Data mining is an important facet of solving multi-objective optimization problem.Because it is one of the effective manner to discover the design knowledge in the multi-objective optimization problem which obtains large data.In the present study, data mining has been performed for a large-scale and real-world multidisciplinary design optimization (MDO) to provide knowledge regarding the design space.The MDO among aerodynamics, structures, and aeroelasticity of the regional-jet wing was carried out using high-fidelity Neff N50 KI5872SF0G Integrated 70/30 Fridge Freezer evaluation models on the adaptive range multi-objective genetic algorithm.As a result, nine non-dominated solutions were generated and used for tradeoff analysis among three objectives.
All solutions evaluated during the evolution were analyzed for the tradeoffs and influence of design variables using a self-organizing map to extract key features of the design space.Although the MDO results showed the Exercise Equipment - Resistance Bands inverted gull-wings as non-dominated solutions, one of the key features found by data mining was the non-gull wing geometry.When this knowledge was applied to one optimum solution, the resulting design was found to have better performance compared with the original geometry designed in the conventional manner.