Computer Science ›› 2018, Vol. 45 ›› Issue (6): 187-192.doi: 10.11896/j.issn.1002-137X.2018.06.033

• Artificial Intelligence • Previous Articles     Next Articles

Improved NSGA2 Algorithm Based on Dominant Strength

LAI Wen-xing, DENG Zhong-min   

  1. School of Astronautics,Beihang University,Beijing 100191,China
  • Received:2017-05-03 Online:2018-06-15 Published:2018-07-24

Abstract: NSGA2 algorithm is a simple,efficient and widely used multi-objective evolutionary algorithm.However,when solving high-dimensional and complex nonlinear multi-objective optimization problems in practical engineering field,NSGA2 has some obvious design defects,such as ineffective identification of pseudo non-dominated solutions,low computational efficiency,poor convergence and distribution.In order to remedy the above drawbacks,this paper proposed an improved NSGA2 algorithm based on dominant strength (INSGA2-DS).INSGA2-DS uses the fast dominant strength sorting method to construct non-dominated set,introduces a new crowding distance with considering variance to improve the distribution of solution sets,and adopts the adaptive elitist retention strategy to adjust elitist retention scale in evolutionary process automatically.The experimental results of INSGA2-DS and NSGA2 with standard test functions show that INSGA2-DS algorithm can improve the convergence and distribution of NSGA2 algorithm effectively.

Key words: Dominant strength, Multi-objective evolutionary algorithm, Multi-objective optimization problems, NSGA2

CLC Number: 

  • TP301
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