Efficient Hybrid-Game Strategies Coupled to Evolutionary Algorithms for Robust Multidisciplinary Design Optimization in Aerospace Engineering

• Published in: IEEE transactions on evolutionary computation Vol. 15; no. 2; pp. 133 - 150
• Main Authors: Lee, D S, Gonzalez, L F, Périaux, J, Srinivas, K
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Biology; Computation; Computer science; Decision theory. Utility theory; Design optimization; Evolutionary algorithms; Evolutionary computation; Evolutionary optimization; Exact sciences and technology; game strategies; Game theory; Games; Mathematical models; Multidisciplinary design optimization; Nash-equilibrium; Operational research and scientific management; Operational research. Management science; Optimization; Optimization methods; Pareto front; robust design; Robustness; shape optimization; Strategy; Studies; Topology; uncertainties; Uncertainty; Nash strategy; Geometrical shape; Pareto front; Pareto optimum; game strategies; shape optimization; Evolutionary algorithm; Multiobjective programming; Nash equilibrium; Modeling; Optimization; Evolutionary optimization; Uncertain system; Aerospace; Nash-equilibrium; Problem solving; Player strategy; robust design; Game equilibrium; Hybrid model; Robustness; uncertainties; Multidisciplinary; Mathematical programming
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2010.2043364

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.