• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2536-2543
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• 2013

In this paper, we proposed the modeling in one zone buildings and the energy efficient temperature control algorithm using particle swarm optimization (PSO). A control horizon switching method with PSO is used for optimal control, and the TOU tariff is included to calculate the energy costs. Simulation results show that the reductions of energy cost and peak power can be obtained using proposed algorithms.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.303-304
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• 2008

For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.39-44
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• 2011

In this paper, we propose a method to improve the Firefly Algorithm(FA) introduced by Xin-She Yang, recently. We design and analyze the improved firefly algorithm based on the heuristic. We compare the FA with the Particle Swarm Optimization (PSO) which the problem domain is similar with the FA in terms of accuracy, algorithm convergence time, the motion of each particle. The compare experiments show that the accuracy of FA is not worse than PSO's, but the convergence time of FA is slower than PSO's. In this paper, we consider intuitive reasons of slow convergence time problem of FA, and propose the improved version of FA using a partial mutation heuristic based on the consideration. The experiments using benchmark functions show the accuracy and convergence time of the improved FA are better than them of PSO and original FA.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.490-498
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• 2016

Proportional Integral Derivative (PID) controller tuning is an area of interest for researchers in many areas of science and engineering. This paper presents a new algorithm for PID controller tuning based on a combination of bacteria foraging and particle swarm optimization. BFO algorithm has recently emerged as a very powerful technique for real parameter optimization. To overcome delay in an optimization, combine the features of BFOA and PSO for tuning the PID controller. This new algorithm is proposed to combine both the algorithms to get better optimization values. The real time prototype model of paper machine is designed and controlled by using PIC microcontroller embedded with the programming in C language.

• Journal of Distribution Science
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• v.12 no.3
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• pp.33-41
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• 2014

Purpose - The purpose of this study was to predict the selection of independent auditors in the companies listed on the Tehran Stock Exchange (TSE) using a combination of PSO algorithm and CART. This study involves applied research. Design, approach and methodology - The population consisted of all the companies listed on TSE during the period 2005-2010, and the sample included 576 data specimens from 95 companies during six consecutive years. The independent variables in the study were the financial ratios of the sample companies, which were analyzed using two data mining techniques, namely, PSO algorithm and CART. Results - The results of this study showed that among the analyzed variables, total assets, current assets, audit fee, working capital, current ratio, debt ratio, solvency ratio, turnover, and capital were predictors of independent auditor selection. Conclusion - The current study is practically the first to focus on this topic in the specific context of Iran. In this regard, the study may be valuable for application in developing countries.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.36-39
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• 2009

We developed an optimal design interface based on VBA(Visual Basic Application) that takes advantage of API(Application Program Interface) function of commonly used EM analysis software. The developed interface is adopted for an optimal design of a septum in a waveguide tee junction using PSO(Particle Swarm Optimization) algorithm. The objective function of the optimal design is defined by $S_{11}$-parameter of the waveguide tee junction Design variables are established as position of the septum, that are changed to satisfy the design goal Using the developed design interface and PSO algorithm, the objective function converged to the smallest value, showing the validity of the proposed method. The design interface was developed using Microsoft Excel software, enabling easy control of design parameters for user. Also, various analysis parameters can be set in the Excel interface, including waveguide input mode and frequency. After completion of the design, field solutions at user-specified positrons can be extracted to the output files in complex number form.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.59-64
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• 2022

Vibration is one of the factors that degrades the performance of equipment and measurement equipment used in high-tech industries such as semiconductors and display. The vibration isolator is classified into passive type and active type. The passive vibration isolator has the weakness of insufficient vibration isolation performance in the low frequency band, so an active vibration control system that can overcome these problems is used recently. In this paper, PID controller is used to control the active vibration isolator. Methods for setting the gain of the PID controller include the Zeigler-Nichols method, the pole placement method. These methods have the disadvantage of requiring a lot of time or knowing the system model accurately. This paper proposes the gain auto tuning method of the active vibration isolator applied with the PSO algorithm, which is an optimization algorithm that is easy to implement and has stable convergence performance with low calculations. It is expected that it will be possible to improve vibration isolation performance and reduce the time required for gain tuning by applying the proposed PSO algorithm to the active vibration isolator.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.319-326
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• 2009

PSO(Particle Swarm Optimization) is an optimization algorithm in which simple particles search an optimal solution using shared information acquired through their own experiences. PSO applications are so numerous and diverse. Lots of researches have been made mainly on the parameter settings, topology, particle's movement in order to achieve fast convergence to proper regions of search space for optimization. In standard PSO, since each particle uses only information of its and best neighbor, swarm does not explore diverse regions and intended to premature to local optima. In this paper, we propose a new particle's movement strategy in order to explore diverse regions of search space. The strategy is that each particle moves according to relative weights of several better neighbors. The strategy of exploring diverse regions is effective and produces less local optimizations and accelerating of the optimization speed and higher success rates than standard PSO. Also, in order to raise success rates, we propose a strategy for checking whether swarm falls into local optimum. The new PSO algorithm with these two strategies shows the improvement in the search speed and success rate in the test of benchmark functions.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.527-531
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• 2012

In this paper, a novel DPSO-QI (Distributed PSO with quantum-infusion mechanism) algorithm improving one of the fatal defect, the so-called premature convergence, that degrades the performance of the conventional PSO algorithms is proposed. The proposed scheme has the following two distinguished features. First, a concept of neighborhood of each particle is introduced, which divides the whole swarm into several small groups with an appropriate size. Such a strategy restricts the information exchange between particles to be done only in each small group. It thus results in the improvement of particles' diversity and further minimization of a probability of occurring the premature convergence phenomena. Second, a quantum-infusion (QI) mechanism based on the quantum mechanics is introduced to generate a meaningful offspring in each small group. This offspring in our PSO mechanism improves the ability to explore a wider area precisely compared to the conventional one, so that the degree of precision of the algorithm is improved. Finally, some numerical results are compared with those of the conventional researches, which clearly demonstrates the effectiveness and reliability of the proposed DPSO-QI algorithm.

• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.803-812
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• 2018

The purpose of this study is to develop Particle Swarm Optimization (PSO) automatic calibration algorithm with multi-objective functions by Python, and to evaluate the applicability by applying the algorithm to the Soil and Water Assessment Tool (SWAT) watershed modeling. The study area is the upstream watershed of Gongdo observation station of Anseongcheon watershed ($364.8km^2$) and the daily observed streamflow data from 2000 to 2015 were used. The PSO automatic algorithm calibrated SWAT streamflow by coefficient of determination ($R^2$), root mean square error (RMSE), Nash-Sutcliffe efficiency ($NSE_Q$), and especially including $NSE_{INQ}$ (Inverse Q) for lateral, base flow calibration. The results between automatic and manual calibration showed $R^2$ of 0.64 and 0.55, RMSE of 0.59 and 0.58, $NSE_Q$ of 0.78 and 0.75, and $NSE_{INQ}$ of 0.45 and 0.09, respectively. The PSO automatic calibration algorithm showed an improvement especially the streamflow recession phase and remedied the limitation of manual calibration by including new parameter (RCHRG_DP) and considering parameters range.