Evolutionary Multi-objective Optimization Algorithm with Preference for Mechanical Design

  • Jianwei Wang
  • Jianming Zhang
  • Xiaopeng Wei
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


Although many techniques have been developed to deal with either multi-criteria or constrained aspect problems, few methods explicitly deal with both features. Therefore, a novel method of evolutionary multi-objective optimization algorithm with preference is proposed. It aims at solving multiobjective and multi-constraint problems, where the user incorporates his/her preferences about the objectives at the very start of the search process, by means of weights. It functions by considering the satisfaction of the constraints as a new objective, and using a multi-criteria decision aid method to rank the members of the EA population at each generation. In addition, the Analytic Hierarchy Process (AHP) is adopted to determine the weights of the sub-objective functions. Also, adaptivity of the weights is applied in order to converge more easily towards the feasible domain. Finally, an example is given to illustrate the validity of the evolutionary multi-objective optimization with preference.


Analytic Hierarchy Process Preference Information Helical Gear Feasible Domain Multiple Criterion Decision Making 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jianwei Wang
    • 1
    • 2
  • Jianming Zhang
    • 2
    • 3
  • Xiaopeng Wei
    • 2
    • 3
  1. 1.School of Mechanical EngineeringDalian University of TechnologyDalianChina
  2. 2.Center for Advanced Design TechnologyDalian UniversityDalianChina
  3. 3.University Key Lab. of Information Science & EngineeringDalian UniversityDalianChina

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