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The optimal design of TEG achieved by multi-objective optimization formulates an appropriate balance between power, efficiency and ecological function, in order that all the three are improved concurrently.
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We also directly compare the 3D distance matrices corresponding to the two structures with the distance error: distanceError = ∑ i, j = 0 n (d i j (X ) − d i, j (X′ ) ) 2 The main difference between the optimization formulated by ChromSDE and those of the other methods is the penalty assigned to non-interacting beads.
If H is an optimal matching, then the optimization problem formulated in (48) is solved.
A procedure is then proposed to solve the optimization problem formulated in the first optimization phase.
The optimization is formulated as a quadratic problem for disciplined convex programming that enables the problem to be analyzed and solved efficiently.
Secondly, an optimization model, formulated to specifically address the discussion comparing both configuration paradigms.
Finally, the optimization problem formulated as MILP is solved to derive the best system.
The optimization problem formulated as non-linear programming task has been solved by improved gradient method.
The optimization is formulated as a mixed-integer non-linear program.
The method is carried out by solving a min-max optimization problem formulated in frequency domain.
The objective function of the on-line optimization is formulated in terms of a Structured Singular Value.
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