Title :
Cooperative co-evolution with correlation identification grouping for large scale function optimization
Author :
Jingjing Sun ; Hongbin Dong
Author_Institution :
Dept. of Comput. Sci. & Technol., Univ. of Harbin Eng., Harbin, China
Abstract :
Cooperative co-evolutionary (CC) architectures provide a framework for solving large scale function optimization problems, by decomposing variables into different groups as subproblems, solving the subproblems, and then reintegrating the solutions. But there is no systematic method for how to decomposing variables, which is a major abstacle for CC framework. This paper provides a correlation identification technique for variables grouping; and combining with Differential Evolution (DE), a Cooperative co-evolutionary differential evolution algorithm with correlation identification grouping (DECC-CIG) is presented. The performance of DECC-CIG is compared with DECC and DECC-NW to highlight its benefits.
Keywords :
correlation methods; differential equations; optimisation; DECC-CIG; cooperative co-evolutionary architectures; cooperative co-evolutionary differential evolution algorithm; correlation identification grouping; large scale function optimization problems; Algorithm design and analysis; Correlation; Couplings; Optimization; Presses; Sociology; Uncertainty;
Conference_Titel :
Information Science and Technology (ICIST), 2013 International Conference on
Conference_Location :
Yangzhou
Print_ISBN :
978-1-4673-5137-9
DOI :
10.1109/ICIST.2013.6747683