Because of the demanding computing requirements of the optimization process, a special mesh is designed and a convergence analysis is carried out before using the multiphysics model.
The optimum operational parameters that fulfill the requirements of the optimization function resulted in areal biomass productivity of 32.1 gdwm−2d−1 and areal lipid production of 198.4 gLipidm−2.
Subsequently, order reduction techniques for dissipative partial-differential equations are combined with adaptive tabulation of microscopic simulation data to reduce the computational requirements of the optimization problem, which is then solved using standard search algorithms.
To reduce the computational requirements of the optimization procedure, several strategies are proposed, including a novel stopping criterion, which provides an efficient way to end the calculations when the optimal solution has been found.
The ANN-based causality analysis is further explored to determine dominant design variables for each of three design requirements for the optimization.
The methodology presented here effectively integrates operating restrictions, information-theoretic requirements, and state-of-the-art optimization techniques to design minimum crest factor multisine signals meeting important user-specified time and frequency domain properties.
The numerical results presented in this paper leads to fulfill the requirement of significant optimization of the important design parameters to achieve a high SNR and sensitivity of a fiber optic SPR sensor with nanoparticle films.
It is worth to mention that the feasibility problem (17) can be easily checked without the requirement of an optimization solver.
In addition, we further consider end-to-end traffic delay requirements since the optimization of link weights for load-balancing and energy savings may introduce substantially increased traffic delay after link sleeping.
Support vector machine is a novel machine learning methodology based on SLT, which has considerable features including the fact that requirement on kernel and nature of the optimization problem results in a uniquely global optimum, high generalization performance, and prevention from converging to a local optimal solution.
