• Title, Summary, Keyword: particle swarm optimization

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A Study on Evaluation Method of Mixed Nash Equilibria by Using the Cournot Model for N-Genco. in Wholesale Electricity Market (도매전력시장에서 N명 발전사업자의 꾸르노 모델을 이용한 혼합 내쉬 균형점 도출 방법론 개발 연구)

  • Lim, Jung-Youl;Lee, Ki-Song;Yang, Kwang-Min;Park, Jong-Bae;Shin, Joong-Rin
    • Proceedings of the KIEE Conference
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    • pp.639-642
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    • 2003
  • This paper presents a method for evaluating the mixed nash equilibria of the Cournot model for N-Gencos. in wholesale electricity market. In the wholesale electricity market, the strategies of N-Genco. can be applied to the game model under the conditions which the Gencos. determine their stratgies to maximize their benefit. Generally, the Lemke algorithm is evaluated the mixed nash equlibria in the two-player game model. However, the necessary condition for the mixed equlibria of N-player are modified as the necessary condition of N-1 player by analyzing the Lemke algorithms. Although reducing the necessary condition for N-player as the one of N-1 player, it is difficult to and the mixed nash equilibria participated two more players by using the mathmatical approaches since those have the nonlinear characteristics. To overcome the above problem, this paper presents the generalized necessary condition for N-player and proposed the object function to and the mixed nash equlibrium. Also, to evaluate the mixed equilibrium through the nonlinear objective function, the Particle Swarm Optimization (PSO) as one of the heuristic algorithm are proposed in this paper. To present the mixed equlibria for the strategy of N-Gencos. through the proposed necessry condition and the evaluation approach, this paper proposes the mixed equilibrium in the cournot game model for 3-players.

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Adaptive Control of Super Peer Ration using Particle Swarm Optimization in Self-Organizing Super Peer Ring Search Scheme (자기 조직적 우수 피어 링 검색기법에서 입자 군집 최적화(PSO)를 이용한 적응적 우수 피어 비율 조절 기법)

  • Jang, Hyung-Gun;Han, Sae-Young;Park, Sung-Yong
    • The KIPS Transactions:PartA
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    • v.13A no.6
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    • pp.501-510
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    • 2006
  • The self-organizing super peer ring P2P search scheme improves search performance of the existing unstructured peer-to-peer systems, in which super peers with high capacity constitute a ring structure and all peer in the system utilize the ring for publishing or querying their keys. In this paper, we further improves the performance of the self-organizing ring by adaptively changing its super peer ratio according to the status of the entire system. By using PSO, the optimized super peer ratio can be maintained within the system. Through simulations, we show that our self-organizing super peer ring optimized by PSO outperforms not only the fixed super peer ring but also the self-organizing super ring with fixed ratio in the aspect of query success rate.

Performance Improvement of Feature Selection Methods based on Bio-Inspired Algorithms (생태계 모방 알고리즘 기반 특징 선택 방법의 성능 개선 방안)

  • Yun, Chul-Min;Yang, Ji-Hoon
    • The KIPS Transactions:PartB
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    • v.15B no.4
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    • pp.331-340
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    • 2008
  • Feature Selection is one of methods to improve the classification accuracy of data in the field of machine learning. Many feature selection algorithms have been proposed and discussed for years. However, the problem of finding the optimal feature subset from full data still remains to be a difficult problem. Bio-inspired algorithms are well-known evolutionary algorithms based on the principles of behavior of organisms, and very useful methods to find the optimal solution in optimization problems. Bio-inspired algorithms are also used in the field of feature selection problems. So in this paper we proposed new improved bio-inspired algorithms for feature selection. We used well-known bio-inspired algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), to find the optimal subset of features that shows the best performance in classification accuracy. In addition, we modified the bio-inspired algorithms considering the prior importance (prior relevance) of each feature. We chose the mRMR method, which can measure the goodness of single feature, to set the prior importance of each feature. We modified the evolution operators of GA and PSO by using the prior importance of each feature. We verified the performance of the proposed methods by experiment with datasets. Feature selection methods using GA and PSO produced better performances in terms of the classification accuracy. The modified method with the prior importance demonstrated improved performances in terms of the evolution speed and the classification accuracy.

Water resources potential assessment of ungauged catchments in Lake Tana Basin, Ethiopia

  • Damtew, Getachew Tegegne;Kim, Young-Oh
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.217-217
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    • 2015
  • The objective of this study was mainly to evaluate the water resources potential of Lake Tana Basin (LTB) by using Soil and Water Assessment Tool (SWAT). From SWAT simulation of LTB, about 5236 km2 area of LTB is gauged watershed and the remaining 9878 km2 area is ungauged watershed. For calibration of model parameters, four gauged stations were considered namely: Gilgel Abay, Gummera, Rib, and Megech. The SWAT-CUP built-in techniques, particle swarm optimization (PSO) and generalized likelihood uncertainty estimation (GLUE) method was used for calibration of model parameters and PSO method were selected for the study based on its performance results in four gauging stations. However the level of sensitivity of flow parameters differ from catchment to catchment, the curve number (CN2) has been found the most sensitive parameters in all gauged catchments. To facilitate the transfer of data from gauged catchments to ungauged catchments, clustering of hydrologic response units (HRUs) were done based on physical similarity measured between gauged and ungauged catchment attributes. From SWAT land use/ soil use/slope reclassification of LTB, a total of 142 HRUs were identified and these HRUs are clustered in to 39 similar hydrologic groups. In order to transfer the optimized model parameters from gauged to ungauged catchments based on these clustered hydrologic groups, this study evaluates three parameter transfer schemes: parameters transfer based on homogeneous regions (PT-I), parameter transfer based on global averaging (PT-II), and parameter transfer by considering Gilgel Abay catchment as a representative catchment (PT-III) since its model performance values are better than the other three gauged catchments. The performance of these parameter transfer approach was evaluated based on values of Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2). The computed NSE values was found to be 0.71, 0.58, and 0.31 for PT-I, PT-II and PT-III respectively and the computed R2 values was found to be 0.93, 0.82, and 0.95 for PT-I, PT-II, and PT-III respectively. Based on the performance evaluation criteria, PT-I were selected for modelling ungauged catchments by transferring optimized model parameters from gauged catchment. From the model result, yearly average stream flow for all homogeneous regions was found 29.54 m3/s, 112.92 m3/s, and 130.10 m3/s for time period (1989 - 2005) for region-I, region-II, and region-III respectively.

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Computational estimation of the earthquake response for fibre reinforced concrete rectangular columns

  • Liu, Chanjuan;Wu, Xinling;Wakil, Karzan;Jermsittiparsert, Kittisak;Ho, Lanh Si;Alabduljabbar, Hisham;Alaskar, Abdulaziz;Alrshoudi, Fahed;Alyousef, Rayed;Mohamed, Abdeliazim Mustafa
    • Steel and Composite Structures
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    • v.34 no.5
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    • pp.743-767
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    • 2020
  • Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R2 = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R2 = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.

A Study on the PAPR Reduction Using Phase Rotation Method Applying Metaheuristic Algorithm (Metaheuristic 알고리즘을 적용한 위상회전 기법에 의한 PAPR 감소에 관한 연구)

  • Yoo, Sun-Yong;Park, Bee-Ho;Kim, Wan-Tae;Cho, Sung-Joon
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.46 no.5
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    • pp.26-35
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    • 2009
  • OFDM (Orthogonal Frequency Division Multiplexing) system is robust to frequency selective fading and narrowband interference in high-speed data communications. However, an OFDM signal consists of a number of independently modulated subcarriers and the superposition of these subcarriers causes a problem that can give a large PAPR(Peak-to-Average Power Ratio). Phase rotation method can reduce the PAPR without nonlinear distortion by multiplying phase weighting factors. But computational complexity of searching phase weighting factors is increased exponentially with the number of subblocks and considered phase factor. Therefore, a new method, which can reduce computational complexity and detect phase weighting factors efficiently, should be developed. In this paper, a modeling process is introduced, which apply metaheuristic algerian in phase rotation method and optimize in PTS (Particle Swarm Optimization) scheme. Proposed algorithm can solve the computational complexity and guarantee to reduce PAPR We analyzed the efficiency of the PAPR reduction through a simulation when we applied the proposed method to telecommunication systems.

Steel Plate Faults Diagnosis with S-MTS (S-MTS를 이용한 강판의 표면 결함 진단)

  • Kim, Joon-Young;Cha, Jae-Min;Shin, Junguk;Yeom, Choongsub
    • Journal of Intelligence and Information Systems
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    • v.23 no.1
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    • pp.47-67
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    • 2017
  • Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.