A Distributed Multiple Populations Framework for Evolutionary Algorithm in Solving Dynamic Optimization Problems
Aiming to dynamic optimization problems (DOPs), this paper develops a novel general distributed multiple populations (DMP) framework for evolutionary algorithms (EAs). DMP employs six strategies designed in three levels (i.e., population-level, subpopulation-level, and individual-level) to deal with...
Main Authors: | Xiong-Wen Luo, Zi-Jia Wang, Ren-Chu Guan, Zhi-Hui Zhan, Ying Gao |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8669738/ |
Similar Items
-
Direct and indirect costs of COPD progression and its comorbidities in a structured disease management program: results from the LQ-DMP study
by: Florian Kirsch, et al.
Published: (2019-10-01) -
Information and knowledge: an evolutionary framework for information science
by: Marcia J. Bates
Published: (2005-01-01) -
PREPREKE ZA SPROVOĐENJE DRUŠTVENO ODGOVORNOG POSLOVANJA U REPUBLICI SRBIJI
by: Sanja Dobričanin, et al.
Published: (2018-12-01) -
An evolutionary algorithm to track changes of optimum value locations in dynamic environments
by: Victoria S. Aragón, et al.
Published: (2004-10-01) -
A Framework for Adapting Population-Based and Heuristic Algorithms for Dynamic Optimization Problems
by: S. M. Ejabati, et al.
Published: (2020-06-01)