• Current Optics and Photonics
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• v.7 no.3
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• pp.248-253
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• 2023

An efficient global optimization approach for a periodic plasmonic nanoslit array based on extraordinary optical transmission within an acceptable time range is proposed using 𝚀 factor analysis method. The particle swarm optimization is employed as a global optimization tool. The figure of merit is defined as a product of transmission peak value and 𝚀 factor. The design variables are the slit width, height, and period of the slit array, respectively. The optical properties such as transmission spectrum and bandwidth are calculated rigorously using the finite element method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.3-10
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• 1998

Optimization problems may be devided into geometry optimization problems and topology optimization problems. In this paper, a method using tile equivalent material properties prediction techniques of a particulate-reinforced composites is proposed for the topology optimization. This method makes use of penalty factor in order that regions with intermediate value of design variables can be penalized. The computational results being obtained from PLBA algorithm of some values of penalty factor are presented.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.227-242
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• 2015

This paper proposes a weighting factor optimization method in predictive control algorithm for torque ripple reduction in an induction motor fed by an indirect matrix converter (IMC). In this paper, the torque ripple behavior is analyzed to validate the proposed weighting factor optimization method in the predictive control platform and shows the effectiveness of the system. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponds to minimum torque ripple and is compared with the results of conventional weighting factor based predictive control algorithm. The predictive control algorithm selects the optimum switching state that minimizes a cost function based on optimized weighting factor to actuate the indirect matrix converter. The conventional and introduced weighting factor optimization method in predictive control algorithm are validated through simulations and experimental validation in DS1104 R&D controller platform and show the potential control, tracking of variables with their respective references and consequently reduces the torque ripple.

• East Asian mathematical journal
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• v.34 no.1
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• pp.1-16
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• 2018

We study an optimization problem for hyperbolic absolute risk aversion (HARA) utility function under two-factor Heston's stochastic volatility model. It is not possible to obtain an explicit solution because our financial market model is complicated. However, by using asymptotic analysis technique, we find the explicit forms of the approximations of the optimal value function and the optimal strategy for HARA utility function.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.450-454
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• 2015

Short execution time is the major performance factor for computer systems. This performance factor is directly determined by code quality, which is influenced by the compiler's optimizations. However, a compiler has limitations when optimizing source code due to insufficient information. Thus, if programmers can learn the reasons why a compiler fails to apply optimizations, they can rewrite code that is more easily understood by the compiler, and thus improve performance. In this paper, we propose a compiler that provides a programmer with reasons for failed optimization and recognizes programmer's additional information to obtain better optimization. As a result, we obtain performance improvement, i.e., reducing execution time and code size, by taking advantage of additional optimization opportunities.

• Food Science of Animal Resources
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• v.43 no.2
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• pp.374-381
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• 2023

In a previous study, 'response surface methodology (RSM) using a fullest balanced model' was proposed to improve the optimization of food processing when a standard second-order model has a significant lack of fit. However, that methodology can be used when each factor of the experimental design has five levels. In response surface experiments for optimization, not only five-level designs, but also three-level designs are used. Therefore, the present study aimed to improve the optimization of food processing when the experimental factors have three levels through a new approach to RSM. This approach employs three-step modeling based on a second-order model, a balanced higher-order model, and a balanced highest-order model. The dataset from the experimental data in a three-level, two-factor central composite design in a previous research was used to illustrate three-step modeling and the subsequent optimization. The proposed approach to RSM predicted improved results of optimization, which are different from the predicted optimization results in the previous research.

• Journal of Power Electronics
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• v.16 no.2
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• pp.610-620
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• 2016

The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method.

• Journal of Power Electronics
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• v.11 no.1
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• pp.105-111
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• 2011

By having accurate knowledge of the magnetic field distribution and the thrust calculation in linear synchronous motors, assessing the performance and optimization of the motor design are possible. In this paper, after carrying out a performance analysis of a single-sided wound secondary linear synchronous motor by varying the motor design parameters in a layer model and a d-q model, machine single- and multi-objective design optimizations are carried out to improve the thrust density of the motor based on the motor weight and the motor efficiency multiplied by its power factor by defining various objective functions including a flexible objective function. A genetic algorithm is employed to search for the optimal design. The results confirm that an overall improvement in the thrust mean, efficiency multiplied by the power factor, and thrust to the motor weight ratio are obtained. Several design conclusions are drawn from the motor analysis and the design optimization. Finally, a finite element analysis is employed to evaluate the effectiveness of the employed machine models and the proposed optimization method.

• Nuclear Engineering and Technology
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• v.48 no.5
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• pp.1126-1139
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• 2016

In this research, for the first time, a new optimization method, i.e., strength Pareto evolutionary algorithm II (SPEA-II), is developed for the burnable poison placement (BPP) optimization of a nuclear reactor core. In the BPP problem, an optimized placement map of fuel assemblies with burnable poison is searched for a given core loading pattern according to defined objectives. In this work, SPEA-II coupled with a nodal expansion code is used for solving the BPP problem of Kraftwerk Union AG (KWU) pressurized water reactor. Our optimization goal for the BPP is to achieve a greater multiplication factor ($K_{eff}$) for gaining possible longer operation cycles along with more flattening of fuel assembly relative power distribution, considering a safety constraint on the radial power peaking factor. For appraising the proposed methodology, the basic approach, i.e., SPEA, is also developed in order to compare obtained results. In general, results reveal the acceptance performance and high strength of SPEA, particularly its new version, i.e., SPEA-II, in achieving a semioptimized loading pattern for the BPP optimization of KWU pressurized water reactor.

• Steel and Composite Structures
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• v.51 no.2
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• pp.203-223
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• 2024

This study shows functionally graded material structural topology optimization under buckling constraints. The SIMP (Solid Isotropic Material with Penalization) material model is used and a method of moving asymptotes is also employed to update topology design variables. In this study, the quadrilateral element is applied to compute buckling load factors. Instead of artificial density properties, functionally graded materials are newly assigned to distribute optimal topology materials depending on the buckling load factors in a given design domain. Buckling load factor formulations are derived and confirmed by the resistance of functionally graded material properties. However, buckling constraints for functionally graded material topology optimization have not been dealt with in single material. Therefore, this study aims to find the minimum compliance topology optimization and the buckling load factor in designing the structures under buckling constraints and generate the functionally graded material distribution with asymmetric stiffness properties that minimize the compliance. Numerical examples verify the superiority and reliability of the present method.