A new genetic algorithm for multiobjective optimization

A. D. Belegundu, P. L.N. Murthy

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


In recent years, much interest has been generated for using genetic algorithms to optimize ccrtain classes of problems. Genetic algorithms have definite advantages over some other types of optimization algorithms in that they are quite robust, and do not require knowledge of the gradients of the objective function. Because of their unique breeding and selection processes, genetic algorithms can be used with equal ease on linear and nonlinear optimization problems, which makes them an excellent all-purpose optimization algorithm. This paper will discuss a new genetic algorithm (GENMO) that can be used to simultaneously optimize multiple objectives. Multiobjective optimization is necessary in design of turbine blades, aircraft and other complex real world structures. The GENMO algorithm can be used to generate Pareto sets — which contain the trade-off information - for two or more conflicting objective functions. This is demonstrated through applications to composites and to turbomachinery airfoils.

Original languageEnglish (US)
Number of pages10
StatePublished - 1996
Event6th AIAA/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 1996 - Bellevue, United States
Duration: Sep 4 1996Sep 6 1996


Other6th AIAA/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 1996
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Mechanical Engineering


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