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In our case, the proposed genetic algorithm is based on multi-objective strategy.
Then two rough approximation-based bi-level multi-objective strategy models are developed.
A multi-objective strategy is used for maximizing the accuracy and minimizing the complexity of the models.
Here we have optimized shape and location of cooling passages of a C3X turbine blade using a multi-objective strategy.
In recent years, the optimal location of flow observations/meters, related to design of optimal district metering areas, has been faced considering optimal network segmentation and the modularity index using a multi-objective strategy.
According to an optimization approach, a multi-objective strategy is selected on a mixed continuous and discrete state variables domain in order to deal with possible multi-gravity assist manoeuvres (GAM) as further degrees of freedom of the problem, in terms of both number and planets sequence selection to minimize both the Δv expense and the time trip timespan.
The improvement of the multi-objective strategy against the single objectives is investigated first.
Only in about 7.0% cases, the multi-objective strategy gets the worst results.
In the circumstances, the multi-objective strategy can rarely find a good docking solution.
The results indicate that the multi-objective strategy can enhance the pose prediction power of docking with the available SFs.
However, through the analysis of the failure cases, we find the multi-objective strategy still limited by the combined SFs.
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