Target matching problems and an adaptive constraint strategy for multiobjective design optimization using genetic algorithms

  • Wang N
  • Tai K
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In multiobjective design optimization problems, the designer may know that some objectives are harder to extremize than others or that some regions of the objective space are more desirable/important. Such useful information can be incorporated into the genetic algorithm optimization procedure by treating the more challenging/important objectives as constraints whose ideal values are adaptively improved/tightened during the procedure to guide the search. Employing this adaptive constraint strategy and a morphological representation of geometric variables, a genetic algorithm was developed and evaluated through special 'Target Matching' test problems which are simulated topology/shape optimization problems with multiple objectives and constraints. © 2010 Published by Elsevier Ltd. All rights reserved.

Author-supplied keywords

  • Genetic algorithms
  • Hybrid algorithms
  • Morphological geometric representation
  • Multiobjective optimization
  • Structural topology optimization

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