DocumentCode
1601427
Title
A Highly Efficient Multi-objective Optimization Evolutionary Algorithm
Author
Zheng, Bojin
Author_Institution
South-central Univ. for Nationalities, Wuhan
Volume
5
fYear
2007
Firstpage
549
Lastpage
554
Abstract
Multi-objective Optimization Evolutionary Algorithms (MOEAs) are effective for solving Multi-objective Optimization Problems. Here a new algorithm is proposed and is compared with some famous MOEAs at the state of the art. The experimental results imply that the approximated Pareto Fronts which are obtained by this algorithm are better than the approximated Pareto Fronts by SPEA2, NSGAII etc. when dealing with the chosen test problems within satisfactory computational time.
Keywords
Pareto optimisation; computational complexity; evolutionary computation; NSGAII; SPEA2; approximated Pareto front; computational complexity; multi-objective optimization evolutionary algorithms; multi-objective optimization problems; Computer science; Educational institutions; Evolutionary computation; Genetic algorithms; Pareto optimization; Sorting; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location
Haikou
Print_ISBN
978-0-7695-2875-5
Type
conf
DOI
10.1109/ICNC.2007.43
Filename
4344900
Link To Document