Sequestering CO2 in the form of carbon-based liquid fuels would provide both a convenient and sustainable form of energy for practical use as well as mitigate the effects of global warming and climate change.

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Fluctuations in manufactured integrated circuit parameters may dramatically reduce the parametric yield. Yield maximization can be formulated as an unconstrained optimization problem in nominal parameter values, which is known as design centering. The high expense of yield evaluations, the absence of any gradient information, and the presence of some numerical noise obstruct the use of the traditional derivative-based optimization methods. In this article, a novel design centering algorithm is presented, which consists of a non-derivative unconstrained optimizer coupled with a variance reduction estimator. The used optimizer combines a trust region mechanism with quadratic interpolation and provides efficient use of yield evaluations. The stratified sampling technique is used to develop a lower variance yield estimator that reduces the number of circuit simulations required to reach a desired accuracy level. Numerical and practical circuit examples are used to demonstrate the efficiency of the proposed algorithm with respect to other methods in the same field.

CitationAgambayev

In this paper, a novel nano antenna with two radiation modes is introduced. The structure of this nano antenna consists of a ring coupler and two patch antennas placed on a SiO 2 substrate. The direction of the main lobe of the radiation pattern of this nano antenna can be adjusted to be either in the broadside or the endfire direction. The proposed nano antenna is optimized to minimize the losses and to maximize the radiation efficiency in addition to achieve maximum discrimination between the two desired directions of the main beam. In optimizing the proposed structure, the computationally expensive fullwave electromagnetic simulation is replaced by cheaper surrogate models, which are kriging models. Two optimization techniques, namely multi-objective particle swarm with preference ranking organization METHod for enrichment evaluations method and design centering using the normed distances, are used to obtain the optimal values of the design parameters. A convergence test is performed to ensure the validity of the obtained simulation results. A sensitivity analysis is performed to show how the manufacturing tolerance in each design parameter is affecting the performance of the proposed nano antenna.

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In this article, a novel derivative-free (DF) surrogate-based trust region optimization approach is proposed. In the proposed approach, quadratic surrogate models are constructed and successively updated. The generated surrogate model is then optimized instead of the underlined objective function over trust regions. Truncated conjugate gradients are employed to find the optimal point within each trust region. The approach constructs the initial quadratic surrogate model using few data points of order O(n), where n is the number of design variables. The proposed approach adopts weighted least squares fitting for updating the surrogate model instead of interpolation which is commonly used in DF optimization. This makes the approach more suitable for stochastic optimization and for functions subject to numerical error. The weights are assigned to give more emphasis to points close to the current center point. The accuracy and efficiency of the proposed approach are demonstrated by applying it to a set of classical bench-mark test problems. It is also employed to find the optimal design of RF cavity linear accelerator with a comparison analysis with a recent optimization technique.