DocumentCode
2258223
Title
A New Multi-objective Differential Evolution Algorithm
Author
Gao, Yuelin ; Zhou, Jingke ; Jia, Songwei
Author_Institution
Inst. of Inf. & Syst. Sci., North Nat. Univ., China
fYear
2010
fDate
11-14 Dec. 2010
Firstpage
170
Lastpage
173
Abstract
A new multi-objective differential evolution algorithm is proposed. A dual elitist selection strategy based on Individual Pareto Rank and Individual Density is employed in the proposed new algorithm. It also remains the characteristic of keeping elitists. The corresponding effects comparisons of new algorithm with other classic multi-objective evolutionary algorithms show that new algorithm require initial population small in size, fewer iterations, and output more optimal solutions. It can improve the diversity metric significantly while ensuring satisfactory convergence metric.
Keywords
Pareto optimisation; convergence; evolutionary computation; iterative methods; convergence metric; diversity metric; dual elitist selection strategy; individual Pareto rank; individual density; iteration; keeping elitists; multiobjective differential evolution algorithm; differential evolution; dual elitist selection strategy; multi-objective optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Security (CIS), 2010 International Conference on
Conference_Location
Nanning
Print_ISBN
978-1-4244-9114-8
Electronic_ISBN
978-0-7695-4297-3
Type
conf
DOI
10.1109/CIS.2010.44
Filename
5696256
Link To Document