• Structural Engineering and Mechanics
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• 제50권3호
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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.

• 정보처리학회논문지B
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• 제18B권1호
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• pp.39-44
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• 2011

본 논문에서는 최근 Xin-She Yang에 의해 소개된 반딧불이 알고리즘(FA)에 휴리스틱을 적용하여 개선하는 방안을 제안한다. 또한 이를 위하여 기존의 FA를 이와 유사한 문제영역의 알고리즘인 Particle Swarm Optimization(PSO)와 정확도 측면, 수렴 시간 측면, 각 입자의 움직임 측면에서 비교 분석한다. 비교 실험 결과, FA의 정확도는 PSO보다 나쁘지 않았지만, 수렴 속도는 느린 것으로 나타났다. 본 논문은 이에 대한 직관적인 원인을 고찰하고, 이를 극복하기 위해, 기존의 FA에 부분 돌연변이 휴리스틱을 적용하여 개선된 FA(Improved FA)를 제안한다. 벤치마크 함수들을 최적화 하는 비교 실험 결과, 개선된 FA가 PSO와 기존의 FA보다 정확도와 수렴속도 측면에서 우수함을 보이고자 한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.778-779
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• 2007

기술의 진보와 친환경적 시대의 조류에 발맞추어 배전계통에서 분산전원의 이용은 더욱 증가하는 추세이다. 따라서 저용량의 다양한 분산전원을 하나의 가상발전소(Virtual Power Plant, VPP)로 운영하는 개념이 도입되고 있다. 본 논문은 Particle Swarm Optimization(PSO) 알고리즘을 이용하여 경제적 효율을 고려한 VPP의 최적구성을 다룬다. 사례연구로는 다양한 분산전원으로 구성된 시스템의 시간별 전력 및 열에너지 수요를 고려한 VPP의 최적구성을 수행한다.

• 한국산학기술학회논문지
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• 제14권5호
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• pp.2536-2543
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• 2013

본 논문에서는 단일존 빌딩의 모델링과 PSO 알고리즘을 이용한 냉방시스템 제어구간 건물 실내온도 제어 알고리즘을 제안한다. 최적제어를 하기 위한 제어구간 설정은 스위칭방법과 PSO 알고리즘을 사용하고 냉방시스템 사용요금은 TOU와 피크요금을 포함 하여 산정한다. 시뮬레이션을 통해 제안한 제어구간 설정방법을 적용하면 전력 사용에 따른 비용의 절감과 피크전력 절감을 확인할 수 있다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.381-383
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• 2009

본 논문에서는 대표적인 시스템 모델링 도구중의 하나인 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)를 설계하고 모델을 최적화하기 위하여 최적화 알고리즘인 PSO(Particle Swarm Optimization) 알고리즘을 이용하였다. 즉, 모델의 최적화에 주요한 영향을 미치는 모델의 파라미터들을 PSO 알고리즘을 이용하여 동정한다. 제안된 RBF 뉴럴 네트워크는 은닉층에서의 활성함수로서 일반적으로 많이 사용되어지는 가우시안 커널함수를 사용한다. 더 나아가 모델의 최적화를 위하여 각 커널함수의 중심값은 HCM 클러스터링에 기반을 두어 중심값을 결정하고, PSO 알고리즘을 통하여 가우시안 커널함수의 분포상수, 은닉층에서의 노드 수 그리고 다수의 입력을 가질 경우 입력의 종류를 동정한다. 제안한 모델의 성능을 평가하기 위해 Mackey-Glass 시계열 공정 데이터를 적용하였으며 제안된 모델의 근사화와 일반화 능력을 분석한다.

• 제어로봇시스템학회논문지
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• 제18권4호
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• pp.353-358
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• 2012

Human detection is a challenging task in many fields because it is difficult to detect humans due to their variable appearance and posture. Furthermore, it is also hard to track the detected human because of their dynamic and unpredictable behavior. The evaluation speed of method is also important as well as its accuracy. In this paper, we propose unified detection and tracking method for humans using Gaussian-PSO (Gaussian Particle Swarm Optimization) with the HOG (Histograms of Oriented Gradients) features to achieve a fast and accurate performance. Keeping the robustness of HOG features on human detection, we raise the process speed in detection and tracking so that it can be used for real-time applications. These advantages are given by a simple process which needs just one linear-SVM classifier with HOG features and Gaussian-PSO procedure for the both of detection and tracking.

• Structural Engineering and Mechanics
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• 제83권5호
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• pp.593-607
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• 2022

To solve the problem of detecting structural damage, a two-stage method using the Kalman filter and Particle Swarm Optimization (PSO) is proposed. In this method, the first PSO population is enhanced using the Kalman filter method based on dynamic responses. Due to noise in the sensor responses and errors in the damage detection process, the accuracy of the damage detection process is reduced. This method proposes a novel approach for solve this problem by integrating the Kalman filter and sensitivity analysis. In the Kalman filter, an approximate damage equation is considered as the equation of state and the damage detection equation based on sensitivity analysis is considered as the observation equation. The first population of PSO are the random damage scenarios. These damage scenarios are estimated using a step of the Kalman filter. The results of this stage are then used to detect the exact location of the damage and its severity with the PSO algorithm. The efficiency of the proposed method is investigated using three numerical examples: a 31-element planer truss, a 52-element space dome, and a 56-element space truss. In these examples, damage is detected for several scenarios in two states: using the no noise responses and using the noisy responses. The results show that the precision and efficiency of the proposed method are appropriate in structural damage detection.

• 한국음향학회지
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• 제28권4호
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• pp.363-369
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• 2009

본 논문에서는 인접된 다중모드 공진점을 갖는 수중 음향 압전 트랜스듀서의 전기적 등가회로 모델을 추정하는 방법을 제안하였다. 트랜스듀서의 실측된 임피던스와 추정된 등가모델의 임피던스 오차가 최소가 되도록 공진모드간 결합 영향을 고려한 적합도 함수를 제안하고, 미립자 집단 최적화 (PSO:Particle Swarm Optimization) 알고리즘을 이용하여 등가회로의 미지상수를 추정하였다. 3개의 공진점을 갖는 샌드위치형 예제 트랜스듀서에 대하여 제안된 방법을 적용하여 등가회로를 모델링하고, 수중에서의 임피던스 측정치와 추정된 등가모델의 임피던스를 비교함으로써 제안된 기법의 타당성을 검증하였다.

• Journal of Information Processing Systems
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• 제15권1호
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• pp.116-126
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• 2019

In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the outset and effectively search locally later in a study, which improves the overall classification accuracy. The experimental results show that the improved particle swarm optimization method is more accurate than a grid search algorithm optimization and other improved particle swarm optimizations with regard to its classification of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.

• Journal of Electrical Engineering and Technology
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• 제5권2호
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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.