Solving rotated multi-objective optimization problems using differential evolution

  • Authors:
  • Antony W. Iorio;Xiaodong Li

  • Affiliations:
  • School of Computer Science and Information Technology, Royal Melbourne Institute of Technology University, Melbourne, Vic., Australia;School of Computer Science and Information Technology, Royal Melbourne Institute of Technology University, Melbourne, Vic., Australia

  • Venue:
  • AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
  • Year:
  • 2004

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Abstract

This paper demonstrates that the self-adaptive technique of Differential Evolution (DE) can be simply used for solving a multi-objective optimization problem where parameters are interdependent The real-coded crossover and mutation rates within the NSGA-II have been replaced with a simple Differential Evolution scheme, and results are reported on a rotated problem which has presented difficulties using existing Multi-objective Genetic Algorithms The Differential Evolution variant of the NSGA-II has demonstrated rotational invariance and superior performance over the NSGA-II on this problem.