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
Link To Document :
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