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This is to be expected from theory; recall from the design of the experiment that in the first generation n g random solutions are generated whereas the repeated random draws based method performs n h =(1−r f )×n p ×n g +r f ×n p solutions which is equal to the number of unique solutions created by the genetic algorithm across all generations.
The generalized eigenvalue problem is defined and numerical solutions are generated for a wide range of rotational speed and tip mass variations.
The generalized eigenvalue problem is defined and numerical solutions are generated for a wide range of rotational speed and taper ratios.
In each iteration, (l) solutions are generated.
Pareto-optimal solutions are generated using a genetic algorithm.
By applying these mechanisms, new solutions are generated.
Owing to this approach, preferable solutions are generated first.
Pareto-optimal solutions are generated by means of a Tabu Search algorithm.
Discrete solutions are generated by integrating Brownian motion-like random search with an integer-rounding operation.
Initially in this proposed algorithm, the solutions are generated based on the fractional lion algorithm.
For triple objective function optimization, the Pareto plots presenting multiple trade-off solutions are generated.
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