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In order to determine the spherical harmonic basis function relationship, an additional weight (0,1) of each spherical harmonic coefficient as random variable is used to search for the optimal variables based on genetic optimization.
A low energy cost optimization can be designed to choose the optimal variables to minimize the total energy cost based on the energy consumption function when given the required conditions S and d.
Observe that recovering optimal variables is more important than knowing the maximum yield because optimal variables are needed for system implementation.
We now denote by { C n 0 ∗ } the optimal variables of (17).
Two optimal variables, including prestress level and cross stress, are considered respectively.
Using (9), (10), and (14) and induction over, one easily concludes that optimal variables are unique, too.
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This paper proposes an online dynamically optimized algorithm for optimal variable charge-rate scheduling of PEVs based on coordinated aggregated particle swarm optimization (CAPSO).
In this paper, a sequential approximate multi-objective optimization method is employed to obtain the data-driven optimal variable blank holder forces in sheet forming under interval uncertainty.
Regression models were fitted to refrigeration and electrical powers and optimal variable settings were identified using an easy-to-use optimization criterion.
The optimal variable node degree distributions are found through density evolution using the Gaussian approximation [1].
Furthermore, an optimal variable sampling interval EWMA average loss chart is also proposed.
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