Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problems - Matlab Code
DOI: 10.1016/j.asoc.2019.105991
URL: https://www.sciencedirect.com/science/article/pii/S1568494619307720
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.
To run the proposed configurations of MOSM framework for solving Big-Opt problems:
- Extract "ProblemData.zip" file to the path of MOSM files.
- Run the SearchManager script in Matlab environment.