlavní autoři: Abdi, Yousef, Feizi-Derakhshi, Mohammad-Reza
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.02.2020
Témata:
Big Data optimization
Evolutionary operators
Hybrid multi-objective evolutionary algorithm
Search Manager framework
Evolutionary operators
Hybrid multi-objective evolutionary algorithm
Search Manager framework
Big Data optimization
ISSN:1568-4946, 1872-9681
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Shrnutí: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. •Search Manager hybridization method extended to multi-objective optimization problems (MOSM).•Five configurations of the MOSM are proposed for Big Data optimization problems.•The proposed algorithms are compared with each other.•The results of proposed algorithms are compared with the results of NSGA-III UCAM.•MOSM is effective in optimizing Big Data optimization problems.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2019.105991