A fast and effective method for pruning of non-dominated solutions in many-objective problems

  • Authors:
  • Saku Kukkonen;Kalyanmoy Deb

  • Affiliations:
  • Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, Finland;Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology Kanpur, Kanpur, India

  • Venue:
  • PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
  • Year:
  • 2006

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Abstract

Diversity maintenance of solutions is an essential part in multi-objective optimization. Existing techniques are suboptimal either in the sense of obtained distribution or execution time. This paper proposes an effective and relatively fast method for pruning a set of non-dominated solutions. The proposed method is based on a crowding estimation technique using nearest neighbors of solutions in Euclidean sense, and a technique for finding these nearest neighbors quickly. The method is experimentally evaluated, and results indicate a good trade-off between the obtained distribution and execution time. Distribution is good also in many-objective problems, when number of objectives is more than two.