• Journal of Electrical Engineering and Technology
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• v.6 no.2
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• pp.182-191
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• 2011

Genetic algorithms (GA) and particle swarm optimization (PSO) are the most famous optimization techniques among various modern heuristic optimization techniques. These two approaches identify the solution to a given objective function, but they employ different strategies and computational effort; therefore, a comparison of their performance is needed. This paper presents the application and performance comparison of the PSO and GA optimization techniques for a static synchronous series compensator-based controller design. The design objective is to enhance power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem, and both PSO and GA optimization techniques are employed to search for the optimal controller parameters.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1237-1253
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• 2018

Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and can solve different optimization problems. In order to further improve the performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four complex function optimization and pressure vessel optimization problems were simulated using the optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to find global search and evolutionary speed. Optimization effect simulation results are satisfactory.

• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.212-220
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• 2012

In the current paper, the optimization shape of a polysilicon variable-capacitance micromotor (VCM) was determined using the seeker optimization algorithm (SOA). The optimum goal of the algorithm was to find the maximum torque value and minimum ripple torque by varying the geometrical parameters. The optimization process was performed using a combination of SOA and the finite-element method (FEM). The fitness value was calculated via FEM analysis using COMSOL3.4, and SOA was realized by MATLAB7.4. The proposed method was applied to a VCM with eight and six poles at the stator and rotor, respectively. For comparison, this optimization was also performed using the genetic algorithm. The results show that the optimized micromotor using SOA had a higher torque value and lower torque ripple, indicating the validity of this methodology for VCM design.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.392-400
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• 2008

This paper proposes a novel optimization algorithm inspired by bacteria behavior patterns for foraging. Most bacteria can trace attractant chemical molecules for foraging. This tracing capability of bacteria called chemotaxis might be optimized for foraging because it has been evolved for few millenniums. From this observation, we developed a new optimization algorithm based on the chemotaxis of bacteria in this paper. We first define behavior and decision rules based on the behavior patterns of bacteria and then devise an optimization algorithm with these behavior and decision rules. Generally bacteria have a quorum sensing mechanism that makes it possible to effectively forage, but we leave its implementation as a further work for simplicity. Thereby, we call our algorithm a simple bacteria cooperative optimization (BCO) algorithm. Our simple BCO is tested with four function optimization problems on various' parameters of the algorithm. It was found from experiments that the simple BCO can be a good framework for optimization.

• Journal of the Korean Statistical Society
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• v.32 no.1
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• pp.1-10
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• 2003

We consider obtaining graphical summaries of uncertainty in estimates of parameters in nonlinear models. A nonlinear constrained optimization algorithm is developed for likelihood based confidence intervals for the functions of parameters in the model The results are applied to the problem of finding significance levels in nonlinear models.

• Journal of Biomedical Engineering Research
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• v.31 no.4
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• pp.310-315
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• 2010

Estimation of muscle forces is important in biomechanics, therefore many researchers have tried to build a muscle model. Recently, optimization techniques for adjusting muscle parameters, i.e. EMG-driven model, have been used to estimate muscle forces and predict joint moments. In this study, an EMG-driven model based on the previous studies has been developed and isometric and isokinetic contraction movements were evaluated to validate the developed model. One healthy male participated in this study. The dynamometer tasks were performed for maximum voluntary isometric contractions (MVIC) for ankle dorsi/plantarflexors, isokinetic contraction at both $30^{\circ}/s$ and $60^{\circ}/s$. EMGs were recorded from the tibialis anterior, gastrocnemius medialis, gastrocnemius lateralis and soleus muscles at the sampling rate of 1000 Hz. The MVIC trial was used to customize the EMG-driven model to the specific subject. Once the subject's own model was developed, the model was used to predict the ankle joint moment for the other two dynamic movements. When no optimization was applied to characterize the muscle parameters, weak correlations were observed between the model prediction and the measured joint moment with large RMS error over 100% (r = 0.468 (123%) and r = 0.060 (159%) in $30^{\circ}/s$ and $60^{\circ}/s$ dynamic movements, respectively). However, once optimization was applied to adjust the muscle parameters, the predicted joint moment was highly similar to the measured joint moment with relatively small RMS error below 40% (r = 0.955 (21%) and r = 0.819 (36%) and in $30^{\circ}/s$ and $60^{\circ}/s$ dynamic movements, respectively). We expect that our EMG-driven model will be employed in our future efforts to estimate muscle forces of the elderly.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.11
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• pp.1765-1771
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• 2010

In the case of micro.electro-mechanical system (MEMS) devices, the quality of punched via hole is one of the most important factors governing the performance of the device. The common features that affect the laser micromachining of via holes drilled by using Nd:$YVO_4$ laser are described, and efficient optimization methods to measure them are presented. The analysis methods involving an orthogonal array, analysis of variance (ANOVA), and response surface optimization are employed to determine the main effects and to determine the optimal laser process parameters. The significant laser process parameters were identified and their effects on the quality of via holes were studied. Finally, an experiment in which the optimal levels of the laser process parameters were used was carried out to demonstrate the effectiveness of the optimization method.

• Structural Engineering and Mechanics
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• v.63 no.2
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• pp.147-160
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• 2017

In this study, the optimum parameters of Tuned Mass Dampers (TMDs) are proposed using Gravity Search Algorithm (GSA) and Particle Swarm Optimization (PSO) to reduce the responses of the structures. A MATLAB program is developed to apply the new approach to the benchmark 10 and 40-story structures. The obtained results are compared to those of other optimization methods used in the literature to verify the developed code. To show the efficiency and accuracy of the proposed methods, nine far-field and near-field worldwide earthquakes are applied to the structures. The results reveal that in the 40-story structure, GSA algorithm can reduce the Relative Displacement (RD) and Absolute Acceleration (AA) up to 43% and 21%, respectively while the PSO decreases them by 50% and 25%, respectively. In contrast, both GSA and PSO algorithms reduce the RD and AA about 29% and 21% for the 10-story structure. Furthermore, using the proposed approach the required TMD parameters reduce by 47% and 63% in the 40 and 10-story buildings in comparison with the referenced ones. Result evaluation and related comparison indicate that these methods are more effective even by using smaller TMD parameters resulting in the reduction of acting force from TMD, having smaller stiffness and damping factors while being more cost effective due to its decreased parameters. In other words, the TMD with optimum parameters can play a positive role in both tall and typical structures.

• Journal of Astronomy and Space Sciences
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• v.38 no.3
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• pp.157-164
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• 2021

We describe a method for the in-orbit calibration of body-mounted magnetometers based on the CHAOS-7 geomagnetic field model. The code is designed to find the true calibration parameters autonomously by using only the onboard magnetometer data and the corresponding CHAOS outputs. As the model output and satellite data have different coordinate systems, they are first transformed to a Star Tracker Coordinate (STC). Then, non-linear optimization processes are run to minimize the differences between the CHAOS-7 model and satellite data in the STC. The process finally searches out a suite of calibration parameters that can maximize the model-data agreement. These parameters include the instrument gain, offset, axis orthogonality, and Euler rotation matrices between the magnetometer frame and the STC. To validate the performance of the Python code, we first produce pseudo satellite data by convoluting CHAOS-7 model outputs with a prescribed set of the 'true' calibration parameters. Then, we let the code autonomously undistort the pseudo satellite data through optimization processes, which ultimately track down the initially prescribed calibration parameters. The reconstructed parameters are in good agreement with the prescribed (true) ones, which demonstrates that the code can be used for actual instrument data calibration. This study is performed using Python 3.8.5, NumPy 1.19.2, SciPy 1.6, AstroPy 4.2, SpacePy 0.2.1, and ChaosmagPy 0.5 including the CHAOS-7.6 geomagnetic field model. This code will be utilized for processing NextSat-1 and Small scale magNetospheric and Ionospheric Plasma Experiment (SNIPE) data in the future.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.9
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• pp.472-481
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• 2017

Radiant floor heating systems have been used as common heating supply systems in most residential buildings in Korea. Since the system uses a floor as thermal storage, proper control strategy should be adopted to avoid over-or under-heating problems. So far, studies related to control of the floor heating system have been conducted based on computer simulations. The active layer in TRNSYS is known for its usability as a floor heating system model and is integrated with the TRNSYS building model (Type 56). However, floor heating system simulations with the active layer are operated only if pre-defined minimum mass flow rate is ensured. This study proposes a simple RC (Resistance-Capacitance) model for radiant floor heating systems. Model parameters such as Rs and Cs are defined by optimization. The active layer, in this study, is used as the target system to search for optimal values. A TRNOPT optimization tool was used to conduct optimization under given simulation conditions. The RC model with optimal parameters are tested in other mass flow rates that were not used during optimization. Results reveal the RC model describes the active layer with successfully optimized model parameters. The RC model has fewer model limitations, and is expected to be used for various target systems, e.g. experimental data of a real radiant heating system.