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An optimization routine may find a local minimum rather than the global minimum first depending on the arbitrary choice of starting values for the optimization and thus fail to report the true optimal parameter values.
The robust optimization approach was then applied to select the true optimal solution of the highest oil recovery by taking into account the geological uncertainties in permeability, porosity, and anisotropy models.
Without restrictive assumptions such as uniformly distributed frequency, identical damping ratio, the optimized non-uniformly MTMDs obtained here can be considered as the "true" optimal ones.
In practice, genetic algorithms find near-optimal conditions since they do not guarantee true optimal condition.
However, the optimum design obtained by SDM differs from the true optimal solution when large modal changes happen.
The proposed architecture effectively controls the local search ability thereby leading to better convergence towards the true optimal solution.
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With an emphasis for moving toward the true Pareto optimal region, an EA can be used to find the Pareto optimal solutions in one single simulation run.
If Krivine's conjecture were true, the optimal partition would have been just a half-plane.
In this paper, we integrate KKTPM with a recently proposed EMO algorithm to enhance its convergence properties towards the true Pareto optimal front.
Firstly, the results showed that MOPSO is able to find a very accurate and uniformly distributed approximation of the true Pareto optimal front.
The approach is highly capable of reaching true Pareto optimal curve indicated by the closeness of our non-dominated solutions to the true Pareto front and their uniform distribution over the Pareto curve (implying diversity).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com