A Novel Multi-objective Evolutionary Algorithm

Authors:
Bojin Zheng;Ting Hu
Affiliations:
College of Computer Science,South-Central University For Nationalities, Wuhan,430074, China;Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, A1B 3X5, Canada
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Year:
2007

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Abstract

Evolutionary Algorithms are recognized to be efficient to deal with Multi-objective Optimization Problems(MOPs) which are difficult to be solved with traditional methods. Here a new Multi-objective Optimization Evolutionary Algorithm named DGPS which is compound with Geometrical Pareto Selection Method (GPS), Weighted Sum Method (WSM) and Dynamical Evolutionary Algorithm (DEA) is proposed. Some famous benchmark functions are carried out to test this algorithm's performance and the numerical experiments show that this algorithm runs much faster than SPEA2, NSGAII, HPMOEA and can obtain finer approximate Pareto fronts which include thousands of well-distributed points.