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 :
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