Shape Optimization
Assessment of Multi-Disciplinary Shape Optimization through MOGA Optimized ANN and G-Optimal Kriging Algorithms
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In the current scenario, the design of a product is being very improved but at most of the situations, the designers has to face the multiple responses of various shape parameter functions, to achieve most optimum results. In most of the problems, the designers has to face multi-disciplinary complex objectives which are dominating in nature and which do not allow another objective has to be completed. In this project, two improved methods have been proposed for multidisciplinary shape optimization. The first method proposed in this project is to use a neural network (NN) to calculate the param...
In the current scenario, the design of a product is being very improved but at most of the situations, the designers has to face the multiple responses of various shape parameter functions, to achieve most optimum results. In most of the problems, the designers has to face multi-disciplinary complex objectives which are dominating in nature and which do not allow another objective has to be completed. In this project, two improved methods have been proposed for multidisciplinary shape optimization. The first method proposed in this project is to use a neural network (NN) to calculate the parameter response functions. At the optimization stage, a multi-objective genetic algorithm (MOGA) is used in combination with objective functions to establish the optimum conditions for the control functions and at the second method, the G-optimal kriging algorithm approach is used to serve the collection of correlation functions. The redesign of the hook model is engaged for showing these optimization approaches for multi-response non-random dominated problem to bear a high loading situation along with the weight and maximum total deformation reduction.
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