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Each measure indicates difficulties for optimization routines encountered when optimizing the objective function.
The combinations of optimum operational paradigms are obtained by optimizing the objective function with ACO (ant colony optimization) and MBFA (modified bacteria foraging algorithm).
A fast analytic-iterative scheme is proposed to efficiently compute the optimal parameters so as to resolve the computational burden required for optimizing the objective function numerically.
Optimizing the objective vector function of each sensor node can achieve the global optimal solution.
In iteration, is found by optimizing the objective function with respect to while keeping the bandwidth constraints satisfied, and fixing.
In iteration, we update by optimizing the objective function over while keeping the total power constraints satisfied, and fixing.
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A robust optimization formulation seeks for optimal solutions that optimize the objective function and meet the problem requirements for all possible uncertainties in constraint coefficients.
The resulting output is passed to the "Single objective optimizer" tool which optimizes the objective function and provides the output as fluxes of all reactions.
Finally, Section Objective function optimization describes the algorithm to optimize the objective function.
Finally, another tool "Reaction knock-out optimizer" deletes the pairs sequentially and optimizes the objective function.
Stochastic optimization combined with a generalized Monte Carlo scheme is used to optimize the objective functions, the effective piezoelectric coefficients d31 and d15.
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