Hybrid-Game Strategies for multi-objective design optimization in engineering

DongSeop Lee*, Luis Felipe Gonzalez, Jacques Periaux, Karkenahalli Srinivas, Eugenio Onate

*Corresponding author for this work

Research output: Contribution to journalArticle

24 Citations (Scopus)


A number of Game Strategies (GS) have been developed in past decades. They have been used in the fields of economics, engineering, computer science and biology due to their efficiency in solving design optimization problems. In addition, research in multi-objective (MO) and multidisciplinary design optimization (MDO) has focused on developing robust and efficient optimization methods to produce a set of high quality solutions with low computational cost. In this paper, two optimization techniques are considered; the first optimization method uses multi-fidelity hierarchical Pareto optimality. The second optimization method uses the combination of two Game Strategies; Nash-equilibrium and Pareto optimality. The paper shows how Game Strategies can be hybridised and coupled to Multi-Objective Evolutionary Algorithms (MOEA) to accelerate convergence speed and to produce a set of high quality solutions. Numerical results obtained from both optimization methods are compared in terms of computational expense and model quality. The benefits of using Hybrid-Game Strategies are clearly demonstrated.

Original languageEnglish
Pages (from-to)189-204
Number of pages16
JournalComputers and Fluids
Issue number1
Publication statusPublished - Aug 2011


  • Game strategies
  • Hybrid-Game
  • Multi-objective evolutionary algorithm (MOEA)
  • Nash-equilibrium
  • Pareto front
  • Shape optimization

Fingerprint Dive into the research topics of 'Hybrid-Game Strategies for multi-objective design optimization in engineering'. Together they form a unique fingerprint.

  • Cite this