A Novel Multiobjective Evolutionary Algorithm Based on Min-Max Strategy

  • Hai Lin Liu
  • Yuping Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2690)


A new fitness function is proposed in the paper at first, in which the fitness of an individual is defined by the maximum value of the weighted normalized objectives. In order to get the required weights, the sphere coordinate transformation is used. The fitness constructed in this way can result in a group of uniform search directions in the objective space. By using these search directions, the evolutionary algorithm can explore the objective space uniformly, keep the diversity of the population and find uniformly distributed solutions on the Pareto frontier gradually. The numerical simulations indicate the proposed algorithm is efficient and has a better performance than the compared ones.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hai Lin Liu
    • 1
  • Yuping Wang
    • 2
  1. 1.Department of Applied MathematicsGuangdong University of TechnologyGuang ZhouP. R. China
  2. 2.Faculty of ScienceXidian UniversityXi’anP. R. China

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