DocumentCode
3065768
Title
An Improved Genetic Algorithm For Multi-Objective Optimization
Author
Lin, Fu ; He, Guiming
Author_Institution
Wuhan University, Wuhan, China
fYear
2005
fDate
05-08 Dec. 2005
Firstpage
938
Lastpage
940
Abstract
The article points out that the traditional methods for multi-objective optimization exist some drawbacks, and presents a new method for multi-objective optimization: Combining genetic search with local search. The improved genetic algorithm (IGA) introduces local search as a means of acceleration and refinement of the solutions of genetic search. The experiments show that the improved genetic algorithm (IGA), compared with the traditional genetic algorithm (GA), can improve efficiency of optimization and ensure a better convergence to the true Pareto optimal front.
Keywords
Acceleration; Algorithm design and analysis; Distributed computing; Electronic mail; Genetic algorithms; Helium; Mathematics; Optimization methods; Pareto analysis; Pareto optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Parallel and Distributed Computing, Applications and Technologies, 2005. PDCAT 2005. Sixth International Conference on
Print_ISBN
0-7695-2405-2
Type
conf
DOI
10.1109/PDCAT.2005.84
Filename
1579068
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