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.
Wang, N. F., & Tai, K. (2010). Target matching problems and an adaptive constraint strategy for multiobjective design optimization using genetic algorithms. Computers and Structures, 88(19–20), 1064–1073. https://doi.org/10.1016/j.compstruc.2010.06.002