An Improved Genetic Algorithm For Multi-Objective Optimization

Authors:
Fu Lin;Guiming He
Affiliations:
Wuhan University, Wuhan, China;Wuhan University, Wuhan, China
Venue:
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
Year:
2005

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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.