Efficient hybrid-game strategies coupled to evolutionary algorithms for robust multidisciplinary design optimization in aerospace engineering

D. S. Lee, L. F. Gonzalez, J. Périaux, K. Srinivas

    Research output: Contribution to journalArticlepeer-review

    35 Citations (Scopus)

    Abstract

    A number of game strategies have been developed in past decades and used in the fields of economics, engineering, computer science, and biology due to their efficiency in solving design optimization problems. In addition, research in multiobjective and multidisciplinary design optimization has focused on developing a robust and efficient optimization method so it can produce a set of high quality solutions with less computational time. In this paper, two optimization techniques are considered; the first optimization method uses multifidelity hierarchical Pareto-optimality. The second optimization method uses the combination of game strategies Nash-equilibrium and Pareto-optimality. This paper shows how game strategies can be coupled to multiobjective evolutionary algorithms and robust design techniques 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 and non-Hybrid-Game strategies are demonstrated.

    Original languageEnglish
    Article number5735238
    Pages (from-to)133-150
    Number of pages18
    JournalIEEE Transactions on Evolutionary Computation
    Volume15
    Issue number2
    DOIs
    Publication statusPublished - Apr 2011

    Keywords

    • Evolutionary optimization
    • game strategies
    • Nash-equilibrium
    • Pareto front
    • robust design
    • shape optimization
    • uncertainties

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