Multiobjective genetic algorithm applied to aerodynamic design of cascade airfoils

Shigeru Obayashi, Takanori Tsukahara, Takashi Nakamura

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


A multiobjective genetic algorithm (GA) based on Fonseca-Fleming's Pareto-based ranking and fitness-sharing techniques has been applied to aerodynamic shape optimization of cascade airfoil design. Airfoil performance is evaluated by a Navier-Stokes code. Evaluation of GA population is parallelized on the Numerical Wind Tunnel, a parallel vector machine. The present multiobjective design seeks high pressure rise, high flow turning angle, and low total pressure loss at a low Mach number. Pareto solutions that perform better than existing control diffusion airfoils were obtained.

Original languageEnglish
Pages (from-to)211-216
Number of pages6
JournalIEEE Transactions on Industrial Electronics
Issue number1
Publication statusPublished - 2000 Feb


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