• Title/Summary/Keyword: particle swarm optimization

Search Result 631, Processing Time 0.186 seconds

Development of optimization algorithm to set transition point for multi-segmented rating curve (구간 분할된 레이팅 커브의 천이점 선정을 위한 최적화 알고리즘 개발)

  • Kim, Yeonsu;Noh, Joonwoo;Kim, Sunghoon;Yu, Wansik
    • Proceedings of the Korea Water Resources Association Conference
    • /
    • /
    • pp.421-421
    • /
    • 2018
  • 효율적인 수자원 관리를 위하여 전국유역조사, 수자원 장기종합계획 등 다양한 사업이 수행되고 있으며, 이를 위하여 유출해석은 필수적인 항목이라 할 수 있다. 유출해석을 위하여 수문모형 또는 관측소의 유량자료가 활용되고 있으나, 이는 기존에 관측된 유량자료를 바탕으로 구축된 수위-유량관계 곡선식(Rating-curve)을 활용하여 재생산된 자료라 할 수 있다. 즉, 수위자료는 매시간 관측소에서 측정이 되지만, 유량자료의 경우 측정이 어려울 뿐만 아니라 변동성 및 불확실성이 크기 때문에 시계열 수위를 곡신식을 통해 유량으로 변환하여 활용하고 있다. 이와 같이 수위-유량관계 곡선식의 정확성이 수문자료 생산에 핵심 요소임에도 불구하고 이에 대한 연구는 제한적이며, 특히 홍수터 등의 영향을 고려하여 분할된 곡선의 천이점 접합시 곡선식의 정확도 향상을 위한 연구도 드문 편이다. 따라서 본 연구에서는 구간 분할된 곡선의 최적 천이점 선정을 위하여 Particle Swarm Optimization(PSO)기법을 활용하였으며, 총 5개 구간까지 구간별 목적함수로 RMSE, RSR, 결정계수 적용시 특성변화에 대한 연구를 수행하였다. 구간에 대하여 절대적인 오차를 산정하는 RMSE를 활용하는 경우 저수위 부분에 대한 오차가 증가하는 것을 확인할 수 있었으며, 상대적인 오차인 RSR, 결정계수를 활용하는 경우 전체 구간에 대한 오차를 보완할 수 있는 것으로 나타났다. PSO기법을 활용하여 도출된 곡선식에 대해서는 구간 및 전체구간에 대한 오차(RMSE, 결정계수, RSR, MAPE)를 활용하여 불확실성을 검토할 수 있도록 하였고, 잔차분석을 통한 이상치 및 회귀곡선에 대한 정규성 검토를 수행할 수 있는 툴을 개발하였다. 레이팅 커브를 작성하는데 있어 최적화 알고리즘을 활용하여 구간분할시 천이점 선정의 자동화로 천이점 선정에 소요되는 시간을 대폭 감축할 수 있을 뿐만 아니라, 구간별 오차를 종합적으로 고려하여 우수한 품질의 레이팅 커브를 도출할 수 있는 기반을 구축하였다.

  • PDF

Study on the Optimal Selection of Rotor Track and Balance Parameters using Non-linear Response Models and Genetic Algorithm (로터 트랙 발란스(RTB) 파라미터 최적화를 위한 비선형 모델링 및 GA 기법 적용 연구)

  • Lee, Seong Han;Kim, Chang Joo;Jung, Sung Nam;Yu, Young Hyun;Kim, Oe Cheul
    • Journal of the Korean Society for Aeronautical & Space Sciences
    • /
    • v.44 no.11
    • /
    • pp.989-996
    • /
    • 2016
  • This paper intends to develop the rotor track and balance (RTB) algorithm using the nonlinear RTB models and a real-coded hybrid genetic algorithm. The RTB response data computed using the trim solutions with variation of the adjustment parameters have been used to build nonlinear RTB models based on the quadratic interpolation functions. Nonlinear programming problems to minimize the track deviations and the airframe vibration responses have been formulated to find optimum settings of balance weights, trim-tab deflections, and pitch-link lengths of each blade. The results are efficiently resolved using the real-coded genetic algorithm hybridized with the particle swarm optimization techniques for convergence acceleration. The nonlinear RTB models and the optimized RTB parameters have been compared with those computed using the linear models to validate the proposed techniques. The results showed that the nonlinear models lead to more accurate models and reduced RTB responses than the linear counterpart.

An Efficient Genetic Algorithm for the Allocation and Engagement Scheduling of Interceptor Missiles (효율적인 유전 알고리즘을 활용한 요격미사일 할당 및 교전 일정계획의 최적화)

  • Lee, Dae Ryeock;Yang, Jaehwan
    • Journal of the Society of Korea Industrial and Systems Engineering
    • /
    • v.39 no.2
    • /
    • pp.88-102
    • /
    • 2016
  • This paper considers the allocation and engagement scheduling problem of interceptor missiles, and the problem was formulated by using MIP (mixed integer programming) in the previous research. The objective of the model is the maximization of total intercept altitude instead of the more conventional objective such as the minimization of surviving target value. The concept of the time window was used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. The MIP formulation of the problem is very complex due to the complexity of the real problem itself. Hence, the finding of an efficient optimal solution procedure seems to be difficult. In this paper, an efficient genetic algorithm is developed by improving a general genetic algorithm. The improvement is achieved by carefully analyzing the structure of the formulation. Specifically, the new algorithm includes an enhanced repair process and a crossover operation which utilizes the idea of the PSO (particle swarm optimization). Then, the algorithm is throughly tested on 50 randomly generated engagement scenarios, and its performance is compared with that of a commercial package and a more general genetic algorithm, respectively. The results indicate that the new algorithm consistently performs better than a general genetic algorithm. Also, the new algorithm generates much better results than those by the commercial package on several test cases when the execution time of the commercial package is limited to 8,000 seconds, which is about two hours and 13 minutes. Moreover, it obtains a solution within 0.13~33.34 seconds depending on the size of scenarios.

The Design of Polynomial RBF Neural Network by Means of Fuzzy Inference System and Its Optimization (퍼지추론 기반 다항식 RBF 뉴럴 네트워크의 설계 및 최적화)

  • Baek, Jin-Yeol;Park, Byaung-Jun;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.58 no.2
    • /
    • pp.399-406
    • /
    • 2009
  • In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

The Optimal Operation of Distributed Generation Possessed by Community Energy System Considering Low-Carbon Paradigm (저탄소 패러다임에 따른 구역전기사업자의 분산전원 최적 운영에 관한 연구)

  • Kim, Sung-Yul;Shim, Hun;Bae, In-Su;Kim, Jin-O
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.58 no.8
    • /
    • pp.1504-1511
    • /
    • 2009
  • By development of renewable energies and high-efficient facilities and deregulated electricity market, the operation cost of distributed generation(DG) becomes more competitive. The amount of distributed resource is considerably increasing in the distribution network consequently. Also, international environmental regulations of the leaking carbon become effective to keep pace with the global efforts for low-carbon paradigm. It contributes to spread out the business of DG. Therefore, the operator of DG is able to supply electric power to customers who are connected directly to DG as well as loads that are connected to entire network. In this situation, community energy system(CES) having DGs is recently a new participant in the energy market. DG's purchase price from the market is different from the DG's sales price to the market due to the transmission service charges and etc. Therefore, CES who owns DGs has to control the produced electric power per hourly period in order to maximize the profit. If there is no regulation for carbon emission(CE), the generators which get higher production than generation cost will hold a prominent position in a competitive price. However, considering the international environment regulation, CE newly will be an important element to decide the marginal cost of generators as well as the classified fuel unit cost and unit's efficiency. This paper will introduce the optimal operation of CES's DG connected to the distribution network considering CE. The purpose of optimization is to maximize the profit of CES and Particle Swarm Optimization (PSO) will be used to solve this problem. The optimal operation of DG represented in this paper is to be resource to CES and system operator for determining the decision making criteria.

Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber

  • Armaghani, Danial Jahed;Mirzaei, Fatemeh;Shariati, Mahdi;Trung, Nguyen Thoi;Shariati, Morteza;Trnavac, Dragana
    • Geomechanics and Engineering
    • /
    • v.20 no.3
    • /
    • pp.191-205
    • /
    • 2020
  • Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.

Analysis for Applicability of Differential Evolution Algorithm to Geotechnical Engineering Field (지반공학 분야에 대한 차분진화 알고리즘 적용성 분석)

  • An, Joon-Sang;Kang, Kyung-Nam;Kim, San-Ha;Song, Ki-Il
    • Journal of the Korean Geotechnical Society
    • /
    • v.35 no.4
    • /
    • pp.27-35
    • /
    • 2019
  • This study confirmed the applicability to the field of geotechnical engineering for relatively complicated space and many target design variables in back analysis. The Sharan's equation and the Blum's method were used for the tunnel field and the retaining wall as a model for the multi-variate problem of geotechnical engineering. Optimization methods are generally divided into a deterministic method and a stochastic method. In this study, Simulated Annealing Method (SA) was selected as a deterministic method and Differential Evolution Algorithm (DEA) and Particle Swarm Optimization Method (PSO) were selected as stochastic methods. The three selected optimization methods were compared by applying a multi-variate model. The problem of deterministic method has been confirmed in the multi-variate back analysis of geotechnical engineering, and the superiority of DEA can be confirmed. DEA showed an average error rate of 3.12% for Sharan's solution and 2.23% for Blum's problem. The iteration number of DEA was confirmed to be smaller than the other two optimization methods. SA was confirmed to be 117.39~167.13 times higher than DEA and PSO was confirmed to be 2.43~6.91 times higher than DEA. Applying a DEA to the multi-variate back analysis of geotechnical problems can be expected to improve computational speed and accuracy.

Design of Pattern Classifier for Electrical and Electronic Waste Plastic Devices Using LIBS Spectrometer (LIBS 분광기를 이용한 폐소형가전 플라스틱 패턴 분류기의 설계)

  • Park, Sang-Beom;Bae, Jong-Soo;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.26 no.6
    • /
    • pp.477-484
    • /
    • 2016
  • Small industrial appliances such as fan, audio, electric rice cooker mostly consist of ABS, PP, PS materials. In colored plastics, it is possible to classify by near infrared(NIR) spectroscopy, while in black plastics, it is very difficult to classify black plastic because of the characteristic of black material that absorbs the light. So the RBFNNs pattern classifier is introduced for sorting electrical and electronic waste plastics through LIBS(Laser Induced Breakdown Spectroscopy) spectrometer. At the preprocessing part, PCA(Principle Component Analysis), as a kind of dimension reduction algorithms, is used to improve processing speed as well as to extract the effective data characteristics. In the condition part, FCM(Fuzzy C-Means) clustering is exploited. In the conclusion part, the coefficients of linear function of being polynomial type are used as connection weights. PSO and 5-fold cross validation are used to improve the reliability of performance as well as to enhance classification rate. The performance of the proposed classifier is described based on both optimization and no optimization.

Design of Microstrip Patch Antenna on UHF Band using Multiple Meander for Metal Attached (금속 부착용 멀티 미앤더형 UHF 대역 마이크로스트립 패치 안테나 설계)

  • Park, Chan-Hong;Choi, Yong-Seok;Koo, Dong-Jin;Jang, Sung-Won;Seong, Hyeon-Kyeong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • /
    • pp.307-311
    • /
    • 2012
  • In this paper, a novel particle swarm optimization method based on IE3D is used to design a mobile communication Microstrip Patch Antenna. The aim of the thesis is to Design and fabricate an inset fed rectangular Microstrip Antenna and study the effect of antenna dimensions Length (L), Width (W) and substrate parameters relative Dielectric constant (${\varepsilon}r$), substrate thickness on Radiation parameters of Band width. When the antenna was designed, a dual-band, dual-polarized antenna was used to secure the bandwidth and improve performance, and a coaxial probe feeding method so that the phased array of antenna is easy.

  • PDF

A Study on Design of Microstrip Patch Antenna for Mobile Communication Systems using IE3D (IE3D를 이용한 단일 급전 이동통신용 마이크로스트립 패치 안테나 설계에 관한 연구)

  • Park, Jong-Dae;Park, Byeong-Ho;Shim, Woo-Seop;Kim, Myeong-Dong;Seong, Hyeon-Kyeong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • /
    • pp.316-319
    • /
    • 2012
  • In this paper, a novel particle swarm optimization method based on IE3D is used to design a mobile communication Microstrip Patch Antenna. The aim of the thesis is to Design and fabricate an inset fed rectangular Microstrip Antenna and study the effect of antenna dimensions Length (L), Width (W) and substrate parameters relative Dielectric constant (${\varepsilon}r$), substrate thickness on Radiation parameters of Band width. When the antenna was designed, a dual-band, dual-polarized antenna was used to secure the bandwidth and improve performance, and a coaxial probe feeding method so that the phased array of antenna is easy.

  • PDF