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A K-Nearest-Neighbors Pareto Rank Assignment Strategy and Compound Crossover Operator Based NSGA-II and Its Applications on Multi-objective Optimization Functions

  • Weiya Guo
  • Zhenhua Li
  • Dan Zhao
  • Tim Wong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5370)

Abstract

We try to improve the NSGA-II, one of the most classical MOP algorithms, in two ways. To measure individual crowding distance by edge weight of minimum spanning tree and k-nearest-neighbors Pareto rank assignment strategy is helpful on diversity of population; A compound crossover operator increases the extent and the ability of search. Experimental results on ZDTs and DTLZs, suggest that A K-Nearest-Neighbors Pareto Rank Assignment Strategy and Compound Crossover Operator Based NSGA-II (KC NSGA-II) works faster and has more diverse solutions than its origins.

Keywords

Multi-Objective Evolutionary Algorithm NSGA-II Minimum Spanning Tree K-Nearest-Neighbors Compound Crossover Operator 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Weiya Guo
    • 1
  • Zhenhua Li
    • 1
  • Dan Zhao
    • 1
  • Tim Wong
    • 1
  1. 1.China University Of GeosciencesChina

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