Multi-objective evolutionary algorithm based on layer strategy

Authors:
Sen Zhao;Zhifeng Hao;Shusen Liu;Weidi Xu;Han Huang
Affiliations:
College of Computer Science and Engineering, South China University of Technology, Guangzhou, China,Department of Computer Science, JiNan University, Guangzhou, China;College of Computer Science and Engineering, South China University of Technology, Guangzhou, China,School of Computer, Guangdong University of Technology, Guangzhou, China;School of Software Engineering, South China University of Technology, Guangzhou, China;School of Software Engineering, South China University of Technology, Guangzhou, China;School of Software Engineering, South China University of Technology, Guangzhou, China
Venue:
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2012

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Abstract

In view of the unsatisfactory search performance of binary crossing operator as well as the elitist-preserving approach's influence on the population's diversity, an algorithm of multi-objective based on layer strategy and self-adaptive crossing distribution index is put forward on the basis of research and analysis on NSGA-II algorithm. The algorithm will be applied to the ZDT series test functions. The experiment results show that the improved algorithm maintains the diversity and distribution of population. Compared with NSGA-II, the Pareto front we get is much closer to the true Pareto optimal front.