DocumentCode
238772
Title
A replacement strategy for balancing convergence and diversity in MOEA/D
Author
Zhenkun Wang ; Qingfu Zhang ; Maoguo Gong ; Aimin Zhou
Author_Institution
Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xian, China
fYear
2014
fDate
6-11 July 2014
Firstpage
2132
Lastpage
2139
Abstract
This paper studies the replacement schemes in MOEA/D and proposes a new replacement named global replacement. It can improve the performance of MOEA/D. Moreover, trade-offs between convergence and diversity can be easily controlled in this replacement strategy. It also shows that different problems need different trade-offs between convergence and diversity. We test the MOEA/D with this global replacement on three sets of benchmark problems to demonstrate its effectiveness.
Keywords
convergence; evolutionary computation; optimisation; MOEA/D performance improvement; benchmark problems; convergence; diversity; global replacement strategy; multiobjective evolutionary algorithm-based-on-decomposition framework; Evolutionary computation; MOEA/D; Multiobjective optimization; replacement; selection operator;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation (CEC), 2014 IEEE Congress on
Conference_Location
Beijing
Print_ISBN
978-1-4799-6626-4
Type
conf
DOI
10.1109/CEC.2014.6900319
Filename
6900319
Link To Document