Abstract
This paper solved aerodynamic drag reduction of transport wing fuselage configuration in transonic regime by using a parallel Nash evolutionary/deterministic hybrid optimization algorithm. Two sets of parameters are used, namely globally and locally. It is shown that optimizing separately local and global parameters by using Nash algorithms is far more efficient than considering these variables as a whole.
Highlights
Since Nash[1,2] introduces Nash Equilibrium (NE) theory in the early 50’s, it has become an efficient tool to solve Multi Objective Optimization (MOO) problems in aerodynamics[3,4,5,6] and other relative fields[7,8]
Since Nash introduced the fundamental concept of equilibrium point, Nash equilibrium has become an efficient tool to solve multi objective aerodynamic optimization problems
In order to solve the Single Objective Optimization (SOO) problem using Nash algorithms proposed in this paper, we introduce the concept of virtual game
Summary
Since Nash[1,2] introduces Nash Equilibrium (NE) theory in the early 50’s, it has become an efficient tool to solve Multi Objective Optimization (MOO) problems in aerodynamics[3,4,5,6] and other relative fields[7,8]. The gradientbased (deterministic) algorithms usually converge quickly in the vicinity of the solution, and are very interesting in terms of rapidity They have some limitations, being strongly dependent on user skills, due to the need to choose the initial. Evolution of the population takes place after the repeated application of the genetic operators (selection, crossover, mutation, etc.) They are robust methods, their convergence is very time-consuming, and they must be considered as sub-optimal algorithms, as for continuous variable optimization the global minimum of the objective function is not guaranteed. The idea in this paper is to bring together evolutionary and deterministic algorithms and combining the advantages of each approach in order to solve complex MOO problem efficiently under the concept of Nash game.
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