Title :
An Improved Genetic Algorithm For Multi-Objective Optimization
Author :
Lin, Fu ; He, Guiming
Author_Institution :
Wuhan University, Wuhan, China
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;
Conference_Titel :
Parallel and Distributed Computing, Applications and Technologies, 2005. PDCAT 2005. Sixth International Conference on
Print_ISBN :
0-7695-2405-2
DOI :
10.1109/PDCAT.2005.84