• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.11 no.2
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• pp.34-38
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• 2012

In this paper, the particle swarm optimization (PSO) algorithm is adopted to design a modified ring-slot type patch rectenna with a resonance frequency of 2.45GHz. In order to accomplish minimization of dimensions and circular polarization (CP) and harmonic suppression, axial direction slits and side-cuts are added to the patch of the ring. The PSO manipulated this kind of multi-dimensional problem very well, and as a result, the designed rectenna shows a desirable performance of return loss of 21.36dB and axial ratio of 2.92dB at the frequency of 2.45GHz with compact sizing.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.222-224
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• 2006

This paper addresses a particle swarm optimization-based approach for solving a generating unit maintenance scheduling problem(GMS) with some constraints. We focus on the power system reliability such as reserve ratio better than cost function as the objective function of GMS problem. It is shown that particle swarm optimization-based method is effective in obtaining feasible schedules such as GMS problem related to power system planning and operation. In this paper, we find the optimal solution of the GMS problem within a specific time horizon using particle swarm optimization algorithm. Simple case study with 16-generators system is applicable to the GMS problem. From the result, we can conclude that PSO is enough to look for the optimal solution properly in the generating unit maintenance scheduling problem.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.797-804
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• 2013

This paper presents an efficient design method of multiplierless 2-D state space digital filter based on a particle swarm optimization(PSO) algorithm. The design task is reformulated as a constrained minimization problem and is solved by our newly developed PSO algorithm. To ensure the stability of the designed 2-D state space digital filters, a stability strategy is embedded in the basic PSO algorithm. The superiority of the proposed method is demonstrated by several experiments. The results show that the approximation error and roundoff noise of the resultant filters are better than those of the digital filters which designed by recently published filter design methods. In addition, the designed filters with power-of-two coefficients have only about 1/4 computational burden of the 2-D digital filters designed in the 2's complement binary representation.

• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.179-190
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• 2010

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.185-188
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• 2009

In this paper, Position Controller for balance of Seesaw System design using PID Algorithm. Seesaw System is that it's system use widely to analyze of ship or flight dynamics, Inverted Pendulumand, Robot System, manage system for theory of modern control system and all sorts of analysis. In case of Seesaw System, it's necessity that understand and analysis of system and correct selection of parameter because the system is strong nonlinear control system. It guarantees efficiency and stability to adapt quickly for disturbance or change of controller from PID Algorithm of guarantee safe from simple and long history and PSO(Particle Swarm Optimization) that sort of metaheuristic optimization that need to accuracy and fast PID parameter tuning.

• Journal of Electrical Engineering and Technology
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• v.6 no.3
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• pp.334-341
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• 2011

This paper presents accurate solutions for nonlinear transcendental equations of the selective harmonic elimination technique used in three-phase PWM inverters feeding the induction motor by particle swarm optimization (PSO). With the proposed approach, the required switching angles are computed efficiently to eliminate low order harmonics up to the $23^{rd}$ from the inverter voltage waveform, whereas the magnitude of the fundamental component is controlled to the desired value. A set of solutions and the evaluation of the proposed method are presented. The obtained results prove that the algorithm converges to a precise solution after several iterations. The salient contribution of the paper is the application of the particle swarm algorithm to attenuate successfully any undesired loworder harmonics from the inverter output voltage. The current paper demonstrates that the PSO is a promising approach to control the operation of a three-phase voltage source inverter with a selective harmonic elimination strategy to be applied in induction motor drives.

• Structural Engineering and Mechanics
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• v.50 no.3
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• pp.323-347
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• 2014

This paper proposes a Particle Swarm Optimization (PSO) algorithm, which is improved by making use of the Harmony Search (HS) approach and called HS-PSO algorithm. A computer code is developed for optimal sizing design of non-linear steel frames with various semi-rigid and rigid beam-to-column connections based on the HS-PSO algorithm. The developed code selects suitable sections for beams and columns, from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange W-shapes, such that the minimum total cost, which comprises total member plus connection costs, is obtained. Stress and displacement constraints of AISC-LRFD code together with the size constraints are imposed on the frame in the optimal design procedure. The nonlinear moment-rotation behavior of connections is modeled using the Frye-Morris polynomial model. Moreover, the P-${\Delta}$ effects of beam-column members are taken into account in the non-linear structural analysis. Three benchmark design examples with several types of connections are presented and the results are compared with those of standard PSO and of other researches as well. The comparison shows that the proposed HS-PSO algorithm performs better both than the PSO and the Big Bang-Big Crunch (BB-BC) methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.9-17
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• 2014

This paper proposes a binary particle swarm optimization (BPSO) algorithm for distributed node localization in wireless sensor networks (WSNs). Each unknown node performs localization using the value of the measured distances from three or more neighboring anchors, i.e., nodes that know their location information. The node that is localized during the localization process is then used as another anchor for remaining nodes. The performances of particle swarm optimization (PSO) and BPSO in terms of localization error and computation time are compared by using simulations in Matlab. The simulation results indicate that PSO-based localization is more accurate. In contrast, BPSO algorithm performs faster for finding the location of unknown nodes for distributed localization. In addition, the effects of transmission range and number of anchor nodes on the localization error and computation time are investigated.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• 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.