A Highly Efficient Multi-objective Optimization Evolutionary Algorithm

Authors:
Bojin Zheng
Affiliations:
South-central University for Nationalities, China
Venue:
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 05
Year:
2007

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Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART II

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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.