Algorithms for clustering data
Algorithms for clustering data
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Data visualization: parallel coordinates and dimension reduction
Computing in Science and Engineering
Visualization and self-organization of multidimensional data through equalized orthogonal mapping
IEEE Transactions on Neural Networks
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An artificial satellite design requires severe design objectives such as performance, reliability, weight, robustness, cost, and so on. To solve the conflicted requirements at the same time, multiobjective optimization is getting more popular in the design. Using the optimization, it becomes ordinary to get many solutions, such as Pareto solutions, quasi-Pareto solutions, and feasible solutions. The alternative solutions, however, are very difficult to be adopted to practical engineering decision directly. Therefore, to make the decision, proper information about the solutions in a function, parameter and real design space should be provided. In this paper, a new approach for the interpretation of Pareto solutions is proposed based on multidimensional visualization and clustering. The proposed method is applied to a thermal robustness and mass optimization problem of heat pipe shape design for an artificial satellite. The information gleaned from the propose approach can support the engineering decision for the design of artificial satellite heat pipe.