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

• Published in: Applied soft computing Vol. 87; p. 105991
• Main Authors: Abdi, Yousef, Feizi-Derakhshi, Mohammad-Reza
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2020
• Subjects: 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
• DOI: 10.1016/j.asoc.2019.105991

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.