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Sample records for optimization based day-ahead

  1. Hybrid Particle Swarm Optimization based Day-Ahead Self-Scheduling for Thermal Generator in Competitive Electricity Market

    DEFF Research Database (Denmark)

    Pindoriya, Naran M.; Singh, S.N.; Østergaard, Jacob

    2009-01-01

    in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting bi-objective optimization problem which has both binary and continuous optimization variables considered as constrained mixed......This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead self-scheduling for thermal power producer in competitive electricity market. The objective functions considered to model the self-scheduling problem are 1) to maximize the profit from selling energy...... integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a day-ahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the day...

  2. Hybrid Particle Swarm Optimization based Day-Ahead Self-Scheduling for Thermal Generator in Competitive Electricity Market

    DEFF Research Database (Denmark)

    Pindoriya, Naran M.; Singh, S.N.; Østergaard, Jacob

    2009-01-01

    This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead self-scheduling for thermal power producer in competitive electricity market. The objective functions considered to model the self-scheduling problem are 1) to maximize the profit from selling energy...

  3. A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration

    DEFF Research Database (Denmark)

    Jiang, Yuewen; Chen, Meisen; You, Shi

    2017-01-01

    In a conventional electricity market, trading is conducted based on power forecasts in the day-ahead market, while the power imbalance is regulated in the real-time market, which is a separate trading scheme. With large-scale wind power connected into the power grid, power forecast errors increase...... in the day-ahead market which lowers the economic efficiency of the separate trading scheme. This paper proposes a robust unified trading model that includes the forecasts of real-time prices and imbalance power into the day-ahead trading scheme. The model is developed based on robust optimization in view...... swarm algorithm (QPSO). Finally, the impacts of associated parameters on the separate trading and unified trading model are analyzed to verify the superiority of the proposed model and algorithm....

  4. A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration

    Directory of Open Access Journals (Sweden)

    Yuewen Jiang

    2017-04-01

    Full Text Available In a conventional electricity market, trading is conducted based on power forecasts in the day-ahead market, while the power imbalance is regulated in the real-time market, which is a separate trading scheme. With large-scale wind power connected into the power grid, power forecast errors increase in the day-ahead market which lowers the economic efficiency of the separate trading scheme. This paper proposes a robust unified trading model that includes the forecasts of real-time prices and imbalance power into the day-ahead trading scheme. The model is developed based on robust optimization in view of the undefined probability distribution of clearing prices of the real-time market. For the model to be used efficiently, an improved quantum-behaved particle swarm algorithm (IQPSO is presented in the paper based on an in-depth analysis of the limitations of the static character of quantum-behaved particle swarm algorithm (QPSO. Finally, the impacts of associated parameters on the separate trading and unified trading model are analyzed to verify the superiority of the proposed model and algorithm.

  5. A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration

    National Research Council Canada - National Science Library

    Yuewen Jiang; Meisen Chen; Shi You

    2017-01-01

    In a conventional electricity market, trading is conducted based on power forecasts in the day-ahead market, while the power imbalance is regulated in the real-time market, which is a separate trading scheme...

  6. A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection

    Directory of Open Access Journals (Sweden)

    Ping Jiang

    2016-08-01

    Full Text Available The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecasting plays significant role for power producers and consumers. Although many studies developing and proposing highly accurate forecasting models exist in the literature, there have been few investigations on improving the forecasting effectiveness of electricity price from the perspective of reducing the volatility of data with satisfactory accuracy. Based on reducing the volatility of the electricity price and the forecasting nature of the radial basis function network (RBFN, this paper successfully develops a two-stage model to forecast the day-ahead electricity price, of which the first stage is particle swarm optimization (PSO-core mapping (CM with self-organizing-map and fuzzy set (PCMwSF, and the second stage is selection rule (SR. The PCMwSF stage applies CM, fuzzy set and optimized weights to obtain the future price, and the SR stage is inspired by the forecasting nature of RBFN and effectively selects the best forecast during the test period. The proposed model, i.e., CM-PCMwSF-SR, not only overcomes the difficulty of reducing the high volatility of the electricity price but also leads to a superior forecasting effectiveness than benchmarks.

  7. A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price

    Directory of Open Access Journals (Sweden)

    Zhilong Wang

    2014-01-01

    Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.

  8. Aggregators’ Optimal Bidding Strategy in Sequential Day-Ahead and Intraday Electricity Spot Markets

    Directory of Open Access Journals (Sweden)

    Xiaolin Ayón

    2017-04-01

    Full Text Available This paper proposes a probabilistic optimization method that produces optimal bidding curves to be submitted by an aggregator to the day-ahead electricity market and the intraday market, considering the flexible demand of his customers (based in time dependent resources such as batteries and shiftable demand and taking into account the possible imbalance costs as well as the uncertainty of forecasts (market prices, demand, and renewable energy sources (RES generation. The optimization strategy aims to minimize the total cost of the traded energy over a whole day, taking into account the intertemporal constraints. The proposed formulation leads to the solution of different linear optimization problems, following the natural temporal sequence of electricity spot markets. Intertemporal constraints regarding time dependent resources are fulfilled through a scheduling process performed after the day-ahead market clearing. Each of the different problems is of moderate dimension and requires short computation times. The benefits of the proposed strategy are assessed comparing the payments done by an aggregator over a sample period of one year following different deterministic and probabilistic strategies. Results show that probabilistic strategy reports better benefits for aggregators participating in power markets.

  9. Coordinated optimization of weekly reserve, day-ahead and balancing energy trade in hydropower

    Science.gov (United States)

    Fodstad, Marte; Schou Grytli, Eirik; Korpås, Magnus

    2017-04-01

    We present a model for optimal trade in a weekly power reserve market under day-ahead and balancing market price uncertainty. The model takes the perspective of a price-taking hydropower producer and a case study for the Norwegian market design and a Norwegian multi-reservoir water course for a winter week is presented. We demonstrate how a bid curve for the reserve market can be established through a sensitivity analysis on reserve prices, and observe that the optimal trade in the succeeding day-ahead and balancing market is highly sensitive to the reserve obligation.

  10. Co-optimization of Energy and Demand-Side Reserves in Day-Ahead Electricity Markets

    Science.gov (United States)

    Surender Reddy, S.; Abhyankar, A. R.; Bijwe, P. R.

    2015-04-01

    This paper presents a new multi-objective day-ahead market clearing (DAMC) mechanism with demand-side reserves/demand response (DR) offers, considering realistic voltage-dependent load modeling. The paper proposes objectives such as social welfare maximization (SWM) including demand-side reserves, and load served error (LSE) minimization. In this paper, energy and demand-side reserves are cleared simultaneously through co-optimization process. The paper clearly brings out the unsuitability of conventional SWM for DAMC in the presence of voltage-dependent loads, due to reduction of load served (LS). Under such circumstances multi-objective DAMC with DR offers is essential. Multi-objective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+) has been used to solve the optimization problem. The effectiveness of the proposed scheme is confirmed with results obtained from IEEE 30 bus system.

  11. Chance-Constrained Programming Based Day-Ahead Optimal Scheduling of Energy Storage%基于机会约束规划的储能日前优化调度

    Institute of Scientific and Technical Information of China (English)

    赵书强; 刘晨亮; 王明雨; 胡永强

    2013-01-01

    To make the output of the hybrid wind/photovoltaic/energy storage system farthest matched with the scheduled output, the method of one-day ahead optimized control of energy storage equipments is adopted. In view of the randomness of PV generation and wind power generation, a chance-constrained programming based optimal control method of energy storage, in which the constraint of power output of energy storage equipments and the constraint of electric quantity are taken into account and the maximum similarity between the total output curve of hybrid wind/photovoltaic/energy storage system and the given scheduled output curve is taken as the objective function, is proposed and solved by stochastic simulation based particle swarm optimization, thus the charging/discharging power of energy storage equipments corresponding to condition that the two curves are most oncoming is obtained. Analysis on results of calculation example shows that the proposed optimized control strategy of energy storage equipments can make the total output of hybrid wind/photovoltaic/energy storage system farthest tracing the scheduled output curve.%为了最大程度地使风光储联合发电系统的出力与计划出力相匹配,采用提前一天对储能装置进行优化控制的方法。因风光出力具有随机性,提出了基于机会约束规划的储能优化控制方法。该方法考虑了储能装置的功率出力和电量约束条件,以风光储总出力曲线与给定的计划出力曲线的相似度最大为目标函数,使用基于随机模拟的粒子群算法求解,得到两条曲线最接近时对应的储能充放电功率。算例分析结果表明,所提出的储能优化控制策略能够使风光储联合发电系统总出力最大程度地跟踪计划出力曲线。

  12. Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market

    DEFF Research Database (Denmark)

    Fotouhi Ghazvini, Mohammad Ali; Faria, Pedro; Ramos, Sergio

    2015-01-01

    spikes, volatile loads and the potential for market power exertion by GENCO (generation companies). A REP can manage the market risks by employing the DR (demand response) programs and using its' generation and storage assets at the distribution network to serve the customers. The proposed model suggests...... how a REP with light physical assets, such as DG (distributed generation) units and ESS (energy storage systems), can survive in a competitive retail market. The paper discusses the effective risk management strategies for the REPs to deal with the uncertainties of the DAM (day-ahead market) and how...... to hedge the financial losses in the market. A two-stage stochastic programming problem is formulated. It aims to establish the financial incentive-based DR programs and the optimal dispatch of the DG units and ESSs. The uncertainty of the forecasted day-ahead load demand and electricity price is also...

  13. Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market

    Directory of Open Access Journals (Sweden)

    Xiaolin Liu

    2012-11-01

    Full Text Available Wind Power Producers (WPPs seek to maximize profit and minimize the imbalance costs when bidding into the day-ahead market, but uncertainties in the hourly available wind and forecasting errors make the bidding risky. This paper assumes that hourly wind power output given by the forecast follows a normal distribution, and proposes three different bidding strategies, i.e., the expected profit-maximization strategy (EPS, the chance-constrained programming-based strategy (CPS and the multi-objective bidding strategy (ECPS. Analytical solutions under the three strategies are obtained. Comparisons among the three strategies are conducted on a hypothetical wind farm which follows the Spanish market rules. Results show that bid under the EPS is highly dependent on market clearing price, imbalance prices, and also the mean value and standard deviation of wind forecast, and that bid under the CPS is largely driven by risk parameters and the mean value and standard deviation of the wind forecast. The ECPS combining both EPS and CPS tends to choose a compromise bid. Furthermore, the ECPS can effectively control the tradeoff between expected profit and target profit for WPPs operating in volatile electricity markets.

  14. Multi-objective parallel particle swarm optimization for day-ahead Vehicle-to-Grid scheduling

    DEFF Research Database (Denmark)

    Soares, Joao; Vale, Zita; Canizes, Bruno

    2013-01-01

    This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle-To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aimi...... calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method....

  15. Optimal bidding in Turkey day ahead electricity market for wind energy and pumped storage hydro power plant

    Directory of Open Access Journals (Sweden)

    Ceyhun Yıldız

    2016-10-01

    Full Text Available In electrical grid; when the demand power increases energy prices increase, when the demand decreases energy prices decrease. For this reason; to increase the total daily income, it is required to shift generations to the hours that high demand power values occurred. Wind Power Plants (WPP have unstable and uncontrollable generation characteristic. For this reason, energy storage systems are needed to shift the generations of WPPs in time scale. In this study, four wind power plants (WPP which are tied to the Turkish interconnected grid and a pumped hydro storage power plant (PSPP that meets the energy storage requirement of these power plants are investigated in Turkey day ahead energy market. An optimization algorithm is developed using linear programming technique to maximize the day ahead market bids of these plants which are going to generate power together. When incomes and generations of the plants that are operated with optimization strategy is analyzed, it is seen that annual income increased by 2.737% compared with WPPs ‘s alone operation and generations are substantially shifted to the high demand power occurred hours.

  16. Optimal Bidding Strategies for Generation Companies in a Day-Ahead Electricity Market with Risk Management Taken into Account

    Directory of Open Access Journals (Sweden)

    Azmi Saleh

    2009-01-01

    Full Text Available Problem statement: In a competitive electricity market with limited number of producers, Generation Companies (Gencos is facing an oligopoly market rather than a perfect competition. Under oligopoly market environment, each Genco may increase its own profit through a favorable bidding strategy. The objective of a Genco is to maximize its profit and minimize the associated risk. In order to achieve this goal, it is necessary and important for the Genco to make optimal bidding strategies with risk management before bidding into spot market to get an expected high profit, since spot prices are substantially volatile. This study propose a method to build optimal bidding strategies in a day-ahead electricity market with incomplete information and considering both risk management and unit commitment. Approach: The proposed methodology employs the Monte Carlo simulation for modeling a risk management and a strategic behavior of rival. A probability density function (pdf, Value at Risk (VaR and Monte Carlo simulation used to build optimal bidding strategies for a Genco. Results: The result of the proposed method shows that a Genco can build optimal bidding strategies to maximize expected total profit considering unit commitment and risk management. The Genco controls the risk by setting the confidence level. If the Genco increase the confidence level, the expected total VaR of profit decrease. Conclusions/Recommendations: The proposed method for building optimal bidding strategies in a day-ahead electricity market to maximize expected total profit considering unit commitment and risk management is helpful for a Genco to make a decision to submit bidding to the Independent System Operator (ISO.

  17. A probabilistic risk-based approach for spinning reserve provision using day-ahead demand response program

    Energy Technology Data Exchange (ETDEWEB)

    Shayesteh, E. [Islamic Azad University, Garmsar Branch, Garmsar (Iran); Yousefi, A.; Parsa Moghaddam, M. [Department of Electrical Engineering, Tarbiat Modares University (TMU), Tehran (Iran)

    2010-05-15

    Spinning Reserve is one of the ancillary services which is essential to satisfy system security constraints when the power system faces with a contingency. In this paper, Day Ahead Demand Response Program as one of the incentive-based Demand Response programs is implemented as a source of spinning reserve. In this regard, certain number of demands are selected according to a sensitivity analysis, and simulated as virtual generation units. The reserve market is cleared for Spinning Reserve allocation considering a probabilistic technique. A comparison is performed between the absence and existence of Day Ahead Demand Response Program from both economical and reliability viewpoints. Numerical studies based on IEEE 57 bus test system is conducted for evaluation of the proposed method. (author)

  18. Day ahead forecast of wind power through optimal application of multivariate analyzing methods

    Energy Technology Data Exchange (ETDEWEB)

    Arnoldt, Alexander; Bretschneider, Peter [Fraunhofer Institute for Optronics, System Technology, and Image Exploitation - Application Centre System Technology (IOSB-AST), Ilmenau (Germany). Energy Systems Group

    2011-07-01

    This paper presents two algorithms in identifying input models for artificial neural networks. The algorithms are based on an entropy analysis and an eigenvalue analysis of the correlation matrix. The resulting input models are used for investigating a feed forward and a recurrent artificial neural network structure to simulate a 24 hour forecast of wind power production. The limitation of the forecast error distribution is investigated through successful implementation of hybridization of single forecast models. Errors of the best forecast model stay between a normalized root mean square error from 3.5% to 6.1%. (orig.)

  19. Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation

    Directory of Open Access Journals (Sweden)

    Rongxiang Yuan

    2016-04-01

    Full Text Available In the traditional paradigm, large power plants provide active and reactive power required for the transmission system and the distribution network purchases grid power from it. However, with more and more distributed energy resources (DERs connected at distribution levels, it is necessary to schedule DERs to meet their demand and participate in the electricity markets at the distribution level in the near future. This paper proposes a comprehensive operational scheduling model to be used in the distribution management system (DMS. The model aims to determine optimal decisions on active elements of the network, distributed generations (DGs, and responsive loads (RLs, seeking to minimize the day-ahead composite economic cost of the distribution network. For more detailed simulation, the composite cost includes the aspects of the operation cost, emission cost, and transmission loss cost of the network. Additionally, the DMS effectively utilizes the reactive power support capabilities of wind and solar power integrated in the distribution, which is usually neglected in previous works. The optimization procedure is formulated as a nonlinear combinatorial problem and solved with a modified differential evolution algorithm. A modified 33-bus distribution network is employed to validate the satisfactory performance of the proposed methodology.

  20. Two-stage stochastic day-ahead optimal resource scheduling in a distribution network with intensive use of distributed energy resources

    DEFF Research Database (Denmark)

    Sousa, Tiago; Ghazvini, Mohammad Ali Fotouhi; Morais, Hugo

    2015-01-01

    The integration of renewable sources and electric vehicles will introduce new uncertainties to the optimal resource scheduling, namely at the distribution level. These uncertainties are mainly originated by the power generated by renewables sources and by the electric vehicles charge requirements....... This paper proposes a two-state stochastic programming approach to solve the day-ahead optimal resource scheduling problem. The case study considers a 33-bus distribution network with 66 distributed generation units and 1000 electric vehicles....

  1. Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

    Science.gov (United States)

    Zhu, Qing; Zou, Yingchao; Lai, Kin Keung

    2014-01-01

    As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations. PMID:25061614

  2. Day-ahead crude oil price forecasting using a novel morphological component analysis based model.

    Science.gov (United States)

    Zhu, Qing; He, Kaijian; Zou, Yingchao; Lai, Kin Keung

    2014-01-01

    As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.

  3. Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

    DEFF Research Database (Denmark)

    Soares, João; Valle, Zita; Morais, Hugo

    2013-01-01

    This paper presents a decision support Tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy ressource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application...... of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network...... constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance...

  4. A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid

    Directory of Open Access Journals (Sweden)

    Ashfaq Ahmad

    2015-12-01

    Full Text Available In the operation of a smart grid (SG, day-ahead load forecasting (DLF is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately predict the load of the next day with a fair enough execution time. Our proposed model consists of three modules; the data preparation module, feature selection and the forecast module. The first module makes the historical load curve compatible with the feature selection module. The second module removes redundant and irrelevant features from the input data. The third module, which consists of an artificial neural network (ANN, predicts future load on the basis of selected features. Moreover, the forecast module uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. Simulations are conducted in MATLAB to validate the performance of our newly-proposed DLF model in terms of accuracy and execution time. Results show that our proposed modified feature selection and modified ANN (m(FS + ANN-based model for SGs is able to capture the non-linearity(ies in the history load curve with 97 . 11 % accuracy. Moreover, this accuracy is achieved at the cost of a fair enough execution time, i.e., we have decreased the average execution time of the existing FS + ANN-based model by 38 . 50 % .

  5. Deriving Optimal End of Day Storage for Pumped-Storage Power Plants in the Joint Energy and Reserve Day-Ahead Scheduling

    Directory of Open Access Journals (Sweden)

    Manuel Chazarra

    2017-06-01

    Full Text Available This paper presents a new methodology to maximise the income and derive the optimal end of day storage of closed-loop and daily-cycle pumped-storage hydropower plants. The plants participate in the day-ahead energy market as a price-taker and in the secondary regulation reserve market as a price-maker, in the context of the Iberian electricity system. The real-time use of the committed reserves is considered in the model formulation. The operation of the plants with the proposed methodology is compared to the ones that use an end of day storage of an empty reservoir or half of the storage capacity. Results show that the proposed methodology increases the maximum theoretical income in all the plants analysed both if they only participate in the day-ahead energy market and if they also participate in the secondary regulation service. It is also shown that the increase in the maximum theoretical income strongly depends on the size of the plant. In addition, it is proven that the end of day storages change notably in the new reserve-driven strategies of pumped-storage hydropower plants and that the proposed methodology is even more recommended if the secondary regulation service is considered.

  6. Optimal Bidding Strategies for a Small Generation Company in a Day-Ahead Electricity Market with Bilateral Contracts Taken into Account

    Directory of Open Access Journals (Sweden)

    Azmi Saleh

    2009-01-01

    Full Text Available Problem Statement: Deregulation power systems have been force to change their structures, from vertically integrated to open market systems. Each generation company (Genco is required to compete with rivals through bidding in a pool market and making a bilateral contract with a distribution company (Disco or consumers to maximize its own profits. A unit commitment becomes responsible for each Genco and difficult for Genco that have one generation plant or small generation capacity. The objective of a Genco is to maximize its profits with makes a decision submit bidding price function to the Independent System Operator (ISO. In order to achieve this goal, it is necessary and important for a small Genco to build optimal bidding strategies considering a bilateral contract and a unit commitment with constraints in time periods for possibilities to get a discontinuous dispatch that could reduce total profits. Approach: The proposed methodology employs an optimization method like Lagrange Relaxation to solve the optimal bidding problem. The solution procedure is applied in the study case and change the market condition to show the effect of bilateral contract to marginal clearing price (MCP, generation output and total profit for a small Genco. Result: The result of the proposed method shows that a Genco can build optimal bidding strategies to maximize total profit considering unit commitment and bilateral contract. Simulation results of a numerical example have demonstrated the bilateral contract reduced the hourly MCP. The bilateral contract will guarantee the Genco getting continuous dispatch during time periods. Conclusions/Recommendations: The proposed method for building optimal bidding strategies in a day-ahead electricity market to maximize total profit considering unit commitment and bilateral contract is helpful for a Genco to make decision in submit bidding to an ISO.

  7. A new approach for GenCos profit based unit commitment in day-ahead competitive electricity markets considering reserve uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Yamin, H.Y. [Department of Power Engineering, Hijjawi Faculty, Yarmouk University, Irbid 21163 (Jordan); El-Dwairi, Q. [Department of Anatomy, Jordan University of Science and Technology, Irbid (Jordan); Shahidehpour, S.M. [Department of Electrical and Computer Engineering, Illinois Institute of Technology (United States)

    2007-10-15

    This paper presents a new approach for GenCos Profit Based Unit Commitment (GPBUC) in day-ahead competitive electricity markets. Generation, spinning and non-spinning reserves are considered in the proposed formulation. The estimated probability that spinning and non-spinning reserves are called and generated is also considered in the formulation to simulate the reserve uncertainty. The artificial neural network (ANN) is applied for forecasting the reserve probability considering line limits, line and generator outages, market prices, bidding strategy, load and reserves patterns. Fuel and emission constraints are included in the model. A hybrid method between Lagrangian relaxation (LR) and evolutionary programming (EP) is applied to solve the proposed GPBUC problem. The proposed approach is applied to a 36 unit test system and the results are compared with those obtained from other approaches. (author)

  8. Study of the Effect of Time-Based Rate Demand Response Programs on Stochastic Day-Ahead Energy and Reserve Scheduling in Islanded Residential Microgrids

    DEFF Research Database (Denmark)

    Vahedipour-Dahraie, Mostafa; Najafi, Hamid Reza; Anvari-Moghaddam, Amjad

    2017-01-01

    In recent deregulated power systems, demand response (DR) has become one of the most cost-effective and efficient solutions for smoothing the load profile when the system is under stress. By participating in DR programs, customers are able to change their energy consumption habits in response...... to energy price changes and get incentives in return. In this paper, we study the effect of various time-based rate (TBR) programs on the stochastic day-ahead energy and reserve scheduling in residential islanded microgrids (MGs). An effective approach is presented to schedule both energy and reserve...... in presence of renewable energy resources (RESs) and electric vehicles (EVs). An economic model of responsive load is also proposed on the basis of elasticity factor to model the behavior of customers participating in various DR programs. A two-stage stochastic programming model is developed accordingly...

  9. A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

    Science.gov (United States)

    Zack, J. W.

    2015-12-01

    Predictions from Numerical Weather Prediction (NWP) models are the foundation for wind power forecasts for day-ahead and longer forecast horizons. The NWP models directly produce three-dimensional wind forecasts on their respective computational grids. These can be interpolated to the location and time of interest. However, these direct predictions typically contain significant systematic errors ("biases"). This is due to a variety of factors including the limited space-time resolution of the NWP models and shortcomings in the model's representation of physical processes. It has become common practice to attempt to improve the raw NWP forecasts by statistically adjusting them through a procedure that is widely known as Model Output Statistics (MOS). The challenge is to identify complex patterns of systematic errors and then use this knowledge to adjust the NWP predictions. The MOS-based improvements are the basis for much of the value added by commercial wind power forecast providers. There are an enormous number of statistical approaches that can be used to generate the MOS adjustments to the raw NWP forecasts. In order to obtain insight into the potential value of some of the newer and more sophisticated statistical techniques often referred to as "machine learning methods" a MOS-method comparison experiment has been performed for wind power generation facilities in 6 wind resource areas of California. The underlying NWP models that provided the raw forecasts were the two primary operational models of the US National Weather Service: the GFS and NAM models. The focus was on 1- and 2-day ahead forecasts of the hourly wind-based generation. The statistical methods evaluated included: (1) screening multiple linear regression, which served as a baseline method, (2) artificial neural networks, (3) a decision-tree approach called random forests, (4) gradient boosted regression based upon an decision-tree algorithm, (5) support vector regression and (6) analog ensemble

  10. Day-Ahead Optimized Economic Dispatching for Combined Cooling, Heating and Power in Micro Energy-Grid Based on Hessian Interior Point Method%基于Hessian内点法的微型能源网日前冷热电联供经济优化调度

    Institute of Scientific and Technical Information of China (English)

    徐青山; 曾艾东; 王凯; 蒋菱

    2016-01-01

    微型能源网包含冷、热、电和气4种能源形式,具有负荷种类多样、供能设备丰富的特点。在对微型能源网内多种供能及蓄能设备进行建模的基础上,提出基于集中互连能源交换网络的冷热电联供微型能源网的供能架构,架构内冷热电负荷被细分为纯电负荷、热水负荷、空间热负荷、冷冻制冷负荷和空间冷负荷,围绕该架构建立冷热电联供微型能源网经济优化调度模型,采用基于Hessian矩阵迭代的内点法对模型进行了求解。算例表明,通过调度微型能源网内各供能设备的运行方式和出力,可以显著降低系统的日运行费用,实现冷热电联供微型能源网的经济优化运行,得出的合理调度方案证实了所提模型和求解方法的正确性及有效性。%Micro energy-grid contains four forms of energy: cold, heat,electricity and gas, possessing features of diversified load forms and energy supply devices. After modeling of a variety of energy supply and storage devices, this paper proposed an energy supply infrastructure for combined cooling heating and power (CCHP)-based micro energy-grid based on centralized and interconnected energy exchange network. Loads are subdivided into pure electricity, hot water, space heating, refrigeration and space cooling. Optimized economic dispatching model of CCHP-based micro energy-grid is established according to the infrastructure. The model is solved with interior point method with Hessian matrix iteration. Study case shows that the dispatching method can achieve optimized economic operation of micro energy-grid by reducing operation cost remarkably through controlling operation mode and output of energy supply and storage equipment. Rational dispatching method confirms correctness and validity of the proposed model and solving method.

  11. One-day-ahead streamflow forecasting via super-ensembles of several neural network architectures based on the Multi-Level Diversity Model

    Science.gov (United States)

    Brochero, Darwin; Hajji, Islem; Pina, Jasson; Plana, Queralt; Sylvain, Jean-Daniel; Vergeynst, Jenna; Anctil, Francois

    2015-04-01

    Theories about generalization error with ensembles are mainly based on the diversity concept, which promotes resorting to many members of different properties to support mutually agreeable decisions. Kuncheva (2004) proposed the Multi Level Diversity Model (MLDM) to promote diversity in model ensembles, combining different data subsets, input subsets, models, parameters, and including a combiner level in order to optimize the final ensemble. This work tests the hypothesis about the minimisation of the generalization error with ensembles of Neural Network (NN) structures. We used the MLDM to evaluate two different scenarios: (i) ensembles from a same NN architecture, and (ii) a super-ensemble built by a combination of sub-ensembles of many NN architectures. The time series used correspond to the 12 basins of the MOdel Parameter Estimation eXperiment (MOPEX) project that were used by Duan et al. (2006) and Vos (2013) as benchmark. Six architectures are evaluated: FeedForward NN (FFNN) trained with the Levenberg Marquardt algorithm (Hagan et al., 1996), FFNN trained with SCE (Duan et al., 1993), Recurrent NN trained with a complex method (Weins et al., 2008), Dynamic NARX NN (Leontaritis and Billings, 1985), Echo State Network (ESN), and leak integrator neuron (L-ESN) (Lukosevicius and Jaeger, 2009). Each architecture performs separately an Input Variable Selection (IVS) according to a forward stepwise selection (Anctil et al., 2009) using mean square error as objective function. Post-processing by Predictor Stepwise Selection (PSS) of the super-ensemble has been done following the method proposed by Brochero et al. (2011). IVS results showed that the lagged stream flow, lagged precipitation, and Standardized Precipitation Index (SPI) (McKee et al., 1993) were the most relevant variables. They were respectively selected as one of the firsts three selected variables in 66, 45, and 28 of the 72 scenarios. A relationship between aridity index (Arora, 2002) and NN

  12. Hourly Electricity Prices in Day-Ahead Markets

    NARCIS (Netherlands)

    R. Huisman (Ronald); C. Huurman; R.J. Mahieu (Ronald)

    2007-01-01

    textabstractThis paper focuses on the characteristics of hourly electricity prices in day-ahead markets. In these markets, quotes for day-ahead delivery of electricity are submitted simultaneously for all hours in the next day. The same information set is used for quoting all hours of the day. The d

  13. Support vector machine for day ahead electricity price forecasting

    Science.gov (United States)

    Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti

    2015-05-01

    Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.

  14. What day-ahead reserves are needed in electric grids with high levels of wind power?

    Science.gov (United States)

    Mauch, Brandon; Apt, Jay; Carvalho, Pedro M. S.; Jaramillo, Paulina

    2013-09-01

    Day-ahead load and wind power forecasts provide useful information for operational decision making, but they are imperfect and forecast errors must be offset with operational reserves and balancing of (real time) energy. Procurement of these reserves is of great operational and financial importance in integrating large-scale wind power. We present a probabilistic method to determine net load forecast uncertainty for day-ahead wind and load forecasts. Our analysis uses data from two different electric grids in the US with similar levels of installed wind capacity but with large differences in wind and load forecast accuracy, due to geographic characteristics. We demonstrate that the day-ahead capacity requirements can be computed based on forecasts of wind and load. For 95% day-ahead reliability, this required capacity ranges from 2100 to 5700 MW for ERCOT, and 1900 to 4500 MW for MISO (with 10 GW of installed wind capacity), depending on the wind and load forecast values. We also show that for each MW of additional wind power capacity for ERCOT, 0.16-0.30 MW of dispatchable capacity will be used to compensate for wind uncertainty based on day-ahead forecasts. For MISO (with its more accurate forecasts), the requirement is 0.07-0.13 MW of dispatchable capacity for each MW of additional wind capacity.

  15. Price forecasting of day-ahead electricity markets using a hybrid forecast method

    Energy Technology Data Exchange (ETDEWEB)

    Shafie-khah, M., E-mail: miadreza@gmail.co [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Moghaddam, M. Parsa, E-mail: parsa@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Sheikh-El-Eslami, M.K., E-mail: aleslam@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of)

    2011-05-15

    Research highlights: {yields} A hybrid method is proposed to forecast the day-ahead prices in electricity market. {yields} The method combines Wavelet-ARIMA and RBFN network models. {yields} PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. {yields} One of the merits of the proposed method is lower need to the input data. {yields} The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

  16. Linear Clearing Prices in Non-Convex European Day-Ahead Electricity Markets

    CERN Document Server

    Martin, Alexander; Pokutta, Sebastian

    2012-01-01

    The European power grid can be divided into several market areas where the price of electricity is determined in a day-ahead auction. Market participants can provide continuous hourly bid curves and combinatorial bids with associated quantities given the prices. The goal of our auction is to maximize the economic surplus of all participants subject to transmission constraints and the existence of linear prices. In general strict linear prices do not exist in non-convex markets. Therefore we enforce the existence of linear prices where no one incurs a loss and only combinatorial bids might see a not realized gain. The resulting optimization problem is an MPEC that can not be solved efficiently by a standard solver. We present an exact algorithm and a fast heuristic for this type of problem. Both algorithms decompose the MPEC into a master MIP and price subproblems (LPs). The modeling technique and the algorithms are applicable to all MIP based combinatorial auctions.

  17. Day-Ahead Self-Scheduling of Thermal Generator in Competitive Electricity Market Using Hybrid PSO

    DEFF Research Database (Denmark)

    Pindoriya, N.M.; Singh, Sri Niwas; Østergaard, Jacob

    2009-01-01

    in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting biobjective optimization problem which has both binary and continuous optimization variables considered as constrained mixed......This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead selfscheduling for thermal power producer in competitive electricity market. The objective functions considered to model the selfscheduling problem are: 1) to maximize the profit from selling energy...... integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a dayahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the dayahead...

  18. A dynamic day-ahead paratransit planning problem

    NARCIS (Netherlands)

    Cremers, M.L.A.G.; Klein Haneveld, W.K.; van der Vlerk, M.H.

    We consider a dynamic planning problem for the transport of elderly and disabled people. The focus is on a decision to make one day ahead: which requests to serve with own vehicles and which ones to assign to subcontractors under uncertainty of late requests that are gradually revealed during the

  19. A dynamic day-ahead paratransit planning problem

    NARCIS (Netherlands)

    Cremers, Marloes; Klein Haneveld, Wim; van der Vlerk, Maarten

    2007-01-01

    We consider a dynamic planning problem for the transport of elderlyand disabled people. The focus is on a decision to make one day ahead:which requests to serve with own vehicles, and which ones to assign tosubcontractors, under uncertainty of late requests which are gradually revealedduring the day

  20. A dynamic day-ahead paratransit planning problem

    NARCIS (Netherlands)

    Cremers, M.L.A.G.; Klein Haneveld, W.K.; van der Vlerk, M.H.

    2010-01-01

    We consider a dynamic planning problem for the transport of elderly and disabled people. The focus is on a decision to make one day ahead: which requests to serve with own vehicles and which ones to assign to subcontractors under uncertainty of late requests that are gradually revealed during the da

  1. A Day-Ahead Dispatching Strategy for Power Pool Composed of Wind Farms, Photovoltaic Generations, Pumped-Storage Power Stations, Gas Turbine Power Plants and Energy Storage Systems Based on Multi Frequency Scale Analysis%基于功率多频率尺度分析的风光水气储联合系统日前调度策略

    Institute of Scientific and Technical Information of China (English)

    马静; 石建磊; 李文泉; 王增平

    2013-01-01

    Based on multi frequency scale analysis on power output of wind farms and photovoltaic (PV) generation (PWP), a day-ahead dispatching strategy for power pool composed of wind farms, photovoltaic generations, pumped-storage power stations, gas turbine power plants and energy storage systems is proposed. Firstly, according to the control objectives the filter analysis on PWP is performed to extract PWP components corresponding to different frequency scales to draft output schedulings for of all kinds of complementary power generations in the power pool; then using improved particle swamp optimization (PSO) the total power output of the power pool is computed;finally, based on different day-ahead dispatching modes and considering stability and wind power accommodation capability of the power pool, the per-unit generation costs of wind farms, photovoltaic generations, pumped-storage power stations, gas turbine power plants and energy storage systems are optimized to draft final output schedulings. Results of calculation example show that comparing with traditional optimization models and optimization algorithms, the proposed method can cope with the power fluctuation due to grid-connection of large-scale wind farms and PV generations to implement stationary power output of the power pool, meanwhile the economy, efficiency, low-carbon operation and environmental protection of the power pool can be ensured.%  提出一种基于功率多频率尺度分析的风光水气储联合系统日前调度策略。该策略首先根据控制目标对风光出力(power of wind photovoltaic,PWP)进行滤波分析,提取不同频率尺度下的PWP分量,制定各类补偿电站出力计划;然后利用改进粒子群优化算法并行优化各电站内部综合成本,计算联合系统总体输出功率;最后依据不同日前调度模式,考虑稳定性和风光消纳能力,优化风光水气储系统单位发电成本,制定出力计划。算例分析结果

  2. Price-Maker Wind Power Producer Participating in a Joint Day-Ahead and Real-Time Market

    DEFF Research Database (Denmark)

    Delikaraoglou, Stefanos; Papakonstantinou, Athanasios; Ordoudis, Christos;

    2015-01-01

    -stage stochastic problem, co-optimizing day-ahead and real-time dispatch. In this framework, we introduce a bilevel model to derive the optimal bid of a strategic wind power producer acting as price-maker both in day-ahead and real-time stages. The proposed model is a Mathematical Program with Equilibrium...... Constraints (MPEC) that is reformulated as a single-level Mixed-Integer Linear Program (MILP), which can be readily solved. Our analysis shows that adopting strategic behaviour may improve producer’s expected profit as the share of wind power increases. However, this incentive diminishes in power systems...

  3. Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Guoqiang Sun

    2014-01-01

    Full Text Available With the development of wind power technology, the security of the power system, power quality, and stable operation will meet new challenges. So, in this paper, we propose a recently developed machine learning technique, relevance vector machine (RVM, for day-ahead wind speed forecasting. We combine Gaussian kernel function and polynomial kernel function to get mixed kernel for RVM. Then, RVM is compared with back propagation neural network (BP and support vector machine (SVM for wind speed forecasting in four seasons in precision and velocity; the forecast results demonstrate that the proposed method is reasonable and effective.

  4. Bidding Strategy for Aggregators of Electric Vehicles in Day-Ahead Electricity Markets

    Directory of Open Access Journals (Sweden)

    Yunpeng Guo

    2017-01-01

    Full Text Available To make full use of the flexible charging and discharging capabilities of the growing number of electric vehicles (EVs, a bidding strategy for EV aggregators to participate in a day-ahead electricity energy market is proposed in this work. The proposed bidding strategy is able to reduce the operating cost of the EV aggregators and to handle the uncertainties of day-ahead market prices properly at the same time. Agreements between the EV owners and the aggregators are discussed, and a hierarchical market structure is proposed. While assuming the aggregators as economic rational entities, the bidding strategy is established based on the market prices, extra battery charging/discharging costs and the expected profits. The bidding clearing system will display the current/temporal market clearance results of the day-ahead market before the final clearance, and hence the market participants can revise their bids and mitigate the risks, to some extent, of forecasted market price forecast errors. Numerical results with a modified IEEE 30-bus system have demonstrated the feasibility and effectiveness of the proposed strategy.

  5. MASCEM: EPEX SPOT Day-Ahead market integration and simulation

    DEFF Research Database (Denmark)

    Santos, Gabriel; Fernandes, Ricardo; Pinto, Tiago

    2015-01-01

    emerged. Decision support tools that facilitate the study and comprehension of these markets became extremely useful, providing players with competitive advantage. MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) arises in this context, modeling and simulating real electricity markets......The energy sector restructuring process in industrialized countries had the aim of reducing electricity prices by increasing competitiveness, and facilitate the integration of distributed energy resources. However, the complexity in market players' interactions has increased, and new problems have....... It is crucial to MASCEM to have the ability to simulate as many market models and player types as possible, thus enhancing the ability to recreate the electricity markets reality in its maximum possible extent. This paper presents the EPEX Spot Day-Ahead market integration in MASCEM. EPEX Spot SE's mission...

  6. Day-Ahead Energy Planning with 100% Electric Vehicle Penetration in the Nordic Region by 2050

    Directory of Open Access Journals (Sweden)

    Zhaoxi Liu

    2014-03-01

    Full Text Available This paper presents the day-ahead energy planning of passenger cars with 100% electric vehicle (EV penetration in the Nordic region by 2050. EVs will play an important role in the future energy systems which can both reduce the greenhouse gas (GHG emissions from the transport sector and provide the demand side flexibility required by smart grids. On the other hand, the EVs will increase the electricity consumption. In order to quantify the electricity consumption increase due to the 100% EV penetration in the Nordic region to facilitate the power system planning studies, the day-ahead energy planning of EVs has been investigated with different EV charging scenarios. Five EV charging scenarios have been considered in the energy planning analysis which are: uncontrolled charging all day, uncontrolled charging at home, timed charging, spot price based charging all day and spot price based charging at home. The demand profiles of the five charging analysis show that timed charging is the least favorable charging option and the spot priced based EV charging might induce high peak demands. The EV charging demand will have a considerable share of the energy consumption in the future Nordic power system.

  7. A two-stage model for a day-ahead paratransit planning problem

    NARCIS (Netherlands)

    Cremers, Maria L. A. G.; Klein Haneveld, Willem K.; van der Vlerk, Maarten H.

    We consider a dynamic planning problem for paratransit transportation. The focus is on a decision to take one day ahead: which requests to serve with own vehicles, and which requests to subcontract to taxis? We call this problem the day-ahead paratransit planning problem. The developed model is a

  8. A two-stage model for a day-ahead paratransit planning problem

    NARCIS (Netherlands)

    Cremers, Maria L. A. G.; Klein Haneveld, Willem K.; van der Vlerk, Maarten H.

    2009-01-01

    We consider a dynamic planning problem for paratransit transportation. The focus is on a decision to take one day ahead: which requests to serve with own vehicles, and which requests to subcontract to taxis? We call this problem the day-ahead paratransit planning problem. The developed model is a no

  9. The Influence of Temperature on Spike Probability in Day-Ahead Power Prices

    NARCIS (Netherlands)

    R. Huisman (Ronald)

    2007-01-01

    textabstractIt is well known that day-ahead prices in power markets exhibit spikes. These spikes are sudden increases in the day-ahead price that occur because power production is not flexible enough to respond to demand and/or supply shocks in the short term. This paper focuses on how temperature i

  10. Retail Pricing and Day-Ahead Demand Response in Smart Distribution Networks

    Directory of Open Access Journals (Sweden)

    GholamReza Yousefi

    2014-03-01

    Full Text Available This paper focuses on day-ahead (DA retailing for fixed and Time-of-Use (TOU price taker customers and DA real time pricing for active customers who participate in short-term markets. Customers’ response to the offered hourly prices are modeled using an hourly acceptance function which includes decreasing linear probability density functions based on the hourly minimum and maximum retail prices allowed by market regulators. Furthermore, the retailer offers to its active customers to participate in the DA demand response program and voluntary reduce their real time consumption for offered incentives. Numerical studies represent the effect of implementing demand response programs on the total benefit of retailing.

  11. Predictive densities for day-ahead electricity prices using time-adaptive quantile regression

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi; Pinson, Pierre; Madsen, Henrik;

    2014-01-01

    is compared to that of four benchmark approaches and the well-known the generalist autoregressive conditional heteroskedasticity (GARCH) model over a three-year evaluation period. While all benchmarks are outperformed in terms of forecasting skill overall, the superiority of the semi-parametric model over......A large part of the decision-making problems actors of the power system are facing on a daily basis requires scenarios for day-ahead electricity market prices. These scenarios are most likely to be generated based on marginal predictive densities for such prices, then enhanced with a temporal...... dependence structure. A semi-parametric methodology for generating such densities is presented: it includes: (i) a time-adaptive quantile regression model for the 5%–95% quantiles; and (ii) a description of the distribution tails with exponential distributions. The forecasting skill of the proposed model...

  12. Everything you need to know to operate on Day-Ahead{sup TM}; Tout ce que vous devez savoir pour intervenir sur Powernext Day-Ahead{sup TM}

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2005-05-15

    The introduction of a power exchange in France is a direct response to the opening up of the European electricity markets. Powernext SA is a Multilateral Trading Facility in charge of managing the French power exchange through an optional and anonymous organised exchange offering: - Day-ahead contracts for the management of volume risk on Powernext Day-Ahead{sup TM} since 21 November 2001, - Medium term contracts for the management of price risk on Powernext Futures{sup TM} since 18 June 2004. This document is the user's guide of Powernext Day-Ahead{sup TM}. It presents: 1 - the power exchange in France (market model, Powernext's regulatory environment, general market operations, 2 - Powernext Day-Ahead{sup TM} members (membership, agreement, start-up notification, tariffs, standing obligations, membership termination), 3 - Powernext Day-Ahead{sup TM} products (specifications, management), 4 - trading (connections, system flow chart, ElWeb client installation and daily connections, portfolio-management, single bidding, type of order, submitting, importing, saving, sending, modifying or canceling an order form, transmission problems, block bidding block bid characteristics, sending, saving, transmitting, modifying and canceling a block bid, price calculations, blind auction procedure, example, taking into account block bids, rounding off rules, consulting and saving the results, sample documents, auction validation), 5 - clearing (LCH.Clearnet SA, legal framework, clearing agreement, PP-DPES agreement, market security, initial margin, daily adjustments, additional margin calls, trade-related financial flows, net financial position, value-added tax, settlement statements, general idea, characteristics and transmission method of settlement statements sample Documents, cash calls, settlement, default, sample cash call documents, financial Reports), 6 - delivery (balance responsible entity, file characteristics and transmission, imbalance settlement

  13. Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling

    Directory of Open Access Journals (Sweden)

    Huiru Zhao

    2016-09-01

    Full Text Available An important goal of China’s electric power system reform is to create a double-side day-ahead wholesale electricity market in the future, where the suppliers (represented by GenCOs and demanders (represented by DisCOs compete simultaneously with each other in one market. Therefore, modeling and simulating the dynamic bidding process and the equilibrium in the double-side day-ahead electricity market scientifically is not only important to some developed countries, but also to China to provide a bidding decision-making tool to help GenCOs and DisCOs obtain more profits in market competition. Meanwhile, it can also provide an economic analysis tool to help government officials design the proper market mechanisms and policies. The traditional dynamic game model and table-based reinforcement learning algorithm have already been employed in the day-ahead electricity market modeling. However, those models are based on some assumptions, such as taking the probability distribution function of market clearing price (MCP and each rival’s bidding strategy as common knowledge (in dynamic game market models, and assuming the discrete state and action sets of every agent (in table-based reinforcement learning market models, which are no longer applicable in a realistic situation. In this paper, a modified reinforcement learning method, called gradient descent continuous Actor-Critic (GDCAC algorithm was employed in the double-side day-ahead electricity market modeling and simulation. This algorithm can not only get rid of the abovementioned unrealistic assumptions, but also cope with the Markov decision-making process with continuous state and action sets just like the real electricity market. Meanwhile, the time complexity of our proposed model is only O(n. The simulation result of employing the proposed model in the double-side day-ahead electricity market shows the superiority of our approach in terms of participant’s profit or social welfare

  14. An Assessment of the Impact of Stochastic Day-Ahead SCUC on Economic and Reliability Metrics at Multiple Timescales

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Hongyu; Ela, Erik; Krad, Ibrahim; Florita, Anthony; Zhang, Jie; Hodge, Bri-Mathias; Ibanez, Eduardo; Gao, Wenzhong

    2015-10-05

    This paper incorporates the stochastic day-ahead security-constrained unit commitment (DASCUC) within a multi-timescale, multi-scheduling application with commitment, dispatch, and automatic generation control. The stochastic DASCUC is solved using a progressive hedging algorithm with constrained ordinal optimization to accelerate the individual scenario solution. Sensitivity studies are performed in the RTS-96 system, and the results show how this new scheduling application would impact costs and reliability with a closer representation of timescales of system operations in practice.

  15. Assessment of the Impact of Stochastic Day-Ahead SCUC on Economic and Reliability Metrics at Multiple Timescales: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Wu, H.; Ela, E.; Krad, I.; Florita, A.; Zhang, J.; Hodge, B. M.; Ibanez, E.; Gao, W.

    2015-03-01

    This paper incorporates the stochastic day-ahead security-constrained unit commitment (DASCUC) within a multi-timescale, multi-scheduling application with commitment, dispatch, and automatic generation control. The stochastic DASCUC is solved using a progressive hedging algorithm with constrained ordinal optimization to accelerate the individual scenario solution. Sensitivity studies are performed in the RTS-96 system, and the results show how this new scheduling application would impact costs and reliability with a closer representation of timescales of system operations in practice.

  16. Day-Ahead Energy Planning with 100% Electric Vehicle Penetration in the Nordic Region by 2050

    DEFF Research Database (Denmark)

    Liu, Zhaoxi; Wu, Qiuwei; Nielsen, Arne Hejde

    2014-01-01

    This paper presents the day-ahead energy planning of passenger cars with 100% electric vehicle (EV) penetration in the Nordic region by 2050. EVs will play an important role in the future energy systems which can both reduce the greenhouse gas (GHG) emission from the transport sector and provide...... demand side flexibility required by the smart grids. On the other hand, the EVs will increase the electricity consumption. In order to quantify the electricity consumption increase due to the 100% EV penetration in the Nordic region to facilitate the power system planning studies, the day-ahead energy...

  17. Day-ahead distributed energy resource scheduling using differential search algorithm

    DEFF Research Database (Denmark)

    Soares, J.; Lobo, C.; Silva, M.

    2015-01-01

    considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. This paper presents an application of differential search algorithm (DSA) for solving the day-ahead scheduling...... a contingency on the large wind farm and different forecasts regarding load demand....

  18. Economic Benefit of Combining Wave and Wind Power Productions in Day-Ahead Electricity Markets

    DEFF Research Database (Denmark)

    Chozas, Julia Fernandez; Sørensen, H.C.; Helstrup Jensen, N.E.

    2012-01-01

    There is usually a cost associated to the integration of non-fully predictable renewables in electricity markets. This cost, named balancing cost, covers the difference between the bid to the day-ahead electricity market and the actual power produced. The objective of the paper is comparing...

  19. Day-ahead resource scheduling including demand response for electric vehicles

    DEFF Research Database (Denmark)

    Soares, Joao; Morais, Hugo; Sousa, Tiago

    2014-01-01

    Summary form only given. The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considerin...

  20. Day-ahead tariffs for the alleviation of distribution grid congestion from electric vehicles

    DEFF Research Database (Denmark)

    O'Connell, Niamh; Wu, Qiuwei; Østergaard, Jacob

    2012-01-01

    An economically efficient day-ahead tariff (DT) is proposed with the purpose of preventing the distribution grid congestion resulting from electric vehicle (EV) charging scheduled on a dayahead basis. The DT concept developed herein is derived from the locational marginal price (LMP), in particular...

  1. Quantum/Relativistic Computation of Security and Efficiency of Electrical Power System for a Day-Ahead: I. Renormalization

    CERN Document Server

    Stefanov, Stefan Z

    2011-01-01

    The realization of Daily Artificial Dispatcher as a quantum/relativistic computation consists of perturbative renormalization of the Electrical Power System (EPS), generating the flowcharts of computation, verification, validation, description and help. Perturbative renormalization of EPS energy and time has been carried out in this paper for a day ahead via virtual thermalization of the EPS for a day ahead.

  2. Morphological impact of a storm can be predicted three days ahead

    Science.gov (United States)

    Baart, F.; van Ormondt, M.; van Thiel de Vries, J. S. M.; van Koningsveld, M.

    2016-05-01

    People living behind coastal dunes depend on the strength and resilience of dunes for their safety. Forecasts of hydrodynamic conditions and morphological change on a timescale of several days can provide essential information to protect lives and property. In order for forecasts to protect they need be relevant, accurate, provide lead time, and information on confidence. Here we show how confident one can be in morphological predictions of several days ahead. The question is answered by assessing the forecast skill as a function of lead time. The study site in the town of Egmond, the Netherlands, where people depend on the dunes for their safety, is used because it is such a rich data source, with a history of forecasts, tide gauges and bathymetry measurements collected by video cameras. Even though the forecasts are on a local scale, the methods are generally applicable. It is shown that the intertidal beach volume change can be predicted up to three days ahead.

  3. Customer Strategies for Responding to Day-Ahead Market HourlyElectricity Pricing

    Energy Technology Data Exchange (ETDEWEB)

    Goldman, Chuck; Hopper, Nicole; Bharvirkar, Ranjit; Neenan,Bernie; Boisvert, Dick; Cappers, Peter; Pratt, Donna; Butkins, Kim

    2005-08-25

    Real-time pricing (RTP) has been advocated as an economically efficient means to send price signals to customers to promote demand response (DR) (Borenstein 2002, Borenstein 2005, Ruff 2002). However, limited information exists that can be used to judge how effectively RTP actually induces DR, particularly in the context of restructured electricity markets. This report describes the second phase of a study of how large, non-residential customers' adapted to default-service day-ahead hourly pricing. The customers are located in upstate New York and served under Niagara Mohawk, A National Grid Company (NMPC)'s SC-3A rate class. The SC-3A tariff is a type of RTP that provides firm, day-ahead notice of hourly varying prices indexed to New York Independent System Operator (NYISO) day-ahead market prices. The study was funded by the California Energy Commission (CEC)'s PIER program through the Demand Response Research Center (DRRC). NMPC's is the first and longest-running default-service RTP tariff implemented in the context of retail competition. The mix of NMPC's large customers exposed to day-ahead hourly prices is roughly 30% industrial, 25% commercial and 45% institutional. They have faced periods of high prices during the study period (2000-2004), thereby providing an opportunity to assess their response to volatile hourly prices. The nature of the SC-3A default service attracted competitive retailers offering a wide array of pricing and hedging options, and customers could also participate in demand response programs implemented by NYISO. The first phase of this study examined SC-3A customers' satisfaction, hedging choices and price response through in-depth customer market research and a Constant Elasticity of Substitution (CES) demand model (Goldman et al. 2004). This second phase was undertaken to answer questions that remained unresolved and to quantify price response to a higher level of granularity. We accomplished these

  4. Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market

    Directory of Open Access Journals (Sweden)

    Claudio Monteiro

    2015-09-01

    Full Text Available This paper presents the analysis of the importance of a set of explanatory (input variables for the day-ahead price forecast in the Iberian Electricity Market (MIBEL. The available input variables include extensive hourly time series records of weather forecasts, previous prices, and regional aggregation of power generations and power demands. The paper presents the comparisons of the forecasting results achieved with a model which includes all these available input variables (EMPF model with respect to those obtained by other forecasting models containing a reduced set of input variables. These comparisons identify the most important variables for forecasting purposes. In addition, a novel Reference Explanatory Model for Price Estimations (REMPE that achieves hourly price estimations by using actual power generations and power demands of such day is described in the paper, which offers the lowest limit for the forecasting error of the EMPF model. All the models have been implemented using the same technique (artificial neural networks and have been satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL. The relative importance of each explanatory variable is identified for the day-ahead price forecasts in the MIBEL. The comparisons also allow outlining guidelines of the value of the different types of input information.

  5. The impact of wind power on APX day-ahead electricity prices in the Netherlands VVM-Intermittency project

    Energy Technology Data Exchange (ETDEWEB)

    Nieuwenhout, F.D.J. [ECN Policy Studies, Amsterdam (Netherlands); Brand, A.J. [ECN Wind Energy, Petten (Netherlands)

    2013-02-15

    A detailed analysis was conducted to assess to what extent availability of wind energy has influenced day-ahead electricity prices in the Netherlands over the period 2006-2009. With a meteorological model, time series of day-ahead wind forecasts were generated, and these were compared with APX-ENDEX day-ahead market prices. Wind energy contributes to only 4% of electricity generation in the Netherlands, but was found to depress average day-ahead market prices by about 5%. With the help of the bid curves on the APX-ENDEX day-ahead market for 2009, a model was developed to assess the impact of increasing levels of wind generation on power prices in the Netherlands. One of the main findings is that the future impact on prices will be less than in the past. With an increase of installed wind capacity from 2200 MW to 6000 MW, average day-ahead prices are expected to be depressed by an additional 6% in case no additional conventional generation is assumed. Taking into account existing government policy on wind and ongoing investments in new conventional power plants, prices in 2016 will be only 3% lower.

  6. Day-Ahead Coordination of Vehicle-to-Grid Operation and Wind Power in Security Constraints Unit Commitment (SCUC

    Directory of Open Access Journals (Sweden)

    Mohammad Javad Abdollahi

    2015-08-01

    Full Text Available In this paper security constraints unit commitment (SCUC in the presence of wind power resources and electrical vehicles to grid is presented. SCUC operation prepare an optimal time table for generation unit commitment in order to maximize security, minimize operation cost and satisfy the constraints of networks and units in a period of time, as one of the most important research interest in power systems. Today, the relationship between power network and energy storage systems is interested for many researchers and network operators. Using Electrical Vehicles (PEVs and wind power for energy production is one of the newest proposed methods for replacing fossil fuels.One of the effective strategies for analyzing of the effects of Vehicle 2 Grid (V2G and wind power in optimal operation of generation is running of SCUC for power systems that are equipped with V2G and wind power resources. In this paper, game theory method is employed for deterministic solution of day-ahead unit commitment with considering security constraints in the simultaneous presence of V2G and wind power units. This problem for two scenarios of grid-controlled mode and consumer-controlled mode in three different days with light, medium and heavy load profiles is analyzed. Simulation results show the effectiveness of the presence of V2G and wind power for decreasing of generation cost and improving operation indices of power systems.

  7. Congestion management of distribution networks with day-ahead dynamic grid tariffs

    DEFF Research Database (Denmark)

    Huang, Shaojun; Wu, Qiuwei

    vehicles (EV) and heat pumps (HP), will be largely deployed in electrical distribution networks. Congestion management will be important in the future active distribution networks. In the IDE4L project, work package 5 is dedicated to develop different kinds of congestion management methods. Demand response......In order to reduce CO2 emissions and alleviate the global warming issue, many countries are setting goals to increase the percentage of renewable energy in the total energy consumption. In this process, a large number of distributed energy resources (DER), distributed generation (DG), electric...... (DR) is one of the important methods. In this report, as one task of work package 5, the day-ahead dynamic tariff (DADT) method for congestion management in distribution networks is presented. The dynamic tariff (DT) can motivate the flexible demands (EV and HP) to shift their energy consumption...

  8. Day-Ahead Scheduling Considering Demand Response as a Frequency Control Resource

    Directory of Open Access Journals (Sweden)

    Yu-Qing Bao

    2017-01-01

    Full Text Available The development of advanced metering technologies makes demand response (DR able to provide fast response services, e.g., primary frequency control. It is recognized that DR can contribute to the primary frequency control like thermal generators. This paper proposes a day-ahead scheduling method that considers DR as a frequency control resource, so that the DR resources can be dispatched properly with other resources. In the proposed method, the objective of frequency control is realized by defining a frequency limit equation under a supposed contingency. The frequency response model is used to model the dynamics of system frequency. The nonlinear frequency limit equation is transformed to a linear arithmetic equation by piecewise linearization, so that the problem can be solved by mixed integer linear programming (MILP. Finally, the proposed method is verified on numerical examples.

  9. Thermal energy storage for electricity-driven space heating in a day-ahead electricity market

    DEFF Research Database (Denmark)

    Pensini, Alessandro

    2012-01-01

    Thermal Energy Storage (TES) in a space heating (SH) application was investigated. The study aimed to determine the economic benefits of introducing TES into an electricity-driven SH system under a day-ahead electricity market. The performance of the TES was assessed by comparing the cost...... of electricity in a system with a TES unit to the case where no storage is in use and the entire heat requirement is fulfilled by purchasing electricity according to the actual load. The study had two goals: 1. Determining how the size – in terms of electricity input (Pmax) and energy capacity (Emax......) – of the TES unit influences the savings. For this purpose, a reference price signal was used. Results show that it is possible to save up to approximately 14% of the electricity costs. In general, savings increase with Pmax and Emax. However, the benefit of increasing these two values ceases when certain...

  10. Day-Ahead Reactive Power Scheduling for Distribution Network Considering Coordination of Distributed Generation With Capacitors%计及分布式电源与电容器协调的配电网日前无功计划

    Institute of Scientific and Technical Information of China (English)

    谭煌; 张璐; 丛鹏伟; 唐巍; 耿光飞; 杨德昌; 李绚丽

    2014-01-01

    针对目前含分布式电源(distributed generation,DG)的配电网中未考虑电容器补偿容量和 DG 无功出力协调调度的问题,研究了考虑 DG 与电容器组协调的无功优化方法。以网损和电压偏移满意度最高为目标,构建含DG的配电网日前动态无功优化调度模型。根据DG无功出力和电容器补偿的特点,提出 DG 和电容器协调的日前无功计划方法。分析了各类DG的无功出力极限并作为约束条件,对电容器和DG进行整体静态优化得到电容器的投切容量曲线;其次采用模糊聚类对电容器投切曲线进行时序分段并融合,制定电容器的日前计划;最后,在电容器补偿容量确定后,以DG作为优化变量,制定DG出力的日前计划。仿真结果验证了所提方法的有效性。%In allusion to the situation that in present distribution networks containing distributed generation (DG) the coordinated dispatching of the compensating capacity of capacitors with reactive output of DG is not taken into account, a reactive power optimization method considering the coordination of capacitor banks with DG is researched. Taking the highest satisfaction degree of both network loss and voltage deviation as the objective, an optimal day-ahead dynamic reactive power scheduling model for distribution network containing DG is constructed. Based on the reactive power output of DG and the features of capacitor compensation, a day-ahead reactive power scheduling that coordinates the DG with capacitor banks is proposed. Firstly, the limits of various DG’s reactive power output are analyzed and taken as constraints, the overall static state optimization of DG and capacitor banks is performed to attain the switching on/off capacity curve of capacitor banks; then fuzzy clustering is adapted to segment the capacity curve and subsection integration is used to formulate the day ahead schedule of capacitors;finally, after the determination of

  11. Powernext Day-Ahead. Powernext Futures. Powernext Carbon. Powernext Weather. Activity assessment first-half 2006; Powernext Day-Ahead. Powernext Futures. Powernext Carbon. Powernext Weather. Bilan d'activite premier semestre 2006

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2006-07-01

    Powernext SA is a Multilateral Trading Facility which organizes and warrants the transactions on the European power exchange and CO{sub 2} exchange markets. This activity report presents the highlights of the market and of Powernext in the first half of 2006: market conditions, prices and traded volumes on Powernext (Powernext Day-Ahead{sup TM} in the case of day-ahead contracts, Powernext Futures{sup TM} in the case of medium-term contracts, and Powernext Carbon in the case of CO{sub 2}), new active members, and liquidity on the power market. (J.S.)

  12. Influence of feasibility constrains on the bidding strategy selection in a day-ahead electricity market session

    Energy Technology Data Exchange (ETDEWEB)

    Borghetti, Alberto [Dept. of Electrical Engineering, University of Bologna, Viale risorgimento 2, 40136 Bologna (Italy); Massucco, Stefano; Silvestro, Federico [Dept. of Electrical Engineering, University of Genova, via all' Opera Pia 11a, 16145 Genova (Italy)

    2009-12-15

    Large part of liberalized electricity markets, including the Italian one, features an auction mechanism, called day-ahead energy market, which matches producers' and buyers' simple bids, consisting of energy quantity and price pairs. The match is achieved by a merit-order economic dispatch procedure independently applied for each of the hours of the following day. Power plants operation should, however, take into account several technical constraints, such as maximum and minimum production bounds, ramp constraints and minimum up and downs times, as well as no-load and startup costs. The presence of these constraints forces to adjust the scheduling provided by the market in order to obtain a feasible scheduling. The paper presents an analysis of the possibility and the limits of taking into account the power plants technical constraints in the bidding strategy selection procedure of generating companies (Gencos). The analysis is carried out by using a computer procedure based both on a simple static game-theory approach and on a cost-minimization unit-commitment algorithm. For illustrative purposes, we present the results obtained for a system with three Gencos, each owning several power plants, trying to model the bidding behaviour of every generator in the system. This approach, although complex from the computational point of view, allows an analysis of both price and quantity bidding strategies and appears to be applicable to markets having different rules and features. (author)

  13. Development and validation of a 5-day-ahead hay fever forecast for patients with grass-pollen-induced allergic rhinitis.

    Science.gov (United States)

    de Weger, Letty A; Beerthuizen, Thijs; Hiemstra, Pieter S; Sont, Jacob K

    2014-08-01

    One-third of the Dutch population suffers from allergic rhinitis, including hay fever. In this study, a 5-day-ahead hay fever forecast was developed and validated for grass pollen allergic patients in the Netherlands. Using multiple regression analysis, a two-step pollen and hay fever symptom prediction model was developed using actual and forecasted weather parameters, grass pollen data and patient symptom diaries. Therefore, 80 patients with a grass pollen allergy rated the severity of their hay fever symptoms during the grass pollen season in 2007 and 2008. First, a grass pollen forecast model was developed using the following predictors: (1) daily means of grass pollen counts of the previous 10 years; (2) grass pollen counts of the previous 2-week period of the current year; and (3) maximum, minimum and mean temperature (R (2)=0.76). The second modeling step concerned the forecasting of hay fever symptom severity and included the following predictors: (1) forecasted grass pollen counts; (2) day number of the year; (3) moving average of the grass pollen counts of the previous 2 week-periods; and (4) maximum and mean temperatures (R (2)=0.81). Since the daily hay fever forecast is reported in three categories (low-, medium- and high symptom risk), we assessed the agreement between the observed and the 1- to 5-day-ahead predicted risk categories by kappa, which ranged from 65 % to 77 %. These results indicate that a model based on forecasted temperature and grass pollen counts performs well in predicting symptoms of hay fever up to 5 days ahead.

  14. Optimal Day-Ahead Scheduling of a Hybrid Electric Grid Using Weather Forecasts

    Science.gov (United States)

    2013-12-01

    geothermal energy, the DoD’s wind power production is still insignificant, despite the fact that wind power is one of the most abundant and promising...million American homes, or the same amount of electricity as 10 nuclear power plants [16]. This 60 GW wind power capacity reduces carbon dioxide (CO2...thermal power plants [16]. 8 Figure 4. Global cumulative installed wind capacity 1996-2012 (from [18]) Despite its abundance, wind energy is

  15. Optimal Day-ahead Charging Scheduling of Electric Vehicles through an Aggregative Game Model

    DEFF Research Database (Denmark)

    Liu, Zhaoxi; Wu, Qiuwei; Huang, Shaojun

    2017-01-01

    The electric vehicle (EV) market has been growing rapidly around the world. With large scale deployment of EVs in power systems, both the grid and EV owners will benefit if the flexible demand of EV charging is properly managed through the electricity market. When EV charging demand is considerable...

  16. Customer response to day-ahead wholesale market electricity prices: Case study of RTP program experience in New York

    Energy Technology Data Exchange (ETDEWEB)

    Goldman, C.; Hopper, N.; Sezgen, O.; Moezzi, M.; Bharvirkar, R.; Neenan, B.; Boisvert, R.; Cappers, P.; Pratt, D.

    2004-07-01

    There is growing interest in policies, programs and tariffs that encourage customer loads to provide demand response (DR) to help discipline wholesale electricity markets. Proposals at the retail level range from eliminating fixed rate tariffs as the default service for some or all customer groups to reinstituting utility-sponsored load management programs with market-based inducements to curtail. Alternative rate designs include time-of-use (TOU), day-ahead real-time pricing (RTP), critical peak pricing, and even pricing usage at real-time market balancing prices. Some Independent System Operators (ISOs) have implemented their own DR programs whereby load curtailment capabilities are treated as a system resource and are paid an equivalent value. The resulting load reductions from these tariffs and programs provide a variety of benefits, including limiting the ability of suppliers to increase spot and long-term market-clearing prices above competitive levels (Neenan et al., 2002; Boren stein, 2002; Ruff, 2002). Unfortunately, there is little information in the public domain to characterize and quantify how customers actually respond to these alternative dynamic pricing schemes. A few empirical studies of large customer RTP response have shown modest results for most customers, with a few very price-responsive customers providing most of the aggregate response (Herriges et al., 1993; Schwarz et al., 2002). However, these studies examined response to voluntary, two-part RTP programs implemented by utilities in states without retail competition.1 Furthermore, the researchers had limited information on customer characteristics so they were unable to identify the drivers to price response. In the absence of a compelling characterization of why customers join RTP programs and how they respond to prices, many initiatives to modernize retail electricity rates seem to be stymied.

  17. Powernext Day-Ahead. Powernext Futures. Powernext Carbon. Powernext Weather. 2006 activity assessment; Powernext Day-Ahead. Powernext Futures. Powernext Carbon. Powernext Weather. Bilan d'activite 2006

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2006-07-01

    Powernext SA is a Multilateral Trading Facility which organizes and warrants the transactions on the European power exchange and CO{sub 2} exchange markets. This activity report presents the highlights of the market and of Powernext in 2006: market conditions (prices on the electricity market, prices on the CO{sub 2} emission allowances market, weather conditions, institutional aspects of the CO{sub 2} market, power generation and consumption, situation at the borders, fuel prices), traded volumes on Powernext (Powernext Day-Ahead{sup TM} in the case of day-ahead contracts, Powernext Futures{sup TM} in the case of medium-term contracts, and Powernext Carbon in the case of CO{sub 2}), new active members, and Powernext liquidity on the power market. (J.S.)

  18. Offering strategy of a price-maker energy storage system in day-ahead and balancing markets

    DEFF Research Database (Denmark)

    Vespermann, Niklas Peter René Erich; Delikaraoglou, Stefanos; Pinson, Pierre

    2017-01-01

    Energy storage systems (ESS) are considered as a promising solution to improve power system flexibility and facilitate the integration of renewables in electricity markets. This paper investigates the impact of strategic offering by an ESS operator in the day-ahead and balancing market....... The offering strategy of a price-maker ESS operator is formulated as a bilevel model, where the upper-level problem represents the profit maximization of the ESS operator and the lower-level problem simulates the market-clearing outcome. This methodological framework can be used either to assess market...

  19. New approach to bidding strategies of generating companies in day ahead energy market

    Energy Technology Data Exchange (ETDEWEB)

    Soleymani, S.; Ranjbar, A.M. [Sharif University of Technology, Tehran (Iran); Niroo Research Institute, Tehran (Iran); Shirani, A.R. [Niroo Research Institute, Tehran (Iran)

    2008-06-15

    In the restructured power systems, generating companies (Genco) are responsible for selling their product in the energy market. In this condition, the question is how much and for what price must each Genco generate to maximize its profit. Therefore, this paper intends to propose a rational method to answer this question. In the proposed methodology, the hourly forecasted market clearing price (FMCP) is used as a reference to model the possible and probable price strategies of Gencos. The forecasted price is the basis of the bidding strategies of each Genco, which can be achieved by solving a bi-level optimization problem using GAMS (general algebraic modeling system) language. The first level, called upper sub-problem is used to maximize the individual Genco's payoffs for obtaining the optimal offered quantity of Gencos. The second one, hereafter called the lower sub-problem uses the results of the upper sub-problem and minimizes the consumer's payment with regard to the technical and network constraints, which leads to the awarded generation of the Gencos. Similar to the other game problems, the Nash equilibrium strategies are the optimum bidding strategies of Gencos. A six bus system is employed to illustrate the application of the proposed method and to show its high precision and capabilities. (author)

  20. Day-Ahead Congestion Management in Distribution Systems through Household Demand Response and Distribution Congestion Prices

    DEFF Research Database (Denmark)

    Liu, Weijia; Wu, Qiuwei; Wen, Fushuan

    2014-01-01

    into balancing power might challenge the operation of electric distribution systems and cause congestions. This paper presents a distribution congestion price (DCP) based market mechanism to alleviate possible distribution system congestions. By employing the loca- tional marginal pricing (LMP) model......, the proposed DCPs are able to reflect the real congestion cost and further direct the schedule of the responses of electric demands. Based on the NordPool Spot market structure, the interactions between aggregators and the distribution system operator (DSO) are discussed, and the procedure for calculating DCPs...

  1. Extended ARMA models for estimating price developments on day-ahead electricity markets

    Energy Technology Data Exchange (ETDEWEB)

    Swider, Derk J. [Institute of Energy Economics and the Rational Use of Energy, University of Stuttgart, Hessbruehlstr. 49a, 70565 Stuttgart (Germany); Weber, Christoph [University of Duisburg-Essen, Universitaetsstr. 12, 45117 Essen (Germany)

    2007-04-15

    In this paper extended models for estimating price developments on electricity markets are presented. The models consider deviations from the normality hypothesis of the prices. Based on an ARMA model combination with GARCH, Gaussian-mixture and switching-regime approaches are comparatively discussed. The comparison is based on historic electricity prices of the spot and two reserve markets in Germany. It is shown that the proposed extended models lead to significantly improved representations of the considered stochastic price processes. It is inferred that these models may be preferred for estimating price developments on electricity markets. (author)

  2. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models

    Energy Technology Data Exchange (ETDEWEB)

    Tan, Zhongfu; Zhang, Jinliang; Xu, Jun [North China Electric Power University, Beijing 102206 (China); Wang, Jianhui [Argonne National Laboratory, Argonne, IL 60439 (United States)

    2010-11-15

    This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. (author)

  3. Day-Ahead Short-Term Forecasting Electricity Load via Approximation

    Science.gov (United States)

    Khamitov, R. N.; Gritsay, A. S.; Tyunkov, D. A.; E Sinitsin, G.

    2017-04-01

    The method of short-term forecasting of a power consumption which can be applied to short-term forecasting of power consumption is offered. The offered model is based on sinusoidal function for the description of day and night cycles of power consumption. Function coefficients - the period and amplitude are set up is adaptive, considering dynamics of power consumption with use of an artificial neural network. The presented results are tested on real retrospective data of power supply company. The offered method can be especially useful if there are no opportunities of collection of interval indications of metering devices of consumers, and the power supply company operates with electrical supply points. The offered method can be used by any power supply company upon purchase of the electric power in the wholesale market. For this purpose, it is necessary to receive coefficients of approximation of sinusoidal function and to have retrospective data on power consumption on an interval not less than one year.

  4. Optimal bidding strategy of battery storage in power markets considering performance based regulation and battery cycle life

    DEFF Research Database (Denmark)

    He, Guannan; Chen, Qixin; Kang, Chongqing

    2016-01-01

    Large-scale battery storage will become an essential part of the future smart grid. This paper investigates the optimal bidding strategy for battery storage in power markets. Battery storage could increase its profitability by providing fast regulation service under a performance-based regulation...... degree. Thus, we incorporate a battery cycle life model into a profit maximization model to determine the optimal bids in day-ahead energy, spinning reserve, and regulation markets. Then a decomposed online calculation method to compute cycle life under different operational strategies is proposed...

  5. Not All Large Customers are Made Alike: Disaggregating Response toDefault-Service Day-Ahead Market Pricing

    Energy Technology Data Exchange (ETDEWEB)

    Hopper, Nicole; Goldman, Charles; Neenan, Bernie

    2006-05-12

    For decades, policymakers and program designers have gone onthe assumption that large customers, particularly industrial facilities,are the best candidates for realtime pricing (RTP). This assumption isbased partly on practical considerations (large customers can providepotentially large load reductions) but also on the premise thatbusinesses focused on production cost minimization are most likely toparticipate and respond to opportunities for bill savings. Yet fewstudies have examined the actual price response of large industrial andcommercial customers in a disaggregated fashion, nor have factors such asthe impacts of demand response (DR) enabling technologies, simultaneousemergency DR program participation and price response barriers been fullyelucidated. This second-phase case study of Niagara Mohawk PowerCorporation (NMPC)'s large customer RTP tariff addresses theseinformation needs. The results demonstrate the extreme diversity of largecustomers' response to hourly varying prices. While two-thirdsexhibitsome price response, about 20 percent of customers provide 75-80 percentof the aggregate load reductions. Manufacturing customers are mostprice-responsive as a group, followed by government/education customers,while other sectors are largely unresponsive. However, individualcustomer response varies widely. Currently, enabling technologies do notappear to enhance hourly price response; customers report using them forother purposes. The New York Independent System Operator (NYISO)'semergency DR programs enhance price response, in part by signaling tocustomers that day-ahead prices are high. In sum, large customers docurrently provide moderate price response, but there is significant roomfor improvement through targeted programs that help customers develop andimplement automated load-response strategies.

  6. On the effectiveness of the anti-gaming policy between the day-ahead and real-time electricity markets in The Netherlands

    NARCIS (Netherlands)

    Boogert, Alexander; Dupont, D.Y.

    2005-01-01

    In this paper, we study the linkage between two related markets for electricity in The Netherlands: the day-ahead market and the real-time market. The Dutch regulator wants to prevent trading across these two markets and has set up a dual pricing system for this purpose. In this paper, we test the

  7. 主动型配电网日前调度策略研究%Study on day-ahead dispatch strategy of active distribution network

    Institute of Scientific and Technical Information of China (English)

    窦震海; 牛焕娜; 高燕; 杨明皓; 杨仁刚

    2014-01-01

    /low voltage distribution network, electricity sales of the power company are bound to decrease and operating income will accordingly be reduced, which is an avoided problem in previous research. All scheduling models proposed in the studies are based on the goal of the lowest supply power cost, and are not concerned with who gets the operating income. However, this is the most significant problem of power companies. In this paper, a novel day-ahead scheduling model of active distribution network is supposed. The goal of this model is maximizing the profit of the power company. And it is more objectively described as purchase power costs and sale power revenues of Chinese power companies with an active distribution network. Moreover, this model takes into account the various power prices of different distributed power, the incentive policies of government and government subsidies of the power company. More importantly, because the network losses of distribution lines in high voltage network before the inlet of medium voltage distribution network is correlated strongly with its electrical distance. This model accounts for the profit of the power company which network losses are reduced owing to use of distribute power and micro-grid. Using this model, the active medium voltage distribution networks are scheduled in different locations, which not only can achieve greater efficiency of power companies through accessing the distribute power and micro-grid at the location away from the main network, but also don’t face losses in close supply power with the new energy accesses through reasonable scheduling. In this paper, examples of IEEE33 node demonstrate the effectiveness and feasibility of the model. The results can provide a reference for optimization scheduling of the active distribution network.%相对于传统配电网,主动型配电网可以合理利用其双向调度功能,充分发挥分布式发电容量,在保护环境的同时还可以提高电力公司的效

  8. 电力用户参与风电消纳的日前市场模式%Day-ahead Market Mode with Power Consumers Participation in Wind Power Accommodation

    Institute of Scientific and Technical Information of China (English)

    夏叶; 康重庆; 陈天恩; 李焰

    2015-01-01

    Facing the wind power accommodation difficulties revealed in‘Northwest,North and Northeast China”region during heating season overlapping wind resource surplus season,a novel day-ahead market mode is proposed to encourage power consumers to participate in wind power accommodation based on the market reform plan of direct electricity purchase by major power consumers.Based on power consumer and generation company direct transactions in the spot market environment,the day-ahead market mode with power consumers participating in wind power accommodation is designed including transaction application,transaction clearing,transaction settlement and market organization.A day-ahead market model is developed with power consumers participation in the wind power accommodation mode taken into account alongside individual demands of each power consumer”s power consumption profile and bidding price limits on power consumers.The rationality and effectiveness of the proposed market mode and model is validated by numerical results.%针对中国西北、华北、东北(“三北”)地区富风期与供热期相重叠造成的风电消纳困境,结合大用户直购电市场化改革方案,提出了激励电力用户参与风电消纳的日前市场模式。依托于现货市场环境下的电力用户与发电企业直接交易,设计了电力用户在日前市场中参与风电消纳的交易申报、交易出清、交易结算及市场组织;精细化考虑电力用户调峰用电曲线的个性化调用需求、电力用户报价限制等约束,建立了考虑电力用户参与风电消纳的日前市场出清模型。算例验证了所述市场模式及模型的合理性、有效性。

  9. Exploiting Flexibility in Coupled Electricity and Natural Gas Markets: A Price-Based Approach

    DEFF Research Database (Denmark)

    Ordoudis, Christos; Delikaraoglou, Stefanos; Pinson, Pierre

    2017-01-01

    . Considering a market-based coupling of these systems, we introduce a decision support tool that increases market efficiency in the current setup where day-ahead and balancing markets are cleared sequentially. The proposed approach relies on the optimal adjustment of natural gas price to modify the scheduling...... of power plants and reveals the necessary flexibility to handle stochastic renewable production. An essential property of this price-based approach is that it guarantees no financial imbalance (deficit or surplus) for the system operator at the day-ahead stage. Our analysis shows that the proposed...... mechanism reduces the expected system cost and efficiently accommodates high shares of renewables....

  10. Optimal Planning Strategy for Large PV/Battery System Based on Long-Term Insolation Forecasting

    Science.gov (United States)

    Yona, Atsushi; Uchida, Kosuke; Senjyu, Tomonobu; Funabashi, Toshihisa

    Photovoltaic (PV) systems are rapidly gaining acceptance as some of the best alternative energy sources. Usually the power output of PV system fluctuates depending on weather conditions. In order to control the fluctuating power output for PV system, it requires control method of energy storage system. This paper proposes an optimization approach to determine the operational planning of power output for PV system with battery energy storage system (BESS). This approach aims to obtain more benefit for electrical power selling and to smooth the fluctuating power output for PV system. The optimization method applies genetic algorithm (GA) considering PV power output forecast error. The forecast error is based on our previous works with the insolation forecasting at one day ahead by using weather reported data, fuzzy theory and neural network(NN). The validity of the proposed method is confirmed by the computer simulations.

  11. A Convex Model of Risk-Based Unit Commitment for Day-Ahead Market Clearing Considering Wind Power Uncertainty

    DEFF Research Database (Denmark)

    Zhang, Ning; Kang, Chongqing; Xia, Qing

    2015-01-01

    The integration of wind power requires the power system to be sufficiently flexible to accommodate its forecast errors. In the market clearing process, the scheduling of flexibility relies on the manner in which the wind power uncertainty is addressed in the unit commitment (UC) model. This paper...... and are considered in both the objective functions and the constraints. The RUC model is shown to be convex and is transformed into a mixed integer linear programming (MILP) problem using relaxation and piecewise linearization. The proposed RUC model is tested using a three-bus system and an IEEE RTS79 system...... that the risk modeling facilitates a strategic market clearing procedure with a reasonable computational expense....

  12. A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

    Directory of Open Access Journals (Sweden)

    Fanrong Kong

    2017-09-01

    Full Text Available To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving.

  13. Distinguishing high and low flow domains in urban drainage systems 2 days ahead using numerical weather prediction ensembles

    DEFF Research Database (Denmark)

    Courdent, Vianney Augustin Thomas; Grum, Morten; Mikkelsen, Peter Steen

    2017-01-01

    Precipitation constitutes a major contribution to the flow in urban storm- and wastewater systems. Forecasts of the anticipated runoff flows, created from radar extrapolation and/or numerical weather predictions, can potentially be used to optimize operation in both wet and dry weather periods....... However, flow forecasts are inevitably uncertain and their use will ultimately require a trade-off between the value of knowing what will happen in the future and the probability and consequence of being wrong. In this study we examine how ensemble forecasts from the HIRLAM-DMI-S05 numerical weather...... prediction (NWP) model subject to three different ensemble post-processing approaches can be used to forecast flow exceedance in a combined sewer for a wide range of ratios between the probability of detection (POD) and the probability of false detection (POFD). We use a hydrological rainfall-runoff model...

  14. Optimal offering and operating strategies for wind-storage systems with linear decision rules

    DEFF Research Database (Denmark)

    Ding, Huajie; Pinson, Pierre; Hu, Zechun

    2016-01-01

    The participation of wind farm-energy storage systems (WF-ESS) in electricity markets calls for an integrated view of day-ahead offering strategies and real-time operation policies. Such an integrated strategy is proposed here by co-optimizing offering at the day-ahead stage and operation policy...... to be used at the balancing stage. Linear decision rules are seen as a natural approach to model and optimize the real-time operation policy. These allow enhancing profits from balancing markets based on updated information on prices and wind power generation. Our integrated strategies for WF...

  15. Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid

    Directory of Open Access Journals (Sweden)

    Nadeem Javaid

    2017-10-01

    Full Text Available In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP technique and two heuristic optimization techniques: genetic algorithm (GA and binary particle swarm optimization (BPSO for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.

  16. An Optimal Charging Strategy for PV-Based Battery Swapping Stations in a DC Distribution System

    Directory of Open Access Journals (Sweden)

    Shengjun Wu

    2017-01-01

    Full Text Available Photovoltaic- (PV- based battery swapping stations (BSSs utilize a typical integration of consumable renewable resources to supply power for electric vehicles (EVs. The charging strategy of PV-based BSSs directly influences the availability, cost, and carbon emissions of the swapping service. This paper proposes an optimal charging strategy to improve the self-consumption of PV-generated power and service availability while considering forecast errors. First, we introduce the typical structure and operation model of PV-based BSSs. Second, three indexes are presented to evaluate operational performance. Then, a particle swarm optimization (PSO algorithm is developed to calculate the optimal charging power and to minimize the charging cost for each time slot. The proposed charging strategy helps decrease the impact of forecast uncertainties on the availability of the battery swapping service. Finally, a day-ahead operation schedule, a real-time decision-making strategy, and the proposed PSO charging strategy for PV-based BSSs are simulated in a case study. The simulation results show that the proposed strategy can effectively improve the self-consumption of PV-generated power and reduce charging cost.

  17. Agent-Based Optimization

    CERN Document Server

    Jędrzejowicz, Piotr; Kacprzyk, Janusz

    2013-01-01

    This volume presents a collection of original research works by leading specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of the agent-based optimization. The book consists of eight chapters covering examples of application of the multi-agent paradigm and respective customized tools to solve  difficult optimization problems arising in different areas such as machine learning, scheduling, transportation and, more generally, distributed and cooperative problem solving.

  18. A Game Theoretical Approach Based Bidding Strategy Optimization for Power Producers in Power Markets with Renewable Electricity

    Directory of Open Access Journals (Sweden)

    Yi Tang

    2017-05-01

    Full Text Available In a competitive electricity market with substantial involvement of renewable electricity, maximizing profits by optimizing bidding strategies is crucial to different power producers including conventional power plants and renewable ones. This paper proposes a game-theoretic bidding optimization method based on bi-level programming, where power producers are at the upper level and utility companies are at the lower level. The competition among the multiple power producers is formulated as a non-cooperative game in which bidding curves are their strategies, while uniform clearing pricing is considered for utility companies represented by an independent system operator. Consequently, based on the formulated game model, the bidding strategies for power producers are optimized for the day-ahead market and the intraday market with considering the properties of renewable energy; and the clearing pricing for the utility companies, with respect to the power quantity from different power producers, is optimized simultaneously. Furthermore, a distributed algorithm is provided to search the solution of the generalized Nash equilibrium. Finally, simulation results were performed and discussed to verify the feasibility and effectiveness of the proposed non-cooperative game-based bi-level optimization approach.

  19. 考虑网损修正的日前市场交易竞价模型%A Biding Model for Day-Ahead Market Transaction Considering Modification of Network Loss

    Institute of Scientific and Technical Information of China (English)

    张芳; 鲍海; 杨以涵; 杨秀媛

    2011-01-01

    对于发电侧开放的电力市场,如果忽略输电损耗对竞价上网的影响,采用发电报价直接竞价对发电商是不公平的.分析了电网功率的物理分布情况,给出了节点负荷和线路损耗与电源提供功率分量的解析表达式,通过功率分解求出了电源在供电路径上输送的功率及产生的损耗.以费用流守恒为原则,从电源节点开始计算电源在输电网络各节点的电价,构建了电源的发电报价网损修正模型.提出电源的报价应该以电厂为单位报价,而不应该以机组为单位报价,并用网损修正的电厂报价构造了日前市场交易模型.改进IEEE 14节点系统的算例分析结果验证了该模型的可行性和有效性.%It is unfair for gencos in an electricity market that is open at generation side and adopts direct bidding by generation bidding while the influence of transmission loss on bidding transaction. The physical distribution of power flow in power grid is analyzed and an analytical expression for the relation of nodal load and network loss to power component supplied by power sources is given. By means of power decomposition the transmitted power from power source along the supply path and its power loss in the supply path can be solved; following the principle of cost flow conservation and starting at power source node, the power prices at transmission nodes are calculated, then a generation bidding model considering the modification of network loss is built. It is proposed that the generation bidding should be based on power plant but not based on generation unit, and a day-ahead market transaction model is built by plant bidding considering the modification of network loss. The feasibility and effectiveness of the proposed model are verified by calculation results of improved IEEE 14-bus system.

  20. Particle Swarm Optimization Based Reactive Power Optimization

    CERN Document Server

    Sujin, P R; Linda, M Mary

    2010-01-01

    Reactive power plays an important role in supporting the real power transfer by maintaining voltage stability and system reliability. It is a critical element for a transmission operator to ensure the reliability of an electric system while minimizing the cost associated with it. The traditional objectives of reactive power dispatch are focused on the technical side of reactive support such as minimization of transmission losses. Reactive power cost compensation to a generator is based on the incurred cost of its reactive power contribution less the cost of its obligation to support the active power delivery. In this paper an efficient Particle Swarm Optimization (PSO) based reactive power optimization approach is presented. The optimal reactive power dispatch problem is a nonlinear optimization problem with several constraints. The objective of the proposed PSO is to minimize the total support cost from generators and reactive compensators. It is achieved by maintaining the whole system power loss as minimum...

  1. Risk Based Optimal Fatigue Testing

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Faber, M.H.; Kroon, I.B.

    1992-01-01

    Optimal fatigue life testing of materials is considered. Based on minimization of the total expected costs of a mechanical component a strategy is suggested to determine the optimal stress range levels for which additional experiments are to be performed together with an optimal value of the maxi......Optimal fatigue life testing of materials is considered. Based on minimization of the total expected costs of a mechanical component a strategy is suggested to determine the optimal stress range levels for which additional experiments are to be performed together with an optimal value...

  2. Study of the Effect of Time-Based Rate Demand Response Programs on Stochastic Day-Ahead Energy and Reserve Scheduling in Islanded Residential Microgrids

    DEFF Research Database (Denmark)

    Vahedipour-Dahraie, Mostafa; Najafi, Hamid Reza; Anvari-Moghaddam, Amjad

    2017-01-01

    In recent deregulated power systems, demand response (DR) has become one of the most cost-effective and efficient solutions for smoothing the load profile when the system is under stress. By participating in DR programs, customers are able to change their energy consumption habits in response...... in presence of renewable energy resources (RESs) and electric vehicles (EVs). An economic model of responsive load is also proposed on the basis of elasticity factor to model the behavior of customers participating in various DR programs. A two-stage stochastic programming model is developed accordingly...... to minimize the expected cost of MG under different TBR programs. To verify the effectiveness and applicability of the proposed approach, a number of simulations are performed under different scenarios using real data; and the impact of TBR-DR actions on energy and reserve scheduling are studied and compared...

  3. Particle swarm optimization based optimal bidding strategy in an ...

    African Journals Online (AJOL)

    user

    Particle swarm optimization based optimal bidding strategy in an open ... relaxation-based approach for strategic bidding in England-Wales pool type electricity market has ... presents the mathematical formulation of optimal bidding problem.

  4. Reliability-based optimization of engineering structures

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard

    2008-01-01

    The theoretical basis for reliability-based structural optimization within the framework of Bayesian statistical decision theory is briefly described. Reliability-based cost benefit problems are formulated and exemplitied with structural optimization. The basic reliability-based optimization prob...

  5. Displacement based multilevel structural optimization

    Science.gov (United States)

    Striz, Alfred G.

    1995-01-01

    Multidisciplinary design optimization (MDO) is expected to play a major role in the competitive transportation industries of tomorrow, i.e., in the design of aircraft and spacecraft, of high speed trains, boats, and automobiles. All of these vehicles require maximum performance at minimum weight to keep fuel consumption low and conserve resources. Here, MDO can deliver mathematically based design tools to create systems with optimum performance subject to the constraints of disciplines such as structures, aerodynamics, controls, etc. Although some applications of MDO are beginning to surface, the key to a widespread use of this technology lies in the improvement of its efficiency. This aspect is investigated here for the MDO subset of structural optimization, i.e., for the weight minimization of a given structure under size, strength, and displacement constraints. Specifically, finite element based multilevel optimization of structures (here, statically indeterminate trusses and beams for proof of concept) is performed. In the system level optimization, the design variables are the coefficients of assumed displacement functions, and the load unbalance resulting from the solution of the stiffness equations is minimized. Constraints are placed on the deflection amplitudes and the weight of the structure. In the subsystems level optimizations, the weight of each element is minimized under the action of stress constraints, with the cross sectional dimensions as design variables. This approach is expected to prove very efficient, especially for complex structures, since the design task is broken down into a large number of small and efficiently handled subtasks, each with only a small number of variables. This partitioning will also allow for the use of parallel computing, first, by sending the system and subsystems level computations to two different processors, ultimately, by performing all subsystems level optimizations in a massively parallel manner on separate

  6. Optimization-Based Layout Design

    Directory of Open Access Journals (Sweden)

    K. Abdel-Malek

    2005-01-01

    Full Text Available The layout problem is of importance to ergonomists, vehicle/cockpit packaging engineers, designers of manufacturing assembly lines, designers concerned with the placement of levers, knobs, controls, etc. in the reachable workspace of a human, and also to users of digital human modeling code, where digital prototyping has become a valuable tool. This paper proposes a hybrid optimization method (gradient-based optimization and simulated annealing to obtain the layout design. We implemented the proposed algorithm for a project at Oral-B Laboratories, where a manufacturing cell involves an operator who handles three objects, some with the left hand, others with the right hand.

  7. Simulation-based optimization parametric optimization techniques and reinforcement learning

    CERN Document Server

    Gosavi, Abhijit

    2003-01-01

    Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to converg...

  8. Optimized Local Trigonometric Bases with Nonuniform Partitions

    Institute of Scientific and Technical Information of China (English)

    Qiao Fang LIAN; Yong Ge WANG; Dun Yan YAN

    2006-01-01

    The authors provide optimized local trigonometric bases with nonuniform partitions which efficiently compress trigonometric functions. Numerical examples demonstrate that in many cases the proposed bases provide better compression than the optimized bases with uniform partitions obtained by Matviyenko.

  9. 基于储能水平控制的微电网能量优化调度%Energy optimal dispatch for microgrid based on state of charge control

    Institute of Scientific and Technical Information of China (English)

    牛焕娜; 罗希; 杨明皓

    2014-01-01

    With the study of renewable energy power generation for microgrid becoming more and more significant, the microgrid's safe and economic operation has been paid more and more attention to. The microgrid economic operation cannot go well without perfect energy optimization scheduling, and making full use of energy storage units, coordinate microgrid internal power supply, storage and load, and the power/energy flow between the microgrid and the large power grid. Maximizing the microgrid operation efficiency under the premise of keeping balance between the supply and demand has become an urgent problem needing solution.Aiming at the problems in current literature, the dynamic optimization and static optimization models can’t combine the day-ahead scheduling with real-time scheduling and fail to make full use of the storage and removal effect of energy storage units. In this paper, firstly, a multiple-time scale optimization scheduling scheme is put forward using the state of charge of energy storage unit as the bond between day-ahead scheduling and real-time scheduling. In day-ahead scheduling stage, a scheduling cycle is divided into 24 hours. In state of charge planning, the controllable micro power on-off plan and power generation plan are made based on the short power generation/consumption prediction data. In real-time scheduling stage, following the controllable micro power on-off planning and the state of charge of day-ahead scheduling, the future 5-15 minutes state of charge, charge and discharge power and the micro power supply power output planning is developed based on the real-time super short power generation/consumption prediction data. The day-ahead optimization model for the microgrid is also put forward, using the state of charge of energy storage units, controlled micro power supply power, and on-off state as control variables. The model puts four parts as constraint conditions within 24 hours, the controllable micro powers output, state of charge

  10. Lifecycle-Based Swarm Optimization Method for Numerical Optimization

    Directory of Open Access Journals (Sweden)

    Hai Shen

    2014-01-01

    Full Text Available Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO. Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques.

  11. Determination of the Prosumer's Optimal Bids

    Science.gov (United States)

    Ferruzzi, Gabriella; Rossi, Federico; Russo, Angela

    2015-12-01

    This paper considers a microgrid connected with a medium-voltage (MV) distribution network. It is assumed that the microgrid, which is managed by a prosumer, operates in a competitive environment and participates in the day-ahead market. Then, as the first step of the short-term management problem, the prosumer must determine the bids to be submitted to the market. The offer strategy is based on the application of an optimization model, which is solved for different hourly price profiles of energy exchanged with the main grid. The proposed procedure is applied to a microgrid and four different its configurations were analyzed. The configurations consider the presence of thermoelectric units that only produce electricity, a boiler or/and cogeneration power plants for the thermal loads, and an electric storage system. The numerical results confirmed the numerous theoretical considerations that have been made.

  12. Duality based contact shape optimization

    DEFF Research Database (Denmark)

    Vondrák, Vít; Dostal, Zdenek; Rasmussen, John

    2001-01-01

    An implementation of semi-analytic method for the sensitivity analysis in contact shape optimization without friction is described. This method is then applied to the contact shape optimization.......An implementation of semi-analytic method for the sensitivity analysis in contact shape optimization without friction is described. This method is then applied to the contact shape optimization....

  13. Multiobjective Optimization Based Vessel Collision Avoidance Strategy Optimization

    Directory of Open Access Journals (Sweden)

    Qingyang Xu

    2014-01-01

    Full Text Available The vessel collision accidents cause a great loss of lives and property. In order to reduce the human fault and greatly improve the safety of marine traffic, collision avoidance strategy optimization is proposed to achieve this. In the paper, a multiobjective optimization algorithm NSGA-II is adopted to search for the optimal collision avoidance strategy considering the safety as well as economy elements of collision avoidance. Ship domain and Arena are used to evaluate the collision risk in the simulation. Based on the optimization, an optimal rudder angle is recommended to navigator for collision avoidance. In the simulation example, a crossing encounter situation is simulated, and the NSGA-II searches for the optimal collision avoidance operation under the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS. The simulation studies exhibit the validity of the method.

  14. Drilling Path Optimization Based on Particle Swarm Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHU Guangyu; ZHANG Weibo; DU Yuexiang

    2006-01-01

    This paper presents a new approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem belonging to discrete space. Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence, based on the mathematical algorithm model, the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution. And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator, the improved operator can satisfy the need of integer coding in drilling path optimization. The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize, fast convergence speed, and better global convergence characteristics, hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes.

  15. Complex System Optimization Using Biogeography-Based Optimization

    Directory of Open Access Journals (Sweden)

    Dawei Du

    2013-01-01

    Full Text Available Complex systems are frequently found in modern industry. But with their multisubsystems, multiobjectives, and multiconstraints, the optimization of complex systems is extremely hard. In this paper, a new algorithm adapted from biogeography-based optimization (BBO is introduced for complex system optimization. BBO/Complex is the combination of BBO with a multiobjective ranking system, an innovative migration approach, and effective diversity control. Based on comparisons with three complex system optimization algorithms (multidisciplinary feasible (MDF, individual discipline feasible (IDF, and collaborative optimization (CO on four real-world benchmark problems, BBO/Complex demonstrates competitive performance. BBO/Complex provides the best performance in three of the benchmark problems and the second best in the fourth problem.

  16. Research of Day-ahead Environmental Protection Dispatch in Carbon Capture Systems Using a-superquantile Method%采用α超分位数方法的含碳捕集系统日前环保调度研究

    Institute of Scientific and Technical Information of China (English)

    范文帅; 张斌; 陈驾宇

    2012-01-01

    According to the safe operation in power system, the change of carbon capture power plant output caused by the flucamtion of carbon price is researched by cost index of the purchase of carbon emission fights. The relationship between carbon price and the level of cadxm capture is reflected by using price elasticity of demand theory in economics, α-superquantile theory is introduced to quantify the uncertain output of carbon capture power plant, Taking day-ahead energyg and environment-protective as objective, the model of daily environmental protection dispatch in cadxm capture power plant integrated system is conslructeck The example on IEEE30 system show that under different confidence levels and different αuantile constraints of carbon emissions, the proposed model can achieve optimal dispatching results in each period that emphasize on environment or economy.%从电力系统安全运行的角度出发,利用购买碳排放权成本指标,研究了碳价波动引起碳捕集电厂出力变化的问题。根据经济学的需求价格弹性原理来反映碳价与碳捕集水平之间的关系,并引入α-超分位数理论来度量碳捕集电厂出力的不确定性,以日前节能环保为目标,构建了碳捕集电厂的电力系统目前环保调度模型。IEEE30节点系统算例表明,采用α-超分位数约束的含碳捕集系统日前环保调度,可在不同置信水平和不同碳捕集电厂碳排放量的α-超分位数约束下,获取侧重环保性或经济性的系统各时段最优调度方案。

  17. The Optimized Operation of Gas Turbine Combined Heat and Power Units Oriented for the Grid-Connected Control

    Science.gov (United States)

    Xia, Shu; Ge, Xiaolin

    2016-04-01

    In this study, according to various grid-connected demands, the optimization scheduling models of Combined Heat and Power (CHP) units are established with three scheduling modes, which are tracking the total generation scheduling mode, tracking steady output scheduling mode and tracking peaking curve scheduling mode. In order to reduce the solution difficulty, based on the principles of modern algebraic integers, linearizing techniques are developed to handle complex nonlinear constrains of the variable conditions, and the optimized operation problem of CHP units is converted into a mixed-integer linear programming problem. Finally, with specific examples, the 96 points day ahead, heat and power supply plans of the systems are optimized. The results show that, the proposed models and methods can develop appropriate coordination heat and power optimization programs according to different grid-connected control.

  18. Reliability-based concurrent subspace optimization method

    Institute of Scientific and Technical Information of China (English)

    FAN Hui; LI Wei-ji

    2008-01-01

    To avoid the high computational cost and much modification in the process of applying traditional re-liability-based design optimization method, a new reliability-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing muhidisciplinary optimization techniques and reli-ability assessment methods. It is shown through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current de-terministic optimization process.

  19. Production Planning Based on BOM Optimization

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    According to a prototype enterprise, a rulebased Bill of Materials (BOM) structure is designed in order to get optimal design and management of product BOM. The constraint rules and optional objects for product data structure optimization are considered by associating customer demands with product BOM. Furthermore, the functional model of production planning system for assembling enterprise is given based on customization and BOM optimization.

  20. Interactive Reliability-Based Optimal Design

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Thoft-Christensen, Palle; Siemaszko, A.

    1994-01-01

    Interactive design/optimization of large, complex structural systems is considered. The objective function is assumed to model the expected costs. The constraints are reliability-based and/or related to deterministic code requirements. Solution of this optimization problem is divided in four main...... be used in interactive optimization....

  1. Reliability based design optimization: Formulations and methodologies

    Science.gov (United States)

    Agarwal, Harish

    Modern products ranging from simple components to complex systems should be designed to be optimal and reliable. The challenge of modern engineering is to ensure that manufacturing costs are reduced and design cycle times are minimized while achieving requirements for performance and reliability. If the market for the product is competitive, improved quality and reliability can generate very strong competitive advantages. Simulation based design plays an important role in designing almost any kind of automotive, aerospace, and consumer products under these competitive conditions. Single discipline simulations used for analysis are being coupled together to create complex coupled simulation tools. This investigation focuses on the development of efficient and robust methodologies for reliability based design optimization in a simulation based design environment. Original contributions of this research are the development of a novel efficient and robust unilevel methodology for reliability based design optimization, the development of an innovative decoupled reliability based design optimization methodology, the application of homotopy techniques in unilevel reliability based design optimization methodology, and the development of a new framework for reliability based design optimization under epistemic uncertainty. The unilevel methodology for reliability based design optimization is shown to be mathematically equivalent to the traditional nested formulation. Numerical test problems show that the unilevel methodology can reduce computational cost by at least 50% as compared to the nested approach. The decoupled reliability based design optimization methodology is an approximate technique to obtain consistent reliable designs at lesser computational expense. Test problems show that the methodology is computationally efficient compared to the nested approach. A framework for performing reliability based design optimization under epistemic uncertainty is also developed

  2. Optimal Dispatch Strategy of a Virtual Power Plant Containing Battery Switch Stations in a Unified Electricity Market

    Directory of Open Access Journals (Sweden)

    Hao Bai

    2015-03-01

    Full Text Available A virtual power plant takes advantage of interactive communication and energy management systems to optimize and coordinate the dispatch of distributed generation, interruptible loads, energy storage systems and battery switch stations, so as to integrate them as an entity to exchange energy with the power market. This paper studies the optimal dispatch strategy of a virtual power plant, based on a unified electricity market combining day-ahead trading with real-time trading. The operation models of interruptible loads, energy storage systems and battery switch stations are specifically described in the paper. The virtual power plant applies an optimal dispatch strategy to earn the maximal expected profit under some fluctuating parameters, including market price, retail price and load demand. The presented model is a nonlinear mixed-integer programming with inter-temporal constraints and is solved by the fruit fly algorithm.

  3. C-21 Fleet: Base Optimization

    Science.gov (United States)

    This research reveals the optimal use of the C -21 in support of Distinguished Visitor transport of the highest ranking military and civilians in our...a more effective use of time. This research project analyzed over 1000 flights on over 350 missions conducted in 2014. Eight C -21s are currently

  4. Reliability-Based Optimization in Structural Engineering

    DEFF Research Database (Denmark)

    Enevoldsen, I.; Sørensen, John Dalsgaard

    1994-01-01

    -based optimal inspection planning and reliability-based experiment planning. It is explained how these optimization problems can be solved by application of similar techniques. The reliability estimation is limited to first order reliability methods (FORM) for both component and systems reliability evaluation......, inclusion of the finite element method as the response evaluation tool and how the size of the problem can be made practicable. Finally, the important task of model evaluation and sensitivity analysis of the optimal solution is treated including a strategy for model-making with both pre and post-analysis.......In this paper reliability-based optimization problems in structural engineering are formulated on the basis of the classical decision theory. Several formulations are presented: Reliability-based optimal design of structural systems with component or systems reliability constraints, reliability...

  5. Classifiers based on optimal decision rules

    KAUST Repository

    Amin, Talha

    2013-11-25

    Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

  6. A new optimization algorithm based on chaos

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave's search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate.In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables optimization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.

  7. Rule-Based Optimization of Reversible Circuits

    CERN Document Server

    Arabzadeh, Mona; Zamani, Morteza Saheb

    2010-01-01

    Reversible logic has applications in various research areas including low-power design and quantum computation. In this paper, a rule-based optimization approach for reversible circuits is proposed which uses both negative and positive control Toffoli gates during the optimization. To this end, a set of rules for removing NOT gates and optimizing sub-circuits with common-target gates are proposed. To evaluate the proposed approach, the best-reported synthesized circuits and the results of a recent synthesis algorithm which uses both negative and positive controls are used. Our experiments reveal the potential of the proposed approach in optimizing synthesized circuits.

  8. Algorithmic Differentiation for Calculus-based Optimization

    Science.gov (United States)

    Walther, Andrea

    2010-10-01

    For numerous applications, the computation and provision of exact derivative information plays an important role for optimizing the considered system but quite often also for its simulation. This presentation introduces the technique of Algorithmic Differentiation (AD), a method to compute derivatives of arbitrary order within working precision. Quite often an additional structure exploitation is indispensable for a successful coupling of these derivatives with state-of-the-art optimization algorithms. The talk will discuss two important situations where the problem-inherent structure allows a calculus-based optimization. Examples from aerodynamics and nano optics illustrate these advanced optimization approaches.

  9. Optimization of multi-constrained structures based on optimality criteria

    Science.gov (United States)

    Rizzi, P.

    1976-01-01

    A weight-reduction algorithm is developed for the optimal design of structures subject to several multibehavioral inequality constraints. The structural weight is considered to depend linearly on the design variables. The algorithm incorporates a simple recursion formula derived from the Kuhn-Tucker necessary conditions for optimality, associated with a procedure to delete nonactive constraints based on the Gauss-Seidel iterative method for linear systems. A number of example problems is studied, including typical truss structures and simplified wings subject to static loads and with constraints imposed on stresses and displacements. For one of the latter structures, constraints on the fundamental natural frequency and flutter speed are also imposed. The results obtained show that the method is fast, efficient, and general when compared to other competing techniques. Extensions to the generality of the method to include equality constraints and nonlinear merit functions is discussed.

  10. Optimal Hops-Based Adaptive Clustering Algorithm

    Science.gov (United States)

    Xuan, Xin; Chen, Jian; Zhen, Shanshan; Kuo, Yonghong

    This paper proposes an optimal hops-based adaptive clustering algorithm (OHACA). The algorithm sets an energy selection threshold before the cluster forms so that the nodes with less energy are more likely to go to sleep immediately. In setup phase, OHACA introduces an adaptive mechanism to adjust cluster head and load balance. And the optimal distance theory is applied to discover the practical optimal routing path to minimize the total energy for transmission. Simulation results show that OHACA prolongs the life of network, improves utilizing rate and transmits more data because of energy balance.

  11. Reliability-Based Topology Optimization With Uncertainties

    Energy Technology Data Exchange (ETDEWEB)

    Bae, Kyoungryun [JAEIK Information and Communication Co. Ltd., Seoul (Korea, Republic of); Wang, Semyung [Kwangju Institute of Science and Technology, Kwangju (Korea, Republic of); Choi, Kyung K. [Univ. of Iowa, Iowa (United States)

    2002-11-15

    A probabilistic optimal design modeled with finite elements is presented. A 2-D finite element model is constructed for topology optimization. Young's modulus, thickness and loading are considered as uncertain variables. The uncertain variable means that the variable has a variance on a certain point. In order to compute reliability constraints, two methods-RIA, PMA-are widely used. To find reliability index easily, the limit state function is linearly approximated at the each iteration. This approximation method is called as the first order reliability method (FORM), which is widely used in reliability based design optimizations (RBDO)

  12. Optimization for manufacturing system based on Pheromone

    Directory of Open Access Journals (Sweden)

    Lei Wang

    2011-06-01

    Full Text Available A new optimization approach, called pheromone, which comes from the collective behavior of ant colonies for food foraging is proposed to optimize task allocation. These ants spread pheromone information and make global information available locally; thus, an ant agent only needs to observe its local environment in order to account for nonlocal concerns in its decisions. This approach has the capacity for task allocation model to automatically find efficient routing paths for processing orders and to reduce communication overhead, which exists in contract net protocol, in shop floor control system. An example confirms that a pheromone-based optimization approach has an excellent allocation performance in shop floor.

  13. Parameter Optimization Based on GA and HFSS

    Institute of Scientific and Technical Information of China (English)

    SUN Shu-hui; WANG Bing-zhong

    2005-01-01

    A new project based on genetic algorithm (GA) and high frequency simulation software (HFSS) is proposed to optimize microwave passive devices effectively. This project is realized with a general program named as optimization program. The program is compiled by Matlab and the macro language of HFSS which is a fast and effective way to accomplish tasks. In the paper, two examples are used to show the project's feasibility.

  14. Inspection-Repair based Availability Optimization of Distribution Systems using Teaching Learning based Optimization

    Science.gov (United States)

    Tiwary, Aditya; Arya, L. D.; Arya, Rajesh; Choube, S. C.

    2016-09-01

    This paper describes a technique for optimizing inspection and repair based availability of distribution systems. Optimum duration between two inspections has been obtained for each feeder section with respect to cost function and subject to satisfaction of availability at each load point. Teaching learning based optimization has been used for availability optimization. The developed algorithm has been implemented on radial and meshed distribution systems. The result obtained has been compared with those obtained with differential evolution.

  15. Optimal pricing decision model based on activity-based costing

    Institute of Scientific and Technical Information of China (English)

    王福胜; 常庆芳

    2003-01-01

    In order to find out the applicability of the optimal pricing decision model based on conventional costbehavior model after activity-based costing has given strong shock to the conventional cost behavior model andits assumptions, detailed analyses have been made using the activity-based cost behavior and cost-volume-profitanalysis model, and it is concluded from these analyses that the theory behind the construction of optimal pri-cing decision model is still tenable under activity-based costing, but the conventional optimal pricing decisionmodel must be modified as appropriate to the activity-based costing based cost behavior model and cost-volume-profit analysis model, and an optimal pricing decision model is really a product pricing decision model construc-ted by following the economic principle of maximizing profit.

  16. A modelling breakthrough for market design analysis to test massive intermittent generation integration in markets results of selected OPTIMATE studies

    DEFF Research Database (Denmark)

    Beaude, Francois; Atayi, A.; Bourmaud, J.-Y.

    2013-01-01

    The OPTIMATE1 platform focuses on electricity system and market designs modelling in order to assess current and innovative designs in Europe. The current paper describes the results of the first validation studies' conducted with the tool. These studies deal with day-ahead market rules, load fle...

  17. Reliability-Based Optimization of Wind Turbines

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Tarp-Johansen, N.J.

    2004-01-01

    Reliability-based optimization of the main tower and monopile foundation of an offshore wind turbine is considered. Different formulations are considered of the objective function including benefits and building and failure costs of the wind turbine. Also different reconstruction policies in case...

  18. Reliability Based Optimization of Fire Protection

    DEFF Research Database (Denmark)

    Thoft-Christensen, Palle

    fire protection (PFP) of firewalls and structural members. The paper is partly based on research performed within the EU supported research project B/E-4359 "Optimized Fire Safety of Offshore Structures" and partly on research supported by the Danish Technical Research Council (see Thoft-Christensen [1...

  19. Optimization-Based Wearable Tactile Rendering.

    Science.gov (United States)

    Perez, Alvaro G; Lobo, Daniel; Chinello, Francesco; Cirio, Gabriel; Malvezzi, Monica; San Martin, Jose; Prattichizzo, Domenico; Otaduy, Miguel A

    2016-10-20

    Novel wearable tactile interfaces offer the possibility to simulate tactile interactions with virtual environments directly on our skin. But, unlike kinesthetic interfaces, for which haptic rendering is a well explored problem, they pose new questions about the formulation of the rendering problem. In this work, we propose a formulation of tactile rendering as an optimization problem, which is general for a large family of tactile interfaces. Based on an accurate simulation of contact between a finger model and the virtual environment, we pose tactile rendering as the optimization of the device configuration, such that the contact surface between the device and the actual finger matches as close as possible the contact surface in the virtual environment. We describe the optimization formulation in general terms, and we also demonstrate its implementation on a thimble-like wearable device. We validate the tactile rendering formulation by analyzing its force error, and we show that it outperforms other approaches.

  20. RPOA Model-Based Optimal Resource Provisioning

    Directory of Open Access Journals (Sweden)

    Noha El. Attar

    2014-01-01

    Full Text Available Optimal utilization of resources is the core of the provisioning process in the cloud computing. Sometimes the local resources of a data center are not adequate to satisfy the users’ requirements. So, the providers need to create several data centers at different geographical area around the world and spread the users’ applications on these resources to satisfy both service providers and customers QoS requirements. By considering the expansion of the resources and applications, the transmission cost and time have to be concerned as significant factors in the allocation process. According to the work of our previous paper, a Resource Provision Optimal Algorithm (RPOA based on Particle Swarm Optimization (PSO has been introduced to find the near optimal resource utilization with considering the customer budget and suitable for deadline time. This paper is considered an enhancement to RPOA algorithm to find the near optimal resource utilization with considering the data transfer time and cost, in addition to the customer budget and deadline time, in the performance measurement.

  1. Isotretinoin Oil-Based Capsule Formulation Optimization

    Directory of Open Access Journals (Sweden)

    Pi-Ju Tsai

    2013-01-01

    Full Text Available The purpose of this study was to develop and optimize an isotretinoin oil-based capsule with specific dissolution pattern. A three-factor-constrained mixture design was used to prepare the systemic model formulations. The independent factors were the components of oil-based capsule including beeswax (X1, hydrogenated coconut oil (X2, and soybean oil (X3. The drug release percentages at 10, 30, 60, and 90 min were selected as responses. The effect of formulation factors including that on responses was inspected by using response surface methodology (RSM. Multiple-response optimization was performed to search for the appropriate formulation with specific release pattern. It was found that the interaction effect of these formulation factors (X1X2, X1X3, and X2X3 showed more potential influence than that of the main factors (X1, X2, and X3. An optimal predicted formulation with Y10 min, Y30 min, Y60 min, and Y90 min release values of 12.3%, 36.7%, 73.6%, and 92.7% at X1, X2, and X3 of 5.75, 15.37, and 78.88, respectively, was developed. The new formulation was prepared and performed by the dissolution test. The similarity factor f2 was 54.8, indicating that the dissolution pattern of the new optimized formulation showed equivalence to the predicted profile.

  2. Under-Exposed Image Enhancement Based on Relaxed Luminance Optimization

    National Research Council Canada - National Science Library

    Chunxiao Liu; Feng Yang

    2013-01-01

    ... optimization based under-exposed image clearness enhancement algorithm, which treats it as the simultaneous augmentation of luminance and contrast, and combines them in an optimization framework under...

  3. Optimization-based controller design for rotorcraft

    Science.gov (United States)

    Tsing, N.-K.; Fan, M. K. H.; Barlow, J.; Tits, A. L.; Tischler, M. B.

    1993-01-01

    An optimization-based methodology for linear control system design is outlined by considering the design of a controller for a UH-60 rotorcraft in hover. A wide range of design specifications is taken into account: internal stability, decoupling between longitudinal and lateral motions, handling qualities, and rejection of windgusts. These specifications are investigated while taking into account physical limitations in the swashplate displacements and rates of displacement. The methodology crucially relies on user-machine interaction for tradeoff exploration.

  4. Optimal interference code based on machine learning

    Science.gov (United States)

    Qian, Ye; Chen, Qian; Hu, Xiaobo; Cao, Ercong; Qian, Weixian; Gu, Guohua

    2016-10-01

    In this paper, we analyze the characteristics of pseudo-random code, by the case of m sequence. Depending on the description of coding theory, we introduce the jamming methods. We simulate the interference effect or probability model by the means of MATLAB to consolidate. In accordance with the length of decoding time the adversary spends, we find out the optimal formula and optimal coefficients based on machine learning, then we get the new optimal interference code. First, when it comes to the phase of recognition, this study judges the effect of interference by the way of simulating the length of time over the decoding period of laser seeker. Then, we use laser active deception jamming simulate interference process in the tracking phase in the next block. In this study we choose the method of laser active deception jamming. In order to improve the performance of the interference, this paper simulates the model by MATLAB software. We find out the least number of pulse intervals which must be received, then we can make the conclusion that the precise interval number of the laser pointer for m sequence encoding. In order to find the shortest space, we make the choice of the greatest common divisor method. Then, combining with the coding regularity that has been found before, we restore pulse interval of pseudo-random code, which has been already received. Finally, we can control the time period of laser interference, get the optimal interference code, and also increase the probability of interference as well.

  5. Real-time energy optimal dispatch for microgrid based onday-ahead scheduling of charge state%基于储能Soc日前计划的微电网实时能量优化调度方法

    Institute of Scientific and Technical Information of China (English)

    孟晓丽; 牛焕娜; 贾东梨; 张晓雪; 罗希; 杨明皓

    2016-01-01

    针对微电网实时优化调度计算的工程需要,提出了一种遵循储能 Soc 日前计划的基于网流模型的微电网实时能量优化调度方法。该方法以尽量遵循储能 Soc 日前计划为前提,首先建立以“等效供电成本”最小为目标函数,以分布式电源出力、储能单元储能水平以及微网与主网交互功率均在限值之内和微网内功率平衡为约束条件的实时优化数学模型;随后提出将该非线性优化模型转换为最小费用最大流网流模型进行线性化求解的方法。算例表明,遵循储能 Soc 日前计划的实时调度计划能够对上级电网起到削峰填谷的作用,无论在并网运行还是孤岛运行模式下能够有效降低微电网供电成本,日供电成本降低达30%以上,该方法能够足实时优化调度计算的工程要求。%Recently, as an effective method of using distributed power generation, microgrid has been developed rapidly. The microgrid's safe and economic operation has been paid more and more attentions. Microgrid economic operation cannot goes well without perfect energy optimization scheduling. Microgrid energy optimization scheduling is based on the distributed power generation forecasting and load forecasting, aiming at making full use of energy storage unit, coordinating microgrid internal power supply and the power/energy flow between microgrid and large power grid and maximizing the microgrid economic benefits. In current literature, the dynamic optimization model and static optimization model can’t combine the day-ahead scheduling with real-time scheduling and fail to make full use of the storage and removal effect of energy storage unit. In order to meet the project needs for real-time optimal dispatch of microgrid, in this paper, we proposed a real-time optimal dispatch model for microgrid based on the state of charge of day-ahead scheduling, and the network flow model. In this method, based on the real

  6. Heat exchanger design based on economic optimization

    Energy Technology Data Exchange (ETDEWEB)

    Caputo, Antonio C.; Pelagagge, Marcello P.; Salini, Paolo [University of l' Aquila (Italy). Faculty of Engineering], e-mail: caputo@ing.inivaq.it, e-mail: pelmar@ing.inivaq.it, e-mail: salini@ing.inivaq.it

    2006-07-01

    Owing to the wide utilization of heat exchangers in industrial processes their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which assume a configuration and gradually change design parameters until a satisfying solution is reached which meets the design specifications. However, such methods, besides being time consuming, do not guarantee the reach of an optimal solution. In this paper a procedure for optimal design for shell and tube heat exchangers is proposed which utilizes a genetic algorithm to minimize the total discounted cost of the equipment including the capital investment and pumping related annual energy expenditures. In order to verify the performances of the proposed method four case studies are also presented showing that total cost reductions greater than 15% are feasible respect traditionally designed exchangers. (author)

  7. Grouping Optimization Based on Social Relationships

    Directory of Open Access Journals (Sweden)

    Rong-Chang Chen

    2012-01-01

    Full Text Available Grouping based on social relationships is a complex problem since the social relationships within a group usually form a complicated network. To solve the problem, a novel approach which uses a combined sociometry and genetic algorithm (CSGA is presented. A new nonlinear relation model derived from the sociometry is established to measure the social relationships, which are then used as the basis in genetic algorithm (GA program to optimize the grouping. To evaluate the effectiveness of the proposed approach, three real datasets collected from a famous college in Taiwan were utilized. Experimental results show that CSGA optimizes the grouping effectively and efficiently and students are very satisfied with the grouping results, feel the proposed approach interesting, and show a high repeat intention of using it. In addition, a paired sample t-test shows that the overall satisfaction on the proposed CSGA approach is significantly higher than the random method.

  8. Model based optimization of EMC input filters

    Energy Technology Data Exchange (ETDEWEB)

    Raggl, K; Kolar, J. W. [Swiss Federal Institute of Technology, Power Electronic Systems Laboratory, Zuerich (Switzerland); Nussbaumer, T. [Levitronix GmbH, Zuerich (Switzerland)

    2008-07-01

    Input filters of power converters for compliance with regulatory electromagnetic compatibility (EMC) standards are often over-dimensioned in practice due to a non-optimal selection of number of filter stages and/or the lack of solid volumetric models of the inductor cores. This paper presents a systematic filter design approach based on a specific filter attenuation requirement and volumetric component parameters. It is shown that a minimal volume can be found for a certain optimal number of filter stages for both the differential mode (DM) and common mode (CM) filter. The considerations are carried out exemplarily for an EMC input filter of a single phase power converter for the power levels of 100 W, 300 W, and 500 W. (author)

  9. Economic and optimal dispatching of power microgrid with renweable energy resources based on stochastic optimization%考虑随机性的微网能量优化调度模型

    Institute of Scientific and Technical Information of China (English)

    柳丹; 李强; 袁晓冬

    2014-01-01

    This paper proposes a stochastic optimization model considering the volatility of wind power and photovoltaic power in microgrid. The model optimizes the economic operation of a microgrid as well as minimizes the flow deviation at the point of common coupling (PCC) from scheduled values. First, the Latin hypercube sampling (LHS) and simultaneous backward reduction (SBR) technique are introduced to describe the stochastic nature of wind power and photovoltaic power. Then the random characteristic is introduced into the objective function of the stochastic model which is solved based on the genetic algorithm (GA). Simulation results demonstrate its rationality and effectiveness for day-ahead scheduling of a microgrid.%针对微网中风能和太阳能等可再生能源具有随机性和波动性的特点,提出了一种考虑随机性的微网能量优化调度模型,用以降低可再生能源发电预测不确定性带来的微电网和主网连接点(Point of common coupling,PCC)处的功率波动,使得微网对于主网成为一个可调度的单元,同时实现微网的经济调度。首先,采用拉丁超立方采样法(Latin hypercube sampling,LHS)和同步回带削减(Simultaneous backward reduction,SBR)技术生成可能的出力场景来描述风电和太阳能光伏发电出力的随机性。然后将PCC处能量波动引入目标函数,使得在系统期望成本最小的同时减小风电和光伏出力波动性对电网的影响,采用遗传算法求解该问题。仿真结果表明,该模型对含风电和太阳能光伏发电的微网优化调度的合理性和有效性。

  10. PRODUCT OPTIMIZATION METHOD BASED ON ANALYSIS OF OPTIMAL VALUES OF THEIR CHARACTERISTICS

    Directory of Open Access Journals (Sweden)

    Constantin D. STANESCU

    2016-05-01

    Full Text Available The paper presents an original method of optimizing products based on the analysis of optimal values of their characteristics . Optimization method comprises statistical model and analytical model . With this original method can easily and quickly obtain optimal product or material .

  11. Optimal Operation of Energy Storage in Power Transmission and Distribution

    OpenAIRE

    2015-01-01

    In this thesis, we investigate optimal operation of energy storage units in power transmission and distribution grids. At transmission level, we investigate the problem where an investor-owned independently-operated energy storage system seeks to offer energy and ancillary services in the day-ahead and real-time markets. We specifically consider the case where a significant portion of the power generated in the grid is from renewable energy resources and there exists significant uncertainty i...

  12. Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

    Energy Technology Data Exchange (ETDEWEB)

    Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA

    2016-10-01

    The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

  13. Robust optimization based upon statistical theory.

    Science.gov (United States)

    Sobotta, B; Söhn, M; Alber, M

    2010-08-01

    Organ movement is still the biggest challenge in cancer treatment despite advances in online imaging. Due to the resulting geometric uncertainties, the delivered dose cannot be predicted precisely at treatment planning time. Consequently, all associated dose metrics (e.g., EUD and maxDose) are random variables with a patient-specific probability distribution. The method that the authors propose makes these distributions the basis of the optimization and evaluation process. The authors start from a model of motion derived from patient-specific imaging. On a multitude of geometry instances sampled from this model, a dose metric is evaluated. The resulting pdf of this dose metric is termed outcome distribution. The approach optimizes the shape of the outcome distribution based on its mean and variance. This is in contrast to the conventional optimization of a nominal value (e.g., PTV EUD) computed on a single geometry instance. The mean and variance allow for an estimate of the expected treatment outcome along with the residual uncertainty. Besides being applicable to the target, the proposed method also seamlessly includes the organs at risk (OARs). The likelihood that a given value of a metric is reached in the treatment is predicted quantitatively. This information reveals potential hazards that may occur during the course of the treatment, thus helping the expert to find the right balance between the risk of insufficient normal tissue sparing and the risk of insufficient tumor control. By feeding this information to the optimizer, outcome distributions can be obtained where the probability of exceeding a given OAR maximum and that of falling short of a given target goal can be minimized simultaneously. The method is applicable to any source of residual motion uncertainty in treatment delivery. Any model that quantifies organ movement and deformation in terms of probability distributions can be used as basis for the algorithm. Thus, it can generate dose

  14. GPU-based ultrafast IMRT plan optimization

    Science.gov (United States)

    Men, Chunhua; Gu, Xuejun; Choi, Dongju; Majumdar, Amitava; Zheng, Ziyi; Mueller, Klaus; Jiang, Steve B.

    2009-11-01

    The widespread adoption of on-board volumetric imaging in cancer radiotherapy has stimulated research efforts to develop online adaptive radiotherapy techniques to handle the inter-fraction variation of the patient's geometry. Such efforts face major technical challenges to perform treatment planning in real time. To overcome this challenge, we are developing a supercomputing online re-planning environment (SCORE) at the University of California, San Diego (UCSD). As part of the SCORE project, this paper presents our work on the implementation of an intensity-modulated radiation therapy (IMRT) optimization algorithm on graphics processing units (GPUs). We adopt a penalty-based quadratic optimization model, which is solved by using a gradient projection method with Armijo's line search rule. Our optimization algorithm has been implemented in CUDA for parallel GPU computing as well as in C for serial CPU computing for comparison purpose. A prostate IMRT case with various beamlet and voxel sizes was used to evaluate our implementation. On an NVIDIA Tesla C1060 GPU card, we have achieved speedup factors of 20-40 without losing accuracy, compared to the results from an Intel Xeon 2.27 GHz CPU. For a specific nine-field prostate IMRT case with 5 × 5 mm2 beamlet size and 2.5 × 2.5 × 2.5 mm3 voxel size, our GPU implementation takes only 2.8 s to generate an optimal IMRT plan. Our work has therefore solved a major problem in developing online re-planning technologies for adaptive radiotherapy.

  15. Location based Network Optimizations for Mobile Wireless Networks

    DEFF Research Database (Denmark)

    Nielsen, Jimmy Jessen

    selection in Wi-Fi networks and predictive handover optimization in heterogeneous wireless networks. The investigations in this work have indicated that location based network optimizations are beneficial compared to typical link measurement based approaches. Especially the knowledge of geographical...

  16. Optimizing mesenchymal stem cell-based therapeutics.

    Science.gov (United States)

    Wagner, Joseph; Kean, Thomas; Young, Randell; Dennis, James E; Caplan, Arnold I

    2009-10-01

    Mesenchymal stem cell (MSC)-based therapeutics are showing significant benefit in multiple clinical trials conducted by both academic and commercial organizations, but obstacles remain for their large-scale commercial implementation. Recent studies have attempted to optimize MSC-based therapeutics by either enhancing their potency or increasing their delivery to target tissues. Overexpression of trophic factors or in vitro exposure to potency-enhancing factors are two approaches that are demonstrating success in preclinical animal models. Delivery enhancement strategies involving tissue-specific cytokine pathways or binding sites are also showing promise. Each of these strategies has its own set of distinct advantages and disadvantages when viewed with a mindset of ultimate commercialization and clinical utility.

  17. Function Optimization Based on Quantum Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Ying Sun

    2014-01-01

    Full Text Available Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained. The method of encoding and decoding of chromosome is first described before a new adaptive selection scheme for angle parameters used for rotation gate is put forward based on the core ideas and principles of quantum computation. Eight typical functions are selected to optimize to evaluate the effectiveness and performance of vbQGA against standard Genetic Algorithm (sGA and Genetic Quantum Algorithm (GQA. The simulation results show that vbQGA is significantly superior to sGA in all aspects and outperforms GQA in robustness and solving velocity, especially for multidimensional and complicated functions.

  18. The Power of Weather: Some Empirical Evidence on Predicting Day-ahead Power Prices through Day-ahead Weather Forecasts

    NARCIS (Netherlands)

    F. Ravazzolo (Francesco); C. Zhou (Chen); C. Huurman

    2007-01-01

    textabstractIn the literature the effects of weather on electricity sales are well-documented. However, studies that have investigated the impact of weather on electricity prices are still scarce (e.g. Knittel and Roberts, 2005), partly because the wholesale power markets have only recently been

  19. The Power of Weather: Some Empirical Evidence on Predicting Day-ahead Power Prices through Day-ahead Weather Forecasts

    NARCIS (Netherlands)

    F. Ravazzolo (Francesco); C. Zhou (Chen); C. Huurman

    2007-01-01

    textabstractIn the literature the effects of weather on electricity sales are well-documented. However, studies that have investigated the impact of weather on electricity prices are still scarce (e.g. Knittel and Roberts, 2005), partly because the wholesale power markets have only recently been der

  20. Powernext Day-Ahead. Powernext Futures. Activity report - 2004; Powernext Day-Ahead. Powernext Futures. Bilan statistique 2004

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2004-07-01

    Powernext SA is a Multilateral Trading Facility which organizes and warrants the transactions on the European power exchange market. This activity report presents the highlights of the market and of Powernext in 2004: market conditions (more reasonable and less volatile prices, steadier market conditions (climate conditions, power consumption, correlation between French and German prices), increasing liquidity, start-up of Powernext Futures{sup TM} for medium-term contracts and introduction of futures price curve, promising volumes to start, and liquidity of the futures market. (J.S)

  1. Reliability-Based Optimization of Structural Elements

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard

    In this paper structural elements from an optimization point of view are considered, i.e. only the geometry of a structural element is optimized. Reliability modelling of the structural element is discussed both from an element point of view and from a system point of view. The optimization...

  2. GA based CNC turning center exploitation process parameters optimization

    Directory of Open Access Journals (Sweden)

    Z. Car

    2009-01-01

    Full Text Available This paper presents machining parameters (turning process optimization based on the use of artificial intelligence. To obtain greater efficiency and productivity of the machine tool, optimal cutting parameters have to be obtained. In order to find optimal cutting parameters, the genetic algorithm (GA has been used as an optimal solution finder. Optimization has to yield minimum machining time and minimum production cost, while considering technological and material constrains.

  3. A Q-Learning-Based Supplier Bidding Strategy in Electricity Auction Market

    Science.gov (United States)

    Xiong, Gaofeng; Hashiyama, Tomonori; Okuma, Shigeru

    One of the most important issues for power suppliers in the deregulated electric industry is how to bid into the electricity auction market to satisfy their profit-maximizing goals. Based on the Q-Learning algorithm, this paper presents a novel supplier bidding strategy to maximize supplier’s profit in the long run. In this approach, the supplier bidding strategy is viewed as one kind of stochastic optimal control problem and each supplier can learn from experience. A competitive day-ahead electricity auction market with hourly bids is assumed here, where no supplier possesses the market power and all suppliers winning the market are paid based on their own bid prices. The dynamics and the incomplete information of the market are considered. The impact of suppliers’ strategic bidding on the market price is analyzed. Agent-based simulations are presented. The simulation results show the feasibility of the proposed bidding strategy.

  4. Numerical design optimization of compressor blade based on ADOP

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    An aerodynamic design optimization platform (ADOP) has been developed. The numerical optimization method is based on genetic algorithm (GA), Pareto ranking and fitness sharing technique. The platform was used for design optimization of the stator of an advanced transonic stage to seek high adiabatic efficiency. The compressor stage efficiency is increased by 0.502% at optimal point and the stall margin is enlarged by nearly 1.0% at design rotating speed. The flow fields of the transonic stage were simulated with FINE/Turbo software package. The optimization result indicates that the optimization platform is effective in 3D numerical design optimization problems.

  5. An optimization method for metamorphic mechanisms based on multidisciplinary design optimization

    Directory of Open Access Journals (Sweden)

    Zhang Wuxiang

    2014-12-01

    Full Text Available The optimization of metamorphic mechanisms is different from that of the conventional mechanisms for its characteristics of multi-configuration. There exist complex coupled design variables and constraints in its multiple different configuration optimization models. To achieve the compatible optimized results of these coupled design variables, an optimization method for metamorphic mechanisms is developed in the paper based on the principle of multidisciplinary design optimization (MDO. Firstly, the optimization characteristics of the metamorphic mechanism are summarized distinctly by proposing the classification of design variables and constraints as well as coupling interactions among its different configuration optimization models. Further, collaborative optimization technique which is used in MDO is adopted for achieving the overall optimization performance. The whole optimization process is then proposed by constructing a two-level hierarchical scheme with global optimizer and configuration optimizer loops. The method is demonstrated by optimizing a planar five-bar metamorphic mechanism which has two configurations, and results show that it can achieve coordinated optimization results for the same parameters in different configuration optimization models.

  6. An optimization method for metamorphic mechanisms based on multidisciplinary design optimization

    Institute of Scientific and Technical Information of China (English)

    Zhang Wuxiang; Wu Teng; Ding Xilun

    2014-01-01

    The optimization of metamorphic mechanisms is different from that of the conventional mechanisms for its characteristics of multi-configuration. There exist complex coupled design vari-ables and constraints in its multiple different configuration optimization models. To achieve the compatible optimized results of these coupled design variables, an optimization method for meta-morphic mechanisms is developed in the paper based on the principle of multidisciplinary design optimization (MDO). Firstly, the optimization characteristics of the metamorphic mechanism are summarized distinctly by proposing the classification of design variables and constraints as well as coupling interactions among its different configuration optimization models. Further, collabora-tive optimization technique which is used in MDO is adopted for achieving the overall optimization performance. The whole optimization process is then proposed by constructing a two-level hierar-chical scheme with global optimizer and configuration optimizer loops. The method is demon-strated by optimizing a planar five-bar metamorphic mechanism which has two configurations, and results show that it can achieve coordinated optimization results for the same parameters in different configuration optimization models.

  7. Logic-based methods for optimization combining optimization and constraint satisfaction

    CERN Document Server

    Hooker, John

    2011-01-01

    A pioneering look at the fundamental role of logic in optimization and constraint satisfaction While recent efforts to combine optimization and constraint satisfaction have received considerable attention, little has been said about using logic in optimization as the key to unifying the two fields. Logic-Based Methods for Optimization develops for the first time a comprehensive conceptual framework for integrating optimization and constraint satisfaction, then goes a step further and shows how extending logical inference to optimization allows for more powerful as well as flexible

  8. Optimized Grid Based e-Learning Framework

    Directory of Open Access Journals (Sweden)

    Suresh Jaganathan

    2014-12-01

    Full Text Available E-Learning is the process of extending the resources to different locations by using multimedia communications. Many e-Learning methodologies are available and based on client-server, peer-to-peer and using Grid Computing concepts. To establish e-Learning process, systems should satisfy these needs, i high storage for storing, ii high network throughput for faster transfer and iii efficient streaming of materials. The first and second needs are satisfied by using Grid and P2P technologies and the third need can be achieved by an efficient video compression algorithm. This study proposes a framework, called Optimized Grid Based e-Learning (OgBeL , which adopts both Grid and P2P technology. To reduce the e-Learning material size for efficient streaming, a light weight compression algorithm called (dWave is embedded in (OgBeL . The behavior of framework is analyzed in terms of time taken to transfer files using in-use grid protocols and in networks combined with grid and P2P.

  9. Fuzzy entropy image segmentation based on particle Swarm optimization

    Institute of Scientific and Technical Information of China (English)

    Linyi Li; Deren Li

    2008-01-01

    Partide swaFnl optimization is a stochastic global optimization algorithm that is based on swarm intelligence.Because of its excellent performance,particle swarm optimization is introduced into fuzzy entropy image segmentation to select the optimal fuzzy parameter combination and fuzzy threshold adaptively.In this study,the particles in the swarm are constructed and the swarm search strategy is proposed to meet the needs of the segmentation application.Then fuzzy entropy image segmentation based on particle swarm opti-mization is implemented and the proposed method obtains satisfactory results in the segmentation experiments.Compared with the exhaustive search method,particle swarm optimization can give the salne optimal fuzzy parameter combination and fuzzy threshold while needing less search time in the segmentation experiments and also has good search stability in the repeated experiments.Therefore,fuzzy entropy image segmentation based on particle swarm optimization is an efficient and promising segmentation method.

  10. An Optimization Model Based on Game Theory

    Directory of Open Access Journals (Sweden)

    Yang Shi

    2014-04-01

    Full Text Available Game Theory has a wide range of applications in department of economics, but in the field of computer science, especially in the optimization algorithm is seldom used. In this paper, we integrate thinking of game theory into optimization algorithm, and then propose a new optimization model which can be widely used in optimization processing. This optimization model is divided into two types, which are called “the complete consistency” and “the partial consistency”. In these two types, the partial consistency is added disturbance strategy on the basis of the complete consistency. When model’s consistency is satisfied, the Nash equilibrium of the optimization model is global optimal and when the model’s consistency is not met, the presence of perturbation strategy can improve the application of the algorithm. The basic experiments suggest that this optimization model has broad applicability and better performance, and gives a new idea for some intractable problems in the field of artificial intelligence

  11. Warehouse Optimization Model Based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Guofeng Qin

    2013-01-01

    Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.

  12. Topology Optimization of Metamaterial-Based Electrically Small Antennas

    DEFF Research Database (Denmark)

    Erentok, Aycan; Sigmund, Ole

    2007-01-01

    A topology optimized metamaterial-based electrically small antenna configuration that is independent of a specific spherical and/or cylindrical metamaterial shell design is demonstrated. Topology optimization is shown to provide the optimal value and placement of a given ideal metamaterial in space...

  13. Optimization of Equipment Maintenance Strategy Based on Availability

    Institute of Scientific and Technical Information of China (English)

    张友诚

    2001-01-01

    It is very important to optimize maintenance strategy in maintenance plan. Proper parameters play a decisive role for the optimization. In the opinion of writer, availability is a basic parameter, failure consequence cost and failure characteristic are also important parameters. Maintenance strategy can be optimized on the base by means of quantitative analysis and diagram.

  14. Process optimization of friction stir welding based on thermal models

    DEFF Research Database (Denmark)

    Larsen, Anders Astrup

    2010-01-01

    This thesis investigates how to apply optimization methods to numerical models of a friction stir welding process. The work is intended as a proof-of-concept using different methods that are applicable to models of high complexity, possibly with high computational cost, and without the possibility...... information of the high-fidelity model. The optimization schemes are applied to stationary thermal models of differing complexity of the friction stir welding process. The optimization problems considered are based on optimizing the temperature field in the workpiece by finding optimal translational speed....... Also an optimization problem based on a microstructure model is solved, allowing the hardness distribution in the plate to be optimized. The use of purely thermal models represents a simplification of the real process; nonetheless, it shows the applicability of the optimization methods considered...

  15. Optimal design of steel portal frames based on genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    Yue CHEN; Kai HU

    2008-01-01

    As for the optimal design of steel portal frames, due to both the complexity of cross selections of beams and columns and the discreteness of design variables, it is difficult to obtain satisfactory results by traditional optimization. Based on a set of constraints of the Technical Specification for Light-weighted Steel Portal Frames of China, a genetic algorithm (GA) optimization program for portal frames, written in MATLAB code, was proposed in this paper. The graph user interface (GUI) is also developed for this optimal program, so that it can be used much more conveniently. Finally, some examples illustrate the effectiveness and efficiency of the genetic-algorithm-based optimal program.

  16. An Efficient Method for Reliability-based Multidisciplinary Design Optimization

    Institute of Scientific and Technical Information of China (English)

    Fan Hui; Li Weiji

    2008-01-01

    Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidiscipLinary design optimization (MDO) techniques for the design of complex engineering system. An advanced first order second moment method-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing multidisciplinary optimization techniques and the reliability analysis methods. It is seen through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current deterministic optimization process.

  17. Topology Optimization in Damping Structure Based on ESO

    Institute of Scientific and Technical Information of China (English)

    GUO Zhong-ze; CHEN Yu-ze; HOU Qiang

    2008-01-01

    The damping material optimal placement for the structure with damping layer is studied based on evolutionary structural optimization (ESO) to maximize modal loss factors. A mathematical model is constructed with the objective function defined as the maximum of modal loss factors of the structure and design constraints function defined as volume fraction ofdamping material. The optimal placement is found. Several examples are presented for verification. The results demonstratethat the method based on ESO is effective in solving the topology optimization of the structure with uncon-strained damping layer and constrained damping layer. This optimization method suits for free and constrained damping structures.

  18. Structural Optimization of Machine Gun Based on Dynamic Stability Concept

    Institute of Scientific and Technical Information of China (English)

    LI Yong-jian; WANG Rui-lin; ZHANG Ben-jun

    2008-01-01

    Improving the firing accuracy is a final goal of structural optimization of machine guns. The main factors which affect the dispersion accuracy of machine gun are analyzed. Based on the concept of dynamic stability, a structural optimization model is built up, and the sensitivity of dispersion accuracy to design variables is analyzed. The optimization results of a type of machine gun show that the method is valid, feasible, and can be used as a guide to the structural optimization of other automatic weapons.

  19. Model-based multiobjective evolutionary algorithm optimization for HCCI engines

    OpenAIRE

    Ma, He; Xu, Hongming; Wang, Jihong; Schnier, Thorsten; Neaves, Ben; Tan, Cheng; Wang, Zhi

    2014-01-01

    Modern engines feature a considerable number of adjustable control parameters. With this increasing number of Degrees of Freedom (DoF) for engines, and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated engine optimization approach is desired. In this paper, a self-learning evolutionary algorithm based multi-objective globally optimization approach for a H...

  20. Weigh in Motion Based on Parameters Optimization

    Institute of Scientific and Technical Information of China (English)

    ZHOU Zhi-feng; CAI Ping; CHEN Ri-xing

    2009-01-01

    Dynamic tire forces are the main factor affecting the measurement accuracy of the axle weight of moving vehicle. This paper presents a novel method to reduce the influence of the dynamic tire forces on the weighing accuracy. On the basis of analyzing the characteristic of the dynamic tire forces, the objective optimization equation is constructed. The optimization algorithm is presented to get the optimal estimations of the objective parameters. According to the estimations of the parameters, the dynamic tire forces are separated from the axle weigh signal. The results of simulation and field experiments prove the effectiveness of the proposed method.

  1. Adaptive Portfolio Optimization for Multiple Electricity Markets Participation.

    Science.gov (United States)

    Pinto, Tiago; Morais, Hugo; Sousa, Tiago M; Sousa, Tiago; Vale, Zita; Praca, Isabel; Faia, Ricardo; Pires, Eduardo Jose Solteiro

    2016-08-01

    The increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources' particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players' participation in electric power negotiations while improving energy efficiency. The opportunity for players' participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator-MIBEL.

  2. PARTICLE SWARM OPTIMIZATION BASED OF THE MAXIMUM ...

    African Journals Online (AJOL)

    2010-06-30

    Jun 30, 2010 ... This latter change instantaneously with changing radiation and temperature, ... dealing accurately with these optimization problems and to ... Shunt resistance Rsh, in parallel with the diode, this corresponds to the leakage.

  3. Portfolio optimization using local linear regression ensembles in RapidMiner

    OpenAIRE

    Gabor Nagy; Gergo Barta; Tamas Henk

    2015-01-01

    In this paper we implement a Local Linear Regression Ensemble Committee (LOLREC) to predict 1-day-ahead returns of 453 assets form the S&P500. The estimates and the historical returns of the committees are used to compute the weights of the portfolio from the 453 stock. The proposed method outperforms benchmark portfolio selection strategies that optimize the growth rate of the capital. We investigate the effect of algorithm parameter m: the number of selected stocks on achieved average annua...

  4. GPP-Based Soft Base Station Designing and Optimization

    Institute of Scientific and Technical Information of China (English)

    Xiao-Feng Tao; Yan-Zhao Hou; Kai-Dong Wang; Hai-Yang He; Y.Jay Guo

    2013-01-01

    It is generally acknowledged that mobile communication base stations are composed of hardware components such as Field Programming Gate Array (FPGA),Digital Signal Processor (DSP),which promise reliable and fluent services for the mobile users.However,with the increasing demand for energy-efficiency,approaches of low power-consumption and high-flexibility are needed urgently.In this circumstance,General Purpose Processor (GPP) attracts people's attention for its low-cost and flexibility.Benefited from the development of modern GPP in multi-core,Single Instruction Multiple Data (SIMD) instructions,larger cache,etc.,GPPs are capable of performing high-density digital processing.In this paper,we compare several software-defined radio (SDR) prototypes and propose the general architecture of GPP-based soft base stations.Then,the schematic design of resource allocation and algorithm optimization in soft base station implementation are studied.As an application example,a prototype of GPP-based soft base station referring to the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) is realized and evaluated.To the best of our knowledge,it is the first Soft-LTE prototype ever reported.In the end,we evaluate the timing performance of the LTE soft base station and a packet loss ratio of less than 0.003 is obtained.

  5. Electricity price forecasting using generalized regression neural network based on principal components analysis

    Institute of Scientific and Technical Information of China (English)

    牛东晓; 刘达; 邢棉

    2008-01-01

    A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.

  6. Reliability Based Optimization of Composite Laminates for Frequency Constraint

    Institute of Scientific and Technical Information of China (English)

    Wu Hao; Yan Ying; Liu Yujia

    2008-01-01

    The reliability based optimization (RBO) issue of composite laminates under fundamental frequency constraint is studied. Considering the uncertainties of material properties, the frequency constraint reliability of the structure is evaluated by the combination of response surface method (RSM) and finite element method. An optimization algorithm is developed based on the mechanism of laminate frequency characteristics, to optimize the laminate in terms of the ply amount and orientation angles. Numerical examples of composite laminates and cylindrical shell illustrate the advantages of the present optimization algorithm on the efficiency and applicability respects.The optimal solutions of RBO are obviously different from the deterministic optimization results, and the necessity of considering material property uncertainties in the composite srtuctural frequency constraint optimization is revealed.

  7. Research on particle swarm optimization algorithm based on optimal movement probability

    Science.gov (United States)

    Ma, Jianhong; Zhang, Han; He, Baofeng

    2017-01-01

    The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.

  8. Decomposition Techniques and Effective Algorithms in Reliability-Based Optimization

    DEFF Research Database (Denmark)

    Enevoldsen, I.; Sørensen, John Dalsgaard

    1995-01-01

    The common problem of an extensive number of limit state function calculations in the various formulations and applications of reliability-based optimization is treated. It is suggested to use a formulation based on decomposition techniques so the nested two-level optimization problem can be solved...

  9. Solution of optimal power flow using evolutionary-based algorithms

    African Journals Online (AJOL)

    This paper applies two reliable and efficient evolutionary-based methods named Shuffled Frog Leaping Algorithm ... Grey Wolf Optimizer (GWO) to solve Optimal Power Flow (OPF) problem. OPF is ..... The wolves search for the prey based on the alpha, beta, and delta positions. ..... Energy Conversion and Management, Vol.

  10. Hierarchical control based on Hopfield network for nonseparable optimization problems

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The nonseparable optimization control problem is considered, where the overall objective function is not of an additive form with respect to subsystems. Since there exists the problem that computation is very slow when using iterative algorithms in multiobjective optimization, Hopfield optimization hierarchical network based on IPM is presented to overcome such slow computation difficulty. Asymptotic stability of this Hopfield network is proved and its equilibrium point is the optimal point of the original problem. The simulation shows that the net is effective to deal with the optimization control problem for large-scale nonseparable steady state systems.

  11. Optimization based automated curation of metabolic reconstructions

    Directory of Open Access Journals (Sweden)

    Maranas Costas D

    2007-06-01

    Full Text Available Abstract Background Currently, there exists tens of different microbial and eukaryotic metabolic reconstructions (e.g., Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis with many more under development. All of these reconstructions are inherently incomplete with some functionalities missing due to the lack of experimental and/or homology information. A key challenge in the automated generation of genome-scale reconstructions is the elucidation of these gaps and the subsequent generation of hypotheses to bridge them. Results In this work, an optimization based procedure is proposed to identify and eliminate network gaps in these reconstructions. First we identify the metabolites in the metabolic network reconstruction which cannot be produced under any uptake conditions and subsequently we identify the reactions from a customized multi-organism database that restores the connectivity of these metabolites to the parent network using four mechanisms. This connectivity restoration is hypothesized to take place through four mechanisms: a reversing the directionality of one or more reactions in the existing model, b adding reaction from another organism to provide functionality absent in the existing model, c adding external transport mechanisms to allow for importation of metabolites in the existing model and d restore flow by adding intracellular transport reactions in multi-compartment models. We demonstrate this procedure for the genome- scale reconstruction of Escherichia coli and also Saccharomyces cerevisiae wherein compartmentalization of intra-cellular reactions results in a more complex topology of the metabolic network. We determine that about 10% of metabolites in E. coli and 30% of metabolites in S. cerevisiae cannot carry any flux. Interestingly, the dominant flow restoration mechanism is directionality reversals of existing reactions in the respective models. Conclusion We have proposed systematic methods to identify and

  12. Reliability-Based Optimization and Optimal Reliability Level of Offshore Wind Turbines

    DEFF Research Database (Denmark)

    Tarp-Johansen, N.J.; Sørensen, John Dalsgaard

    2006-01-01

    Different formulations relevant for the reliability-based optimization of offshore wind turbines are presented, including different reconstruction policies in case of failure. Illustrative examples are presented and, as a part of the results, optimal reliability levels for the different failure m...

  13. Reliability-Based Optimization and Optimal Reliability Level of Offshore Wind Turbines

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Tarp-Johansen, N.J.

    2005-01-01

    Different formulations relevant for the reliability-based optimization of offshore wind turbines are presented, including different reconstruction policies in case of failure. Illustrative examples are presented and, as a part of the results, optimal reliability levels for the different failure...

  14. Optimal separable bases and molecular collisions

    Energy Technology Data Exchange (ETDEWEB)

    Poirier, L W [Univ. of California, Berkeley, CA (United States)

    1997-12-01

    A new methodology is proposed for the efficient determination of Green`s functions and eigenstates for quantum systems of two or more dimensions. For a given Hamiltonian, the best possible separable approximation is obtained from the set of all Hilbert space operators. It is shown that this determination itself, as well as the solution of the resultant approximation, are problems of reduced dimensionality for most systems of physical interest. Moreover, the approximate eigenstates constitute the optimal separable basis, in the sense of self-consistent field theory. These distorted waves give rise to a Born series with optimized convergence properties. Analytical results are presented for an application of the method to the two-dimensional shifted harmonic oscillator system. The primary interest however, is quantum reactive scattering in molecular systems. For numerical calculations, the use of distorted waves corresponds to numerical preconditioning. The new methodology therefore gives rise to an optimized preconditioning scheme for the efficient calculation of reactive and inelastic scattering amplitudes, especially at intermediate energies. This scheme is particularly suited to discrete variable representations (DVR`s) and iterative sparse matrix methods commonly employed in such calculations. State to state and cumulative reactive scattering results obtained via the optimized preconditioner are presented for the two-dimensional collinear H + H{sub 2} {yields} H{sub 2} + H system. Computational time and memory requirements for this system are drastically reduced in comparison with other methods, and results are obtained for previously prohibitive energy regimes.

  15. Reliability Based Optimization of Structural Systems

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard

    1987-01-01

    The optimization problem to design structural systems such that the reliability is satisfactory during the whole lifetime of the structure is considered in this paper. Some of the quantities modelling the loads and the strength of the structure are modelled as random variables. The reliability...

  16. Optimal separable bases and molecular collisions

    Energy Technology Data Exchange (ETDEWEB)

    Poirier, Lionel W. [Univ. of California, Berkeley, CA (United States)

    1997-12-01

    A new methodology is proposed for the efficient determination of Green`s functions and eigenstates for quantum systems of two or more dimensions. For a given Hamiltonian, the best possible separable approximation is obtained from the set of all Hilbert space operators. It is shown that this determination itself, as well as the solution of the resultant approximation, are problems of reduced dimensionality for most systems of physical interest. Moreover, the approximate eigenstates constitute the optimal separable basis, in the sense of self-consistent field theory. These distorted waves give rise to a Born series with optimized convergence properties. Analytical results are presented for an application of the method to the two-dimensional shifted harmonic oscillator system. The primary interest however, is quantum reactive scattering in molecular systems. For numerical calculations, the use of distorted waves corresponds to numerical preconditioning. The new methodology therefore gives rise to an optimized preconditioning scheme for the efficient calculation of reactive and inelastic scattering amplitudes, especially at intermediate energies. This scheme is particularly suited to discrete variable representations (DVR`s) and iterative sparse matrix methods commonly employed in such calculations. State to state and cumulative reactive scattering results obtained via the optimized preconditioner are presented for the two-dimensional collinear H + H2 → H2 + H system. Computational time and memory requirements for this system are drastically reduced in comparison with other methods, and results are obtained for previously prohibitive energy regimes.

  17. DYNAMIC LABELING BASED FPGA DELAY OPTIMIZATION ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    吕宗伟; 林争辉; 张镭

    2001-01-01

    DAG-MAP is an FPGA technology mapping algorithm for delay optimization and the labeling phase is the algorithm's kernel. This paper studied the labeling phase and presented an improved labeling method. It is shown through the experimental results on MCNC benchmarks that the improved method is more effective than the original method while the computation time is almost the same.

  18. An approximation based global optimization strategy for structural synthesis

    Science.gov (United States)

    Sepulveda, A. E.; Schmit, L. A.

    1991-01-01

    A global optimization strategy for structural synthesis based on approximation concepts is presented. The methodology involves the solution of a sequence of highly accurate approximate problems using a global optimization algorithm. The global optimization algorithm implemented consists of a branch and bound strategy based on the interval evaluation of the objective function and constraint functions, combined with a local feasible directions algorithm. The approximate design optimization problems are constructed using first order approximations of selected intermediate response quantities in terms of intermediate design variables. Some numerical results for example problems are presented to illustrate the efficacy of the design procedure setforth.

  19. A Novel Optimal Control Method for Impulsive-Correction Projectile Based on Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Ruisheng Sun

    2016-01-01

    Full Text Available This paper presents a new parametric optimization approach based on a modified particle swarm optimization (PSO to design a class of impulsive-correction projectiles with discrete, flexible-time interval, and finite-energy control. In terms of optimal control theory, the task is described as the formulation of minimum working number of impulses and minimum control error, which involves reference model linearization, boundary conditions, and discontinuous objective function. These result in difficulties in finding the global optimum solution by directly utilizing any other optimization approaches, for example, Hp-adaptive pseudospectral method. Consequently, PSO mechanism is employed for optimal setting of impulsive control by considering the time intervals between two neighboring lateral impulses as design variables, which makes the briefness of the optimization process. A modification on basic PSO algorithm is developed to improve the convergence speed of this optimization through linearly decreasing the inertial weight. In addition, a suboptimal control and guidance law based on PSO technique are put forward for the real-time consideration of the online design in practice. Finally, a simulation case coupled with a nonlinear flight dynamic model is applied to validate the modified PSO control algorithm. The results of comparative study illustrate that the proposed optimal control algorithm has a good performance in obtaining the optimal control efficiently and accurately and provides a reference approach to handling such impulsive-correction problem.

  20. Combination of anti-optimization and fuzzy-set-based analysis for structural optimization under uncertainty

    Directory of Open Access Journals (Sweden)

    J. Fang

    1998-01-01

    Full Text Available An approach to the optimum design of structures, in which uncertainties with a fuzzy nature in the magnitude of the loads are considered, is proposed in this study. The optimization process under fuzzy loads is transformed into a fuzzy optimization problem based on the notion of Werners' maximizing set by defining membership functions of the objective function and constraints. In this paper, Werner's maximizing set is defined using the results obtained by first conducting an optimization through anti-optimization modeling of the uncertain loads. An example of a ten-bar truss is used to illustrate the present optimization process. The results are compared with those yielded by other optimization methods.

  1. Function Optimization Based on Quantum Genetic Algorithm

    OpenAIRE

    Ying Sun; Hegen Xiong

    2014-01-01

    Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA) in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded c...

  2. Optimal Design of DC Electromagnets Based on Imposed Dynamic Characteristics

    Directory of Open Access Journals (Sweden)

    Sergiu Ivas

    2016-10-01

    Full Text Available In this paper is proposed a method for computing of optimal geometric dimensions of a DC electromagnet, based on the imposed dynamical characteristics. For obtaining the optimal design, it is built the criterion function in an analytic form that may be optimized in the order to find the constructive solution. Numerical simulations performed in Matlab software confirm the proposed work. The presented method can be extended to other electromagnetic devices which frequently operate in dynamic regime.

  3. Support vector machines optimization based theory, algorithms, and extensions

    CERN Document Server

    Deng, Naiyang; Zhang, Chunhua

    2013-01-01

    Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twi

  4. Fuzzy controller based on chaos optimal design and its application

    Institute of Scientific and Technical Information of China (English)

    邹恩; 李祥飞; 张泰山

    2004-01-01

    In order to overcome difficulty of tuning parameters of fuzzy controller, a chaos optimal design method based on annealing strategy is proposed. First, apply the chaotic variables to search for parameters of fuzzy controller, and transform the optimal variables into chaotic variables by carrier-wave method. Making use of the intrinsic stochastic property and ergodicity of chaos movement to escape from the local minimum and direct optimization searching within global range, an approximate global optimal solution is obtained. Then, the chaos local searching and optimization based on annealing strategy are cited, the parameters are optimized again within the limits of the approximate global optimal solution, the optimization is realized by means of combination of global and partial chaos searching, which can converge quickly to global optimal value. Finally, the third order system and discrete nonlinear system are simulated and compared with traditional method of fuzzy control. The results show that the new chaos optimal design method is superior to fuzzy control method, and that the control results are of high precision, with no overshoot and fast response.

  5. Optimal Control of Energy Storage Based on Fuzzy Correlated-chance Programming%基于模糊相关机会规划的储能优化控制

    Institute of Scientific and Technical Information of China (English)

    胡永强; 刘晨亮; 赵书强; 王明雨

    2014-01-01

    为了最大限度地使风光储联合发电系统的出力与计划出力相匹配,采用提前一日对储能装置进行优化控制的方法。因风光出力具有模糊性,提出了基于模糊相关机会规划的储能优化控制方法。该方法考虑了储能装置的出力和电量约束条件,每个时段的匹配程度用可信度表示,以一日内96个时段总的可信度均值最大为目标,采用基于模糊模拟的遗传算法求解,得到可信度均值最大时不同时段对应的储能充放电功率。算例分析表明,所提出的储能优化控制策略使风光储联合发电系统的总出力可基本跟踪计划出力曲线。%In order to maximize the match between the output of the hybrid wind/photovoltaic/energy storage system and the scheduling curve,the energy storage device is optimized and controlled one day ahead.Owing to the fuzziness of the wind and photovoltaic output,an optimal control method of energy storage is proposed based on a fuzzy correlated-chance programming theory.This method considers the constraints of the energy storage device including the power output and energy constraints, and the matching degree of each time is represented by the credibility value.So the final goal is to maximize the mean value of the total credibility within a day of 9 6 periods.By using the fuzzy simulation based genetic algorithm,the different periods of energy storage charge and discharge power corresponding to the credibility of the maximum mean value can be obtained. Numerical results analysis shows that the proposed optimal control strategy is valid for maximizing the match between the output of the hybrid wind/photovoltaic/energy storage system and the scheduling curve. This work is supported by Fundamental Research Funds for the Central Universities(No.12MS106).

  6. Geometry Optimization of a Segmented Thermoelectric Generator Based on Multi-parameter and Nonlinear Optimization Method

    Science.gov (United States)

    Cai, Lanlan; Li, Peng; Luo, Qi; Zhai, Pengcheng; Zhang, Qingjie

    2017-03-01

    As no single thermoelectric material has presented a high figure-of-merit (ZT) over a very wide temperature range, segmented thermoelectric generators (STEGs), where the p- and n-legs are formed of different thermoelectric material segments joined in series, have been developed to improve the performance of thermoelectric generators. A crucial but difficult problem in a STEG design is to determine the optimal values of the geometrical parameters, like the relative lengths of each segment and the cross-sectional area ratio of the n- and p-legs. Herein, a multi-parameter and nonlinear optimization method, based on the Improved Powell Algorithm in conjunction with the discrete numerical model, was implemented to solve the STEG's geometrical optimization problem. The multi-parameter optimal results were validated by comparison with the optimal outcomes obtained from the single-parameter optimization method. Finally, the effect of the hot- and cold-junction temperatures on the geometry optimization was investigated. Results show that the optimal geometry parameters for maximizing the specific output power of a STEG are different from those for maximizing the conversion efficiency. Data also suggest that the optimal geometry parameters and the interfacial temperatures of the adjacent segments optimized for maximum specific output power or conversion efficiency vary with changing hot- and cold-junction temperatures. Through the geometry optimization, the CoSb3/Bi2Te3-based STEG can obtain a maximum specific output power up to 1725.3 W/kg and a maximum efficiency of 13.4% when operating at a hot-junction temperature of 823 K and a cold-junction temperature of 298 K.

  7. Performance investigation of multigrid optimization for DNS-based optimal control problems

    Science.gov (United States)

    Nita, Cornelia; Vandewalle, Stefan; Meyers, Johan

    2016-11-01

    Optimal control theory in Direct Numerical Simulation (DNS) or Large-Eddy Simulation (LES) of turbulent flow involves large computational cost and memory overhead for the optimization of the controls. In this context, the minimization of the cost functional is typically achieved by employing gradient-based iterative methods such as quasi-Newton, truncated Newton or non-linear conjugate gradient. In the current work, we investigate the multigrid optimization strategy (MGOpt) in order to speed up the convergence of the damped L-BFGS algorithm for DNS-based optimal control problems. The method consists in a hierarchy of optimization problems defined on different representation levels aiming to reduce the computational resources associated with the cost functional improvement on the finest level. We examine the MGOpt efficiency for the optimization of an internal volume force distribution with the goal of reducing the turbulent kinetic energy or increasing the energy extraction in a turbulent wall-bounded flow; problems that are respectively related to drag reduction in boundary layers, or energy extraction in large wind farms. Results indicate that in some cases the multigrid optimization method requires up to a factor two less DNS and adjoint DNS than single-grid damped L-BFGS. The authors acknowledge support from OPTEC (OPTimization in Engineering Center of Excellence, KU Leuven, Grant No PFV/10/002).

  8. Optimal Reliability-Based Planning of Experiments for POD Curves

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Faber, M. H.; Kroon, I. B.

    Optimal planning of the crack detection test is considered. The test are used to update the information on the reliability of the inspection techniques modelled by probability of detection (P.O.D.) curves. It is shown how cost-optimal and reliability based test plans can be obtained using First O...... Order Reliability Methods in combination with life-cycle cost-optimal inspection and maintenance planning. The methodology is based on preposterior analyses from Bayesian decision theory. An illustrative example is shown.......Optimal planning of the crack detection test is considered. The test are used to update the information on the reliability of the inspection techniques modelled by probability of detection (P.O.D.) curves. It is shown how cost-optimal and reliability based test plans can be obtained using First...

  9. An integrated reliability-based design optimization of offshore towers

    Energy Technology Data Exchange (ETDEWEB)

    Karadeniz, Halil [Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft (Netherlands)], E-mail: h.karadeniz@tudelft.nl; Togan, Vedat [Department of Civil Engineering, Karadeniz Technical University, Trabzon (Turkey); Vrouwenvelder, Ton [Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft (Netherlands)

    2009-10-15

    After recognizing the uncertainty in the parameters such as material, loading, geometry and so on in contrast with the conventional optimization, the reliability-based design optimization (RBDO) concept has become more meaningful to perform an economical design implementation, which includes a reliability analysis and an optimization algorithm. RBDO procedures include structural analysis, reliability analysis and sensitivity analysis both for optimization and for reliability. The efficiency of the RBDO system depends on the mentioned numerical algorithms. In this work, an integrated algorithms system is proposed to implement the RBDO of the offshore towers, which are subjected to the extreme wave loading. The numerical strategies interacting with each other to fulfill the RBDO of towers are as follows: (a) a structural analysis program, SAPOS, (b) an optimization program, SQP and (c) a reliability analysis program based on FORM. A demonstration of an example tripod tower under the reliability constraints based on limit states of the critical stress, buckling and the natural frequency is presented.

  10. Gradient-Based Cuckoo Search for Global Optimization

    Directory of Open Access Journals (Sweden)

    Seif-Eddeen K. Fateen

    2014-01-01

    Full Text Available One of the major advantages of stochastic global optimization methods is the lack of the need of the gradient of the objective function. However, in some cases, this gradient is readily available and can be used to improve the numerical performance of stochastic optimization methods specially the quality and precision of global optimal solution. In this study, we proposed a gradient-based modification to the cuckoo search algorithm, which is a nature-inspired swarm-based stochastic global optimization method. We introduced the gradient-based cuckoo search (GBCS and evaluated its performance vis-à-vis the original algorithm in solving twenty-four benchmark functions. The use of GBCS improved reliability and effectiveness of the algorithm in all but four of the tested benchmark problems. GBCS proved to be a strong candidate for solving difficult optimization problems, for which the gradient of the objective function is readily available.

  11. OPF-Based Optimal Location of Two Systems Two Terminal HVDC to Power System Optimal Operation

    Directory of Open Access Journals (Sweden)

    Mehdi Abolfazli

    2013-04-01

    Full Text Available In this paper a suitable mathematical model of the two terminal HVDC system is provided for optimal power flow (OPF and optimal location based on OPF such power injection model. The ability of voltage source converter (VSC-based HVDC to independently control active and reactive power is well represented by the model. The model is used to develop an OPF-based optimal location algorithm of two systems two terminal HVDC to minimize the total fuel cost and active power losses as objective function. The optimization framework is modeled as non-linear programming (NLP and solved by Matlab and GAMS softwares. The proposed algorithm is implemented on the IEEE 14- and 30-bus test systems. The simulation results show ability of two systems two terminal HVDC in improving the power system operation. Furthermore, two systems two terminal HVDC is compared by PST and OUPFC in the power system operation from economical and technical aspects.

  12. Physics-based model for a water-saving greenhouse

    NARCIS (Netherlands)

    Speetjens, S.L.; Stigter, J.D.; Straten, van G.

    2010-01-01

    A new greenhouse type has been designed to study ways of decreasing water use by horticulture in semi-arid regions. To control the greenhouse a model-based control design is required. To this end a model is needed to predict the systems behaviour (1 day ahead), without much computational effort. A

  13. Reliability-Based Optimization for Maintenance Management in Bridge Networks

    OpenAIRE

    Hu, Xiaofei

    2014-01-01

    This dissertation addresses the problem of optimizing maintenance, repair and reconstruction decisions for bridge networks. Incorporating network topologies into bridge management problems is computationally difficult. Because of the interdependencies among networked bridges, they have to be analyzed together. Simulation-based numerical optimization techniques adopted in past research are limited to networks of moderate sizes. In this dissertation, novel approaches are developed to dete...

  14. Optimal Reliability-Based Planning of Experiments for POD Curves

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Faber, M. H.; Kroon, I. B.

    Optimal planning of the crack detection test is considered. The test are used to update the information on the reliability of the inspection techniques modelled by probability of detection (P.O.D.) curves. It is shown how cost-optimal and reliability based test plans can be obtained using First...

  15. Cross-layer utility-based system optimization

    NARCIS (Netherlands)

    Ditzel, M.; Kester, L.J.H.M.; Broek, S.P. van den; Rijn, M. van

    2013-01-01

    Multilevel fusion systems need provisions to optimally schedule scarce processing and communication resources. To this end, we explore the idea of using utility-based metrics to optimize the run-time operation of a computation and communication constrained multilevel system, including automatic deci

  16. Reliability-Based Optimization of Series Systems of Parallel Systems

    DEFF Research Database (Denmark)

    Enevoldsen, I.; Sørensen, John Dalsgaard

    Reliability-based design of structural systems is considered. Especially systems where the reliability model is a series system of parallel systems are analysed. A sensitivity analysis for this class of problems is presented. Direct and sequential optimization procedures to solve the optimization...... problems are described. Numerical tests indicate that a sequential technique called the bounds iteration method (BIM) is particularly fast and stable....

  17. portfolio optimization based on nonparametric estimation methods

    Directory of Open Access Journals (Sweden)

    mahsa ghandehari

    2017-03-01

    Full Text Available One of the major issues investors are facing with in capital markets is decision making about select an appropriate stock exchange for investing and selecting an optimal portfolio. This process is done through the risk and expected return assessment. On the other hand in portfolio selection problem if the assets expected returns are normally distributed, variance and standard deviation are used as a risk measure. But, the expected returns on assets are not necessarily normal and sometimes have dramatic differences from normal distribution. This paper with the introduction of conditional value at risk ( CVaR, as a measure of risk in a nonparametric framework, for a given expected return, offers the optimal portfolio and this method is compared with the linear programming method. The data used in this study consists of monthly returns of 15 companies selected from the top 50 companies in Tehran Stock Exchange during the winter of 1392 which is considered from April of 1388 to June of 1393. The results of this study show the superiority of nonparametric method over the linear programming method and the nonparametric method is much faster than the linear programming method.

  18. Affordance Learning Based on Subtask's Optimal Strategy

    Directory of Open Access Journals (Sweden)

    Huaqing Min

    2015-08-01

    Full Text Available Affordances define the relationships between the robot and environment, in terms of actions that the robot is able to perform. Prior work is mainly about predicting the possibility of a reactive action, and the object's affordance is invariable. However, in the domain of dynamic programming, a robot’s task could often be decomposed into several subtasks, and each subtask could limit the search space. As a result, the robot only needs to replan its sub strategy when an unexpected situation happens, and an object’s affordance might change over time depending on the robot’s state and current subtask. In this paper, we propose a novel affordance model linking the subtask, object, robot state and optimal action. An affordance represents the first action of the optimal strategy under the current subtask when detecting an object, and its influence is promoted from a primitive action to the subtask strategy. Furthermore, hierarchical reinforcement learning and state abstraction mechanism are introduced to learn the task graph and reduce state space. In the navigation experiment, the robot equipped with a camera could learn the objects’ crucial characteristics, and gain their affordances in different subtasks.

  19. Optimization of FPGA-based Moore FSM

    Science.gov (United States)

    Barkalov, Aleksander; Titarenko, Larysa; Chmielewski, Sławomir

    2014-10-01

    A metod is proposed for hardware reduction in FPGA-based Moore FSM. It is based on using two sources of codes. It reduces the number of LUTs in the FSM circuit. The results of investigations are shown.

  20. Optimal Heating in Heat-Treatment Process Based on Grey Asynchronous Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    2012-01-01

    To ensure plate heating quality and reduce energy consumption in heat-treatment process, optimal heating for plates in a roller hearth furnace was investigated and a new strategy for heating procedure optimization was developed. During solving process, plate temperature forecast model based on heat transfer mechanics was established to calculate plate temperature with the assumed heating procedure. In addition, multi-objective feature of optimal heating was analyzed. And the method, which is composed of asynchronous particle swarm optimization and grey relational analysis, was adopted for solving the multi-objective problem. The developed strategy for optimizing heating has been applied to the mass production. The result indicates that the absolute plate discharging temperature deviation between measured value and target value does not exceed ± 8 ℃, and the relative deviation is less than ± 0.77%.

  1. Optimization of Land Use Structure Based on Ecological GREEN Equivalent

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Optimization of land use structure consists of economic and social and ecological optimization.Applying the minds of system engineering and principles of ecology,this paper presents such thoughts:the optimal forest-coverage rate calculated according to the reality of a district is set as main standard of ecological rationality in the district;through considering the value of ecosystem services of the land with GREEN equivalent (mainly cultivated land and grassland)and based on the rule,GREEN equivalent,this paper introduces the area conversion between woodland and cultivated land,also between woodland and grassland;this paper establishes a multi-dimension controlling model of optimization of land use structure.In addition,a multi-objective linear programming model for optimization of land use structure is designed.In the end,this paper tests and verifies this theory of ecological optimization,taking Qionghai city in Hainan Province as an example.

  2. Optimization of transmission system design based on genetic algorithm

    Directory of Open Access Journals (Sweden)

    Xianbing Chen

    2016-05-01

    Full Text Available Transmission system is a crucial precision mechanism for twin-screw chemi-mechanical pulping equipment. The structure of the system designed by traditional method is not optimal because the structure designed by the traditional methods is easy to fall into the local optimum. To achieve the global optimum, this article applies the genetic algorithm which has grown in recent years in the field of structure optimization. The article uses the volume of transmission system as the objective function to optimize the structure designed by traditional method. Compared to the simulation results, the original structure is not optimal, and the optimized structure is tighter and more reasonable. Based on the optimized results, the transmission shafts in the transmission system are designed and checked, and the parameters of the twin screw are selected and calculated. The article provided an effective method to design the structure of transmission system.

  3. Reliability-based design optimization with progressive surrogate models

    Science.gov (United States)

    Kanakasabai, Pugazhendhi; Dhingra, Anoop K.

    2014-12-01

    Reliability-based design optimization (RBDO) has traditionally been solved as a nested (bilevel) optimization problem, which is a computationally expensive approach. Unilevel and decoupled approaches for solving the RBDO problem have also been suggested in the past to improve the computational efficiency. However, these approaches also require a large number of response evaluations during optimization. To alleviate the computational burden, surrogate models have been used for reliability evaluation. These approaches involve construction of surrogate models for the reliability computation at each point visited by the optimizer in the design variable space. In this article, a novel approach to solving the RBDO problem is proposed based on a progressive sensitivity surrogate model. The sensitivity surrogate models are built in the design variable space outside the optimization loop using the kriging method or the moving least squares (MLS) method based on sample points generated from low-discrepancy sampling (LDS) to estimate the most probable point of failure (MPP). During the iterative deterministic optimization, the MPP is estimated from the surrogate model for each design point visited by the optimizer. The surrogate sensitivity model is also progressively updated for each new iteration of deterministic optimization by adding new points and their responses. Four example problems are presented showing the relative merits of the kriging and MLS approaches and the overall accuracy and improved efficiency of the proposed approach.

  4. Bi-objective optimization for multi-modal transportation routing planning problem based on Pareto optimality

    Directory of Open Access Journals (Sweden)

    Yan Sun

    2015-09-01

    Full Text Available Purpose: The purpose of study is to solve the multi-modal transportation routing planning problem that aims to select an optimal route to move a consignment of goods from its origin to its destination through the multi-modal transportation network. And the optimization is from two viewpoints including cost and time. Design/methodology/approach: In this study, a bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. Minimizing the total transportation cost and the total transportation time are set as the optimization objectives of the model. In order to balance the benefit between the two objectives, Pareto optimality is utilized to solve the model by gaining its Pareto frontier. The Pareto frontier of the model can provide the multi-modal transportation operator (MTO and customers with better decision support and it is gained by the normalized normal constraint method. Then, an experimental case study is designed to verify the feasibility of the model and Pareto optimality by using the mathematical programming software Lingo. Finally, the sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case. Findings: The calculation results indicate that the proposed model and Pareto optimality have good performance in dealing with the bi-objective optimization. The sensitivity analysis also shows the influence of the variation of the demand and supply on the multi-modal transportation organization clearly. Therefore, this method can be further promoted to the practice. Originality/value: A bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. The Pareto frontier based sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case.

  5. Performance optimization of web-based medical simulation.

    Science.gov (United States)

    Halic, Tansel; Ahn, Woojin; De, Suvranu

    2013-01-01

    This paper presents a technique for performance optimization of multimodal interactive web-based medical simulation. A web-based simulation framework is promising for easy access and wide dissemination of medical simulation. However, the real-time performance of the simulation highly depends on hardware capability on the client side. Providing consistent simulation in different hardware is critical for reliable medical simulation. This paper proposes a non-linear mixed integer programming model to optimize the performance of visualization and physics computation while considering hardware capability and application specific constraints. The optimization model identifies and parameterizes the rendering and computing capabilities of the client hardware using an exploratory proxy code. The parameters are utilized to determine the optimized simulation conditions including texture sizes, mesh sizes and canvas resolution. The test results show that the optimization model not only achieves a desired frame per second but also resolves visual artifacts due to low performance hardware.

  6. Hybrid and adaptive meta-model-based global optimization

    Science.gov (United States)

    Gu, J.; Li, G. Y.; Dong, Z.

    2012-01-01

    As an efficient and robust technique for global optimization, meta-model-based search methods have been increasingly used in solving complex and computation intensive design optimization problems. In this work, a hybrid and adaptive meta-model-based global optimization method that can automatically select appropriate meta-modelling techniques during the search process to improve search efficiency is introduced. The search initially applies three representative meta-models concurrently. Progress towards a better performing model is then introduced by selecting sample data points adaptively according to the calculated values of the three meta-models to improve modelling accuracy and search efficiency. To demonstrate the superior performance of the new algorithm over existing search methods, the new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization example involving vehicle crash simulation. The method is particularly suitable for design problems involving computation intensive, black-box analyses and simulations.

  7. Stochastic learning and optimization a sensitivity-based approach

    CERN Document Server

    Cao, Xi-Ren

    2007-01-01

    Performance optimization is vital in the design and operation of modern engineering systems. This book provides a unified framework based on a sensitivity point of view. It introduces new approaches and proposes new research topics.

  8. Design of Optimal Attack-Angle for RLV Reentry Based on Quantum Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Qingzhen Zhang

    2014-04-01

    Full Text Available The attack-angle optimization is a key problem for reentry trajectory design of a gliding type reusable launch vehicle (RLV. In order to solve such a problem, the equations of motion are derived first. A physical programming (PP method is briefly presented and the preference function is reflected in mathematical representation. The attack-angle optimization problem with four criteria (i.e., downrange, total heat, heat rate, and trajectory oscillation is converted into a single-objective optimization problem based on the PP method. A winged gliding reentry RLV is chosen as a simulation example and the transformed single-objective problem is solved by the quantum-behaved particle swarm optimization (QPSO algorithm based on two types of preference structures, longer range preference and smaller total heat preference. The constraints of maximizing heating rate, normal load factor, and dynamic pressure and minimizing terminal velocity are handled by a penalty function method. The simulation results demonstrate the efficiency of these methods. The physical causation of the optimal solution and the typical profiles are presented, which reflect the designer's preference. At last, the feasibility and advantages of QPSO are revealed by comparison with the results of genetic algorithm (GA and standard particle swarm optimization (PSO algorithm on this optimization problem.

  9. Reliability-Based Structural Optimization of Wave Energy Converters

    DEFF Research Database (Denmark)

    Ambühl, Simon; Kramer, Morten; Sørensen, John Dalsgaard

    2014-01-01

    More and more wave energy converter (WEC) concepts are reaching prototype level. Once the prototype level is reached, the next step in order to further decrease the levelized cost of energy (LCOE) is optimizing the overall system with a focus on structural and maintenance (inspection) costs......, as well as on the harvested power from the waves. The target of a fully-developed WEC technology is not maximizing its power output, but minimizing the resulting LCOE. This paper presents a methodology to optimize the structural design of WECs based on a reliability-based optimization problem...

  10. Torpedo Overall Multidisciplinary Design Based on Collaborative Optimization

    Institute of Scientific and Technical Information of China (English)

    YU De-hai; SONG Bao-wei; LI Jia-wang; YANG Shi-xing; GAO Zhi-yong

    2008-01-01

    A torpedo multidisciplinary design optimization (MDO) based on the collaborative optimization is proposed. Through decomposition and coordination, some problems in torpedo design such as multidisciplinary coupling, large data volume and complex data relationships can be solved. Taking aim at some complex problems in the torpedo design, such as computation in multidisciplinary design, organization, modeling and information exchange, the collaborative optimization methods based on approximate technology are presented. An example to increase the torpedo range is also given. It demonstrates that the method can converge quickly, has higher reliability and smaller data throughput, and is a very effective MDO method.

  11. Adaptive Central Force Optimization Algorithm Based on the Stability Analysis

    Directory of Open Access Journals (Sweden)

    Weiyi Qian

    2015-01-01

    Full Text Available In order to enhance the convergence capability of the central force optimization (CFO algorithm, an adaptive central force optimization (ACFO algorithm is presented by introducing an adaptive weight and defining an adaptive gravitational constant. The adaptive weight and gravitational constant are selected based on the stability theory of discrete time-varying dynamic systems. The convergence capability of ACFO algorithm is compared with the other improved CFO algorithm and evolutionary-based algorithm using 23 unimodal and multimodal benchmark functions. Experiments results show that ACFO substantially enhances the performance of CFO in terms of global optimality and solution accuracy.

  12. Optimization Research of Urban Space Configuration Based on Space Syntax

    Institute of Scientific and Technical Information of China (English)

    Zhu Qing; Wang Jingwen

    2005-01-01

    In this paper, a new method based on the space syntax is presented to optimize the urban space configuration. Space syntax theory is used to detect systematically whether one urban space configuration is optimal or not from four aspects including traffic space, cognition space, land use space and culture space. After introducing the computational and cognitive aspects of space syntax for the research of urban space, a framework of urban space optimization based on space syntax is proposed, then the integration with GIS and the extension to third dimension are discussed. Finally, a case study for Kanmen town of Zhejiang province of P.R.China is illustrated by using Axwoman tool.

  13. AN OPTIMIZATION ALGORITHM BASED ON BACTERIA BEHAVIOR

    Directory of Open Access Journals (Sweden)

    Ricardo Contreras

    2014-09-01

    Full Text Available Paradigms based on competition have shown to be useful for solving difficult problems. In this paper we present a new approach for solving hard problems using a collaborative philosophy. A collaborative philosophy can produce paradigms as interesting as the ones found in algorithms based on a competitive philosophy. Furthermore, we show that the performance - in problems associated to explosive combinatorial - is comparable to the performance obtained using a classic evolutive approach.

  14. Concentric Circular Antenna Array Synthesis Using Biogeography Based Optimization

    Directory of Open Access Journals (Sweden)

    Urvinder Singh

    2012-03-01

    Full Text Available Biogeography based optimization (BBO is a new stochastic force based on the science of biogeography. Biogeography is the schoolwork of geographical allotment of biological organisms. BBO utilizes migration operator to share information between the problem solutions. The problem solutions are known as habitats and sharing of features is called migration. In this paper, BBO algorithm is developed to optimize the current excitations of concentric circular antenna arrays (CCAA. Concentric Circular Antenna Array (CCAA has numerous attractive features that make it essential in mobile and communication applications. The goal of the optimization is to reduce the side lobe levels and the primary lobe beam width as much as possible. To confirm the capabilities of BBO, three different CCAA antennas of different sizes are taken. The results obtained by BBO are compared with the Real coded Genetic Algorithm (RGA, Craziness based Particle Swarm Optimization (CRPSO and Hybrid Evolutionary Programming (HEP.

  15. Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-07-01

    Full Text Available The optimized dispatch of different distributed generations (DGs in stand-alone microgrid (MG is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL and combined cooling-heating-power (CCHP model of micro-gas turbine (MT, a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV, wind turbine (WT, fuel cell (FC, diesel engine (DE, MT and energy storage (ES. Four typical scenarios were designed according to different day types (work day or weekend and weather conditions (sunny or rainy in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO to propose modified chaos particle swarm optimization (MCPSO whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.

  16. Optimal Sensor Decision Based on Particle Filter

    Institute of Scientific and Technical Information of China (English)

    XU Meng; WANG Hong-wei; HU Shi-qiang

    2006-01-01

    A novel infrared and radar synergistic tracking algorithm, which is based on the idea of closed loop control, and target's motion model identification and particle filter approach, was put forward. In order to improve the observability and filtering divergence of infrared search and tracking, the unscented Kalman filter algorithm that has stronger ability of non-linear approximation was adopted. The polynomial and least square method based on radar and IRST measurements to identify the parameters of the model was proposed, and a "pseudo sensor" was suggested to estimate the target position according to the identified model even if the radar is turned off. At last,the average Kullback-Leibler discrimination distance based on particle filter was used to measure the tracking performance, based on tracking performance and fuzzy stochastic decision, the idea of closed loop was used to retrieve the module parameter of "pseudo sensor". The experimental result indicates that the algorithm can not only limit the radar activity effectively but also keep the tracking accuracy of active/passive system well.

  17. GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE

    Directory of Open Access Journals (Sweden)

    Ashish Jain

    2012-07-01

    Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.

  18. AGENT based structural static and dynamic collaborative optimization

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    A static and dynamic collaborative optimization mode for complex machine system and itsontology project relationship are put forward, on which an agent-based structural static and dynamiccollaborative optimization system is constructed as two agent colonies: optimization agent colony andfinite element analysis colony. And a two-level solving strategy as well as the necessity and possibilityfor handing with finite element analysis model in multi-level mode is discussed. Furthermore, the coop-eration of all FEA agents for optimal design of complicated structural is studied in detail. Structural stat-ic and dynamic collaborative optimization of hydraulic excavator working equimpent is taken as an ex-ample to show that the system is reliable.

  19. Earth Observation Satellites Scheduling Based on Decomposition Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Feng Yao

    2010-11-01

    Full Text Available A decomposition-based optimization algorithm was proposed for solving Earth Observation Satellites scheduling problem. The problem was decomposed into task assignment main problem and single satellite scheduling sub-problem. In task assignment phase, the tasks were allocated to the satellites, and each satellite would schedule the task respectively in single satellite scheduling phase. We adopted an adaptive ant colony optimization algorithm to search the optimal task assignment scheme. Adaptive parameter adjusting strategy and pheromone trail smoothing strategy were introduced to balance the exploration and the exploitation of search process. A heuristic algorithm and a very fast simulated annealing algorithm were proposed to solve the single satellite scheduling problem. The task assignment scheme was valued by integrating the observation scheduling result of multiple satellites. The result was responded to the ant colony optimization algorithm, which can guide the search process of ant colony optimization. Computation results showed that the approach was effective to the satellites observation scheduling problem.

  20. Study on Ice Regime Forecast Based on SVR Optimized by Particle Swarm Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG; Fu-qiang; RONG; Fei

    2012-01-01

    [Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. Particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.

  1. Reliability-based design optimization using convex approximations and sequential optimization and reliability assessment method

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Tae Min; Lee, Byung Chai [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of)

    2010-01-15

    In this study, an effective method for reliability-based design optimization (RBDO) is proposed enhancing sequential optimization and reliability assessment (SORA) method by convex approximations. In SORA, reliability estimation and deterministic optimization are performed sequentially. The sensitivity and function value of probabilistic constraint at the most probable point (MPP) are obtained in the reliability analysis loop. In this study, the convex approximations for probabilistic constraint are constructed by utilizing the sensitivity and function value of the probabilistic constraint at the MPP. Hence, the proposed method requires much less function evaluations of probabilistic constraints in the deterministic optimization than the original SORA method. The efficiency and accuracy of the proposed method were verified through numerical examples

  2. A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm

    Science.gov (United States)

    Mohanty, Prases K.; Parhi, Dayal R.

    2014-12-01

    Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.

  3. Optimal dynamic capacity allocation of HVDC interconnections for cross-border exchange of balancing services in presence of uncertainty

    DEFF Research Database (Denmark)

    Delikaraoglou, Stefanos; Pinson, Pierre; Eriksson, Robert

    2015-01-01

    of the power system depends both on the technical parameters of its components, i.e., generators and transmission infrastructure, as well as on the operational practices that make optimal use of the available assets. This work focuses on alternative market designs that enable sharing of cross-border balancing...... and the uncertainty arising from their partial predictability. Considering that the existing setup of the European electricity markets promotes the spatial coordination of neighbouring power systems only on the day-ahead market stage, regional system operators have to rely mainly on their internal balancing resources...... resources between adjacent power systems through High Voltage Direct Current (HVDC) interconnections which provide increased controllability. In this context, we formulate a stochastic market-clearing algorithm that attains full spatio-temporal integration of reserve capacity, day-ahead and balancing...

  4. Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    Po-Chen Cheng

    2015-06-01

    Full Text Available In this paper, an asymmetrical fuzzy-logic-control (FLC-based maximum power point tracking (MPPT algorithm for photovoltaic (PV systems is presented. Two membership function (MF design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V curve of solar cells under standard test conditions (STC. The second method uses the particle swarm optimization (PSO technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively.

  5. Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

    Directory of Open Access Journals (Sweden)

    P. Sabarinath

    2015-01-01

    Full Text Available The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.

  6. Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey

    Directory of Open Access Journals (Sweden)

    Antimo Barbato

    2014-09-01

    Full Text Available The residential sector is currently one of the major contributors to the global energy balance. However, the energy demand of residential users has been so far largely uncontrollable and inelastic with respect to the power grid conditions. With the massive introduction of renewable energy sources and the large variations in energy flows, also the residential sector is required to provide some flexibility in energy use so as to contribute to the stability and efficiency of the electric system. To address this issue, demand management mechanisms can be used to optimally manage the energy resources of customers and their energy demand profiles. A very promising technique is represented by demand-side management (DSM, which consists in a proactive method aimed at making users energy-efficient in the long term. In this paper, we survey the most relevant studies on optimization methods for DSM of residential consumers. Specifically, we review the related literature according to three axes defining contrasting characteristics of the schemes proposed: DSM for individual users versus DSM for cooperative consumers, deterministic DSM versus stochastic DSM and day-ahead DSM versus real-time DSM. Based on this classification, we provide a big picture of the key features of different approaches and techniques and discuss future research directions.

  7. Particle swarm optimization based space debris surveillance network scheduling

    Science.gov (United States)

    Jiang, Hai; Liu, Jing; Cheng, Hao-Wen; Zhang, Yao

    2017-02-01

    The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of in-orbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.

  8. Cooperative Game Study of Airlines Based on Flight Frequency Optimization

    Directory of Open Access Journals (Sweden)

    Wanming Liu

    2014-01-01

    Full Text Available By applying the game theory, the relationship between airline ticket price and optimal flight frequency is analyzed. The paper establishes the payoff matrix of the flight frequency in noncooperation scenario and flight frequency optimization model in cooperation scenario. The airline alliance profit distribution is converted into profit distribution game based on the cooperation game theory. The profit distribution game is proved to be convex, and there exists an optimal distribution strategy. The results show that joining the airline alliance can increase airline whole profit, the change of negotiated prices and cost is beneficial to profit distribution of large airlines, and the distribution result is in accordance with aviation development.

  9. Genetic-evolution-based optimization methods for engineering design

    Science.gov (United States)

    Rao, S. S.; Pan, T. S.; Dhingra, A. K.; Venkayya, V. B.; Kumar, V.

    1990-01-01

    This paper presents the applicability of a biological model, based on genetic evolution, for engineering design optimization. Algorithms embodying the ideas of reproduction, crossover, and mutation are developed and applied to solve different types of structural optimization problems. Both continuous and discrete variable optimization problems are solved. A two-bay truss for maximum fundamental frequency is considered to demonstrate the continuous variable case. The selection of locations of actuators in an actively controlled structure, for minimum energy dissipation, is considered to illustrate the discrete variable case.

  10. Maximum length scale in density based topology optimization

    DEFF Research Database (Denmark)

    Lazarov, Boyan Stefanov; Wang, Fengwen

    2017-01-01

    The focus of this work is on two new techniques for imposing maximum length scale in topology optimization. Restrictions on the maximum length scale provide designers with full control over the optimized structure and open possibilities to tailor the optimized design for broader range...... of manufacturing processes by fulfilling the associated technological constraints. One of the proposed methods is based on combination of several filters and builds on top of the classical density filtering which can be viewed as a low pass filter applied to the design parametrization. The main idea...

  11. Genetic-evolution-based optimization methods for engineering design

    Science.gov (United States)

    Rao, S. S.; Pan, T. S.; Dhingra, A. K.; Venkayya, V. B.; Kumar, V.

    1990-01-01

    This paper presents the applicability of a biological model, based on genetic evolution, for engineering design optimization. Algorithms embodying the ideas of reproduction, crossover, and mutation are developed and applied to solve different types of structural optimization problems. Both continuous and discrete variable optimization problems are solved. A two-bay truss for maximum fundamental frequency is considered to demonstrate the continuous variable case. The selection of locations of actuators in an actively controlled structure, for minimum energy dissipation, is considered to illustrate the discrete variable case.

  12. Optimizing Combination of Units Commitment Based on Improved Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    LAI Yifei; ZHANG Qianhua; JIA Junping

    2007-01-01

    GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms, such as natural selection, genetic recombination and survival of the fittest. By use of coding betterment, the dynamic changes of the mutation rate and the crossover probability, the dynamic choice of subsistence, the reservation of the optimal fitness value, a modified genetic algorithm for optimizing combination of units in thermal power plants is proposed.And through taking examples, test result are analyzed and compared with results of some different algorithms. Numerical results show available value for the unit commitment problem with examples.

  13. CADLIVE optimizer: web-based parameter estimation for dynamic models

    Directory of Open Access Journals (Sweden)

    Inoue Kentaro

    2012-08-01

    Full Text Available Abstract Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models.

  14. Optimal Design of Mountain Bicycle Based on Biomechanics

    Institute of Scientific and Technical Information of China (English)

    卜研; 黄田; 项忠霞; 吴小凡; 陈春

    2010-01-01

    To achieve better cycling performance and vibration comfort of mountain bicycle, the optimization of frame structural parameters and rear suspension scale parameters is investigated based on biomechanics.Firstly, the quadratic sum of rider lower limb muscles stresses is presented as the evaluation criterion of muscle fatigue.By taking the criterion as the objective function, the relative positions of three pivot points of frame are optimized to ensure that the frame structural parameters match the stature o...

  15. Reliability-based design optimization with Cross-Entropy method

    OpenAIRE

    Ghidey, Hiruy

    2015-01-01

    Implementation of the Cross-entropy (CE) method to solve reliability-based design optimization (RBDO) problems was investigated. The emphasis of this implementation method was to solve independently both the reliability and optimization sub-problems within the RBDO problem; therefore, the main aim of this study was to evaluate the performance of the Cross-entropy method in terms of efficiency and accuracy to solve RBDO problems. A numerical approach was followed in which the implementatio...

  16. Perspective texture synthesis based on improved energy optimization.

    Directory of Open Access Journals (Sweden)

    Syed Muhammad Arsalan Bashir

    Full Text Available Perspective texture synthesis has great significance in many fields like video editing, scene capturing etc., due to its ability to read and control global feature information. In this paper, we present a novel example-based, specifically energy optimization-based algorithm, to synthesize perspective textures. Energy optimization technique is a pixel-based approach, so it's time-consuming. We improve it from two aspects with the purpose of achieving faster synthesis and high quality. Firstly, we change this pixel-based technique by replacing the pixel computation with a little patch. Secondly, we present a novel technique to accelerate searching nearest neighborhoods in energy optimization. Using k- means clustering technique to build a search tree to accelerate the search. Hence, we make use of principal component analysis (PCA technique to reduce dimensions of input vectors. The high quality results prove that our approach is feasible. Besides, our proposed algorithm needs shorter time relative to other similar methods.

  17. Algebra-Based Optimization of XML-Extended OLAP Queries

    DEFF Research Database (Denmark)

    Yin, Xuepeng; Pedersen, Torben Bach

    is desirable. This report presents a complete foundation for such OLAP-XML federations. This includes a prototypical query engine, a simplified query semantics based on previous work, and a complete physical algebra which enables precise modeling of the execution tasks of an OLAP-XML query. Effective algebra......-based and cost-based query optimization and implementation are also proposed, as well as the execution techniques. Finally, experiments with the prototypical query engine w.r.t. federation performance, optimization effectiveness, and feasibility suggest that our approach, unlike the physical integration...

  18. Applying BAT Evolutionary Optimization to Image-Based Visual Servoing

    Directory of Open Access Journals (Sweden)

    Marco Perez-Cisneros

    2015-01-01

    Full Text Available This paper presents a predictive control strategy for an image-based visual servoing scheme that employs evolutionary optimization. The visual control task is approached as a nonlinear optimization problem that naturally handles relevant visual servoing constraints such as workspace limitations and visibility restrictions. As the predictive scheme requires a reliable model, this paper uses a local model that is based on the visual interaction matrix and a global model that employs 3D trajectory data extracted from a quaternion-based interpolator. The work assumes a free-flying camera with 6-DOF simulation whose results support the discussion on the constraint handling and the image prediction scheme.

  19. Optimization of wireless sensor networks based on chicken swarm optimization algorithm

    Science.gov (United States)

    Wang, Qingxi; Zhu, Lihua

    2017-05-01

    In order to reduce the energy consumption of wireless sensor network and improve the survival time of network, the clustering routing protocol of wireless sensor networks based on chicken swarm optimization algorithm was proposed. On the basis of LEACH agreement, it was improved and perfected that the points on the cluster and the selection of cluster head using the chicken group optimization algorithm, and update the location of chicken which fall into the local optimum by Levy flight, enhance population diversity, ensure the global search capability of the algorithm. The new protocol avoided the die of partial node of intensive using by making balanced use of the network nodes, improved the survival time of wireless sensor network. The simulation experiments proved that the protocol is better than LEACH protocol on energy consumption, also is better than that of clustering routing protocol based on particle swarm optimization algorithm.

  20. Global Optimization Based on the Hybridization of Harmony Search and Particle Swarm Optimization Methods

    Directory of Open Access Journals (Sweden)

    A. P. Karpenko

    2014-01-01

    Full Text Available We consider a class of stochastic search algorithms of global optimization which in various publications are called behavioural, intellectual, metaheuristic, inspired by the nature, swarm, multi-agent, population, etc. We use the last term.Experience in using the population algorithms to solve challenges of global optimization shows that application of one such algorithm may not always effective. Therefore now great attention is paid to hybridization of population algorithms of global optimization. Hybrid algorithms unite various algorithms or identical algorithms, but with various values of free parameters. Thus efficiency of one algorithm can compensate weakness of another.The purposes of the work are development of hybrid algorithm of global optimization based on known algorithms of harmony search (HS and swarm of particles (PSO, software implementation of algorithm, study of its efficiency using a number of known benchmark problems, and a problem of dimensional optimization of truss structure.We set a problem of global optimization, consider basic algorithms of HS and PSO, give a flow chart of the offered hybrid algorithm called PSO HS , present results of computing experiments with developed algorithm and software, formulate main results of work and prospects of its development.

  1. Cover crop-based ecological weed management: exploration and optimization

    NARCIS (Netherlands)

    Kruidhof, H.M.

    2008-01-01

    Keywords: organic farming, ecologically-based weed management, cover crops, green manure, allelopathy, Secale cereale, Brassica napus, Medicago sativa Cover crop-based ecological weed management: exploration and optimization. In organic farming systems, weed control is recognized as one of the mai

  2. Cover crop-based ecological weed management: exploration and optimization

    NARCIS (Netherlands)

    Kruidhof, H.M.

    2008-01-01

    Keywords: organic farming, ecologically-based weed management, cover crops, green manure, allelopathy, Secale cereale, Brassica napus, Medicago sativa Cover crop-based ecological weed management: exploration and optimization. In organic farming systems, weed control is recognized as one of the

  3. Gradient-based methods for production optimization of oil reservoirs

    Energy Technology Data Exchange (ETDEWEB)

    Suwartadi, Eka

    2012-07-01

    Production optimization for water flooding in the secondary phase of oil recovery is the main topic in this thesis. The emphasis has been on numerical optimization algorithms, tested on case examples using simple hypothetical oil reservoirs. Gradientbased optimization, which utilizes adjoint-based gradient computation, is used to solve the optimization problems. The first contribution of this thesis is to address output constraint problems. These kinds of constraints are natural in production optimization. Limiting total water production and water cut at producer wells are examples of such constraints. To maintain the feasibility of an optimization solution, a Lagrangian barrier method is proposed to handle the output constraints. This method incorporates the output constraints into the objective function, thus avoiding additional computations for the constraints gradient (Jacobian) which may be detrimental to the efficiency of the adjoint method. The second contribution is the study of the use of second-order adjoint-gradient information for production optimization. In order to speedup convergence rate in the optimization, one usually uses quasi-Newton approaches such as BFGS and SR1 methods. These methods compute an approximation of the inverse of the Hessian matrix given the first-order gradient from the adjoint method. The methods may not give significant speedup if the Hessian is ill-conditioned. We have developed and implemented the Hessian matrix computation using the adjoint method. Due to high computational cost of the Newton method itself, we instead compute the Hessian-timesvector product which is used in a conjugate gradient algorithm. Finally, the last contribution of this thesis is on surrogate optimization for water flooding in the presence of the output constraints. Two kinds of model order reduction techniques are applied to build surrogate models. These are proper orthogonal decomposition (POD) and the discrete empirical interpolation method (DEIM

  4. GPU-Monte Carlo based fast IMRT plan optimization

    Directory of Open Access Journals (Sweden)

    Yongbao Li

    2014-03-01

    Full Text Available Purpose: Intensity-modulated radiation treatment (IMRT plan optimization needs pre-calculated beamlet dose distribution. Pencil-beam or superposition/convolution type algorithms are typically used because of high computation speed. However, inaccurate beamlet dose distributions, particularly in cases with high levels of inhomogeneity, may mislead optimization, hindering the resulting plan quality. It is desire to use Monte Carlo (MC methods for beamlet dose calculations. Yet, the long computational time from repeated dose calculations for a number of beamlets prevents this application. It is our objective to integrate a GPU-based MC dose engine in lung IMRT optimization using a novel two-steps workflow.Methods: A GPU-based MC code gDPM is used. Each particle is tagged with an index of a beamlet where the source particle is from. Deposit dose are stored separately for beamlets based on the index. Due to limited GPU memory size, a pyramid space is allocated for each beamlet, and dose outside the space is neglected. A two-steps optimization workflow is proposed for fast MC-based optimization. At first step, a rough dose calculation is conducted with only a few number of particle per beamlet. Plan optimization is followed to get an approximated fluence map. In the second step, more accurate beamlet doses are calculated, where sampled number of particles for a beamlet is proportional to the intensity determined previously. A second-round optimization is conducted, yielding the final result.Results: For a lung case with 5317 beamlets, 105 particles per beamlet in the first round, and 108 particles per beam in the second round are enough to get a good plan quality. The total simulation time is 96.4 sec.Conclusion: A fast GPU-based MC dose calculation method along with a novel two-step optimization workflow are developed. The high efficiency allows the use of MC for IMRT optimizations.--------------------------------Cite this article as: Li Y, Tian Z

  5. SAR Image Segmentation Based On Hybrid PSOGSA Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Amandeep Kaur

    2014-09-01

    Full Text Available Image segmentation is useful in many applications. It can identify the regions of interest in a scene or annotate the data. It categorizes the existing segmentation algorithm into region-based segmentation, data clustering, and edge-base segmentation. Region-based segmentation includes the seeded and unseeded region growing algorithms, the JSEG, and the fast scanning algorithm. Due to the presence of speckle noise, segmentation of Synthetic Aperture Radar (SAR images is still a challenging problem. We proposed a fast SAR image segmentation method based on Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA. In this method, threshold estimation is regarded as a search procedure that examinations for an appropriate value in a continuous grayscale interval. Hence, PSO-GSA algorithm is familiarized to search for the optimal threshold. Experimental results indicate that our method is superior to GA based, AFS based and ABC based methods in terms of segmentation accuracy, segmentation time, and Thresholding.

  6. Segment-Based Predominant Learning Swarm Optimizer for Large-Scale Optimization.

    Science.gov (United States)

    Yang, Qiang; Chen, Wei-Neng; Gu, Tianlong; Zhang, Huaxiang; Deng, Jeremiah D; Li, Yun; Zhang, Jun

    2016-10-24

    Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is proposed to randomly divide the whole dimensions into segments. During update, variables in different segments are evolved by learning from different exemplars while the ones in the same segment are evolved by the same exemplar. Second, to accelerate search speed and enhance search diversity, a predominant learning strategy is also proposed, which lets several predominant particles guide the update of a particle with each predominant particle responsible for one segment of dimensions. By combining these two learning strategies together, SPLSO evolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities. Extensive experiments are conducted on two large-scale benchmark function sets to investigate the influence of each algorithmic component and comparisons with several state-of-the-art meta-heuristic algorithms dealing with large-scale problems demonstrate the competitive efficiency and effectiveness of the proposed optimizer. Further the scalability of the optimizer to solve problems with dimensionality up to 2000 is also verified.

  7. Statistical Testing of Optimality Conditions in Multiresponse Simulation-based Optimization (Revision of 2005-81)

    NARCIS (Netherlands)

    Bettonvil, B.W.M.; Del Castillo, E.; Kleijnen, J.P.C.

    2007-01-01

    This paper studies simulation-based optimization with multiple outputs. It assumes that the simulation model has one random objective function and must satisfy given constraints on the other random outputs. It presents a statistical procedure for test- ing whether a specific input combination

  8. Statistical Testing of Optimality Conditions in Multiresponse Simulation-based Optimization (Revision of 2005-81)

    NARCIS (Netherlands)

    Bettonvil, B.W.M.; Del Castillo, E.; Kleijnen, J.P.C.

    2007-01-01

    This paper studies simulation-based optimization with multiple outputs. It assumes that the simulation model has one random objective function and must satisfy given constraints on the other random outputs. It presents a statistical procedure for test- ing whether a specific input combination (propo

  9. Structural Optimization of Slender Robot Arm Based on Sensitivity Analysis

    Directory of Open Access Journals (Sweden)

    Zhong Luo

    2012-01-01

    Full Text Available An effective structural optimization method based on a sensitivity analysis is proposed to optimize the variable section of a slender robot arm. The structure mechanism and the operating principle of a polishing robot are introduced firstly, and its stiffness model is established. Then, a design of sensitivity analysis method and a sequential linear programming (SLP strategy are developed. At the beginning of the optimization, the design sensitivity analysis method is applied to select the sensitive design variables which can make the optimized results more efficient and accurate. In addition, it can also be used to determine the scale of moving step which will improve the convergency during the optimization process. The design sensitivities are calculated using the finite difference method. The search for the final optimal structure is performed using the SLP method. Simulation results show that the proposed structure optimization method is effective in enhancing the stiffness of the robot arm regardless of the robot arm suffering either a constant force or variable forces.

  10. Optimal image-fusion method based on nonsubsampled contourlet transform

    Science.gov (United States)

    Dou, Jianfang; Li, Jianxun

    2012-10-01

    The optimization of image fusion is researched. Based on the properties of nonsubsampled contourlet transform (NSCT), shift invariance, multiscale and multidirectional expansion, the fusion parameters of the multiscale decompostion scheme is optimized. In order to meet the requirement of feedback optimization, a new image fusion quality metric of image quality index normalized edge association (IQI-NEA) is built. A polynomial model is adopted to establish the relationship between the IQI_NEA metric and several decomposition levels. The optimal fusion includes four steps. First, the source images are decomposed in NSCT domain for several given levels. Second, principal component analysis is adopted to fuse the low frequency coefficients and the maximum fusion rule is utilized to fuse the high frequency coefficients to obtain the fused coefficients and the fused result is reconstructed from the obtained fused coefficients. Third, calculate the fusion quality metric IQI_NEA for the source images and fused images. Finally, the optimal fused image and optimal level are obtained through extremum properties of polynomials function. The visual and statistical results show that the proposed method has optimized the fusion performance compared to the existing fusion schemes, in terms of the visual effects and quantitative fusion evaluation indexes.

  11. A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization.

    Science.gov (United States)

    Zhang, Yong-Feng; Chiang, Hsiao-Dong

    2016-06-20

    A novel three-stage methodology, termed the "consensus-based particle swarm optimization (PSO)-assisted Trust-Tech methodology," to find global optimal solutions for nonlinear optimization problems is presented. It is composed of Trust-Tech methods, consensus-based PSO, and local optimization methods that are integrated to compute a set of high-quality local optimal solutions that can contain the global optimal solution. The proposed methodology compares very favorably with several recently developed PSO algorithms based on a set of small-dimension benchmark optimization problems and 20 large-dimension test functions from the CEC 2010 competition. The analytical basis for the proposed methodology is also provided. Experimental results demonstrate that the proposed methodology can rapidly obtain high-quality optimal solutions that can contain the global optimal solution. The scalability of the proposed methodology is promising.

  12. Trust regions in Kriging-based optimization with expected improvement

    Science.gov (United States)

    Regis, Rommel G.

    2016-06-01

    The Kriging-based Efficient Global Optimization (EGO) method works well on many expensive black-box optimization problems. However, it does not seem to perform well on problems with steep and narrow global minimum basins and on high-dimensional problems. This article develops a new Kriging-based optimization method called TRIKE (Trust Region Implementation in Kriging-based optimization with Expected improvement) that implements a trust-region-like approach where each iterate is obtained by maximizing an Expected Improvement (EI) function within some trust region. This trust region is adjusted depending on the ratio of the actual improvement to the EI. This article also develops the Kriging-based CYCLONE (CYClic Local search in OptimizatioN using Expected improvement) method that uses a cyclic pattern to determine the search regions where the EI is maximized. TRIKE and CYCLONE are compared with EGO on 28 test problems with up to 32 dimensions and on a 36-dimensional groundwater bioremediation application in appendices supplied as an online supplement available at http://dx.doi.org/10.1080/0305215X.2015.1082350. The results show that both algorithms yield substantial improvements over EGO and they are competitive with a radial basis function method.

  13. APPROACH ON INTELLIGENT OPTIMIZATION DESIGN BASED ON COMPOUND KNOWLEDGE

    Institute of Scientific and Technical Information of China (English)

    Yao Jianchu; Zhou Ji; Yu Jun

    2003-01-01

    A concept of an intelligent optimal design approach is proposed, which is organized by a kind of compound knowledge model. The compound knowledge consists of modularized quantitative knowledge, inclusive experience knowledge and case-based sample knowledge. By using this compound knowledge model, the abundant quantity information of mathematical programming and the symbolic knowledge of artificial intelligence can be united together in this model. The intelligent optimal design model based on such a compound knowledge and the automatically generated decomposition principles based on it are also presented. Practically, it is applied to the production planning, process schedule and optimization of production process of a refining & chemical work and a great profit is achieved. Specially, the methods and principles are adaptable not only to continuous process industry, but also to discrete manufacturing one.

  14. Mode-Tracking Based Stationary-Point Optimization

    CERN Document Server

    Bergeler, Maike; Reiher, Markus

    2014-01-01

    In this work, we present a transition-state optimization protocol based on the Mode-Tracking algorithm [J. Chem. Phys. 118 (2003) 1634]. By calculating only the eigenvector of interest instead of diagonalizing the full Hessian matrix and performing an eigenvector following search based on the selectively calculated vector, we can efficiently optimize transition-state structures. The initial guess structures and eigenvectors are either chosen from a linear interpolation between the reactant and product structures, from a nudged-elastic band search, from a constrained-optimization scan, or from the minimum-energy structures. Alternatively, initial guess vectors based on chemical intuition may be defined. We then iteratively refine the selected vectors by the Davidson subspace iteration technique. This procedure accelerates finding transition states for large molecules of a few hundred atoms. It is also beneficial in cases where the starting structure is very different from the transition-state structure or wher...

  15. Optimizing medical data quality based on multiagent web service framework.

    Science.gov (United States)

    Wu, Ching-Seh; Khoury, Ibrahim; Shah, Hemant

    2012-07-01

    One of the most important issues in e-healthcare information systems is to optimize the medical data quality extracted from distributed and heterogeneous environments, which can extremely improve diagnostic and treatment decision making. This paper proposes a multiagent web service framework based on service-oriented architecture for the optimization of medical data quality in the e-healthcare information system. Based on the design of the multiagent web service framework, an evolutionary algorithm (EA) for the dynamic optimization of the medical data quality is proposed. The framework consists of two main components; first, an EA will be used to dynamically optimize the composition of medical processes into optimal task sequence according to specific quality attributes. Second, a multiagent framework will be proposed to discover, monitor, and report any inconstancy between the optimized task sequence and the actual medical records. To demonstrate the proposed framework, experimental results for a breast cancer case study are provided. Furthermore, to show the unique performance of our algorithm, a comparison with other works in the literature review will be presented.

  16. Prediction of RNA Secondary Structure Based on Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    LIU Yuan-ning; DONG Hao; ZHANG Hao; WANG Gang; LI Zhi; CHEN Hui-ling

    2011-01-01

    A novel method for the prediction of RNA secondary structure was proposed based on the particle swarm optimization(PSO). PSO is known to be effective in solving many different types of optimization problems and known for being able to approximate the global optimal results in the solution space. We designed an efficient objective function according to the minimum free energy, the number of selected stems and the average length of selected stems. We calculated how many legal stems there were in the sequence, and selected some of them to obtain an optimal result using PSO in the right of the objective function. A method based on the improved particle swarm optimization(IPSO) was proposed to predict RNA secondary structure, which consisted of three stages. The first stage was applied to e ncoding the source sequences, and to exploring all the legal stems. Then, a set of encoded stems were created in order to prepare input data for the second stage. In the second stage, IPSO was responsible for structure selection. At last, the optimal result was obtained from the secondary structures selected via IPSO. Nine sequences from the comparative RNA website were selected for the evaluation of the proposed method. Compared with other six methods, the proposed method decreased the complexity and enhanced the sensitivity and specificity on the basis of the experiment results.

  17. Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Mohammed Hasan Abdulameer

    2014-01-01

    Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

  18. Inversion method based on stochastic optimization for particle sizing.

    Science.gov (United States)

    Sánchez-Escobar, Juan Jaime; Barbosa-Santillán, Liliana Ibeth; Vargas-Ubera, Javier; Aguilar-Valdés, Félix

    2016-08-01

    A stochastic inverse method is presented based on a hybrid evolutionary optimization algorithm (HEOA) to retrieve a monomodal particle-size distribution (PSD) from the angular distribution of scattered light. By solving an optimization problem, the HEOA (with the Fraunhofer approximation) retrieves the PSD from an intensity pattern generated by Mie theory. The analyzed light-scattering pattern can be attributed to unimodal normal, gamma, or lognormal distribution of spherical particles covering the interval of modal size parameters 46≤α≤150. The HEOA ensures convergence to the near-optimal solution during the optimization of a real-valued objective function by combining the advantages of a multimember evolution strategy and locally weighted linear regression. The numerical results show that our HEOA can be satisfactorily applied to solve the inverse light-scattering problem.

  19. Engineering Design Optimization Based on Intelligent Response Surface Methodology

    Institute of Scientific and Technical Information of China (English)

    SONG Guo-hui; WU Yu; LI Cong-xin

    2008-01-01

    An intelligent response surface methodology (IRSM) was proposed to achieve the most competitivemetal forming products, in which artificial intelligence technologies are introduced into the optimization process.It is used as simple and inexpensive replacement for computationally expensive simulation model. In IRSM,the optimal design space can be reduced greatly without any prior information about function distribution.Also, by identifying the approximation error region, new design points can be supplemented correspondingly toimprove the response surface model effectively. The procedure is iterated until the accuracy reaches the desiredthreshold value. Thus, the global optimization can be performed based on this substitute model. Finally, wepresent an optimization design example about roll forming of a "U" channel product.

  20. Planar straightness error evaluation based on particle swarm optimization

    Science.gov (United States)

    Mao, Jian; Zheng, Huawen; Cao, Yanlong; Yang, Jiangxin

    2006-11-01

    The straightness error generally refers to the deviation between an actual line and an ideal line. According to the characteristics of planar straightness error evaluation, a novel method to evaluate planar straightness errors based on the particle swarm optimization (PSO) is proposed. The planar straightness error evaluation problem is formulated as a nonlinear optimization problem. According to minimum zone condition the mathematical model of planar straightness together with the optimal objective function and fitness function is developed. Compared with the genetic algorithm (GA), the PSO algorithm has some advantages. It is not only implemented without crossover and mutation but also has fast congruence speed. Moreover fewer parameters are needed to set up. The results show that the PSO method is very suitable for nonlinear optimization problems and provides a promising new method for straightness error evaluation. It can be applied to deal with the measured data of planar straightness obtained by the three-coordinates measuring machines.

  1. Optimization and Design of Wideband Antenna Based on Q Factor

    Directory of Open Access Journals (Sweden)

    Han Liu

    2015-01-01

    Full Text Available A wideband antenna is designed based on Q factor in this paper. Firstly, the volume-surface integral equations (VSIEs and self-adaptive differential evolution algorithm (DEA are introduced as the basic theories to optimize antennas. Secondly, we study the computation of Q of arbitrary shaped structures, aiming at designing an antenna with maximum bandwidth by minimizing the Q of the antenna. This method is much more efficient for only Q values at specific frequency points that are computed, which avoids optimizing bandwidth directly. Thirdly, an integrated method combining the above method with VSIEs and self-adaptive DEA is employed to optimize the wideband antenna, extending its bandwidth from 11.5~16.5 GHz to 7~20 GHz. Lastly, the optimized antenna is fabricated and measured. The measured results are consistent with the simulated results, demonstrating the feasibility and effectiveness of the proposed method.

  2. Routing Optimization Based on Taboo Search Algorithm for Logistic Distribution

    Directory of Open Access Journals (Sweden)

    Hongxue Yang

    2014-04-01

    Full Text Available Along with the widespread application of the electronic commerce in the modern business, the logistic distribution has become increasingly important. More and more enterprises recognize that the logistic distribution plays an important role in the process of production and sales. A good routing for logistic distribution can cut down transport cost and improve efficiency. In order to cut down transport cost and improve efficiency, a routing optimization based on taboo search for logistic distribution is proposed in this paper. Taboo search is a metaheuristic search method to perform local search used for logistic optimization. The taboo search is employed to accelerate convergence and the aspiration criterion is combined with the heuristics algorithm to solve routing optimization. Simulation experimental results demonstrate that the optimal routing in the logistic distribution can be quickly obtained by the taboo search algorithm

  3. Application of Teaching Learning Based Optimization in antenna designing

    Directory of Open Access Journals (Sweden)

    S. Dwivedi

    2015-07-01

    Full Text Available Numerous optimization techniques are studied and applied on antenna designs to optimize various performance parameters. Authors used many Multiple Attributes Decision Making (MADM methods, which include, Weighted Sum Method (WSM, Weighted Product Method (WPM, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS, Analytic Hierarchy Process (AHP, ELECTRE, etc. Of these many MADM methods, TOPSIS and AHP are more widely used decision making methods. Both TOPSIS and AHP are logical decision making approaches and deal with the problem of choosing an alternative from a set of alternatives which are characterized in terms of some attributes. Analytic Hierarchy Process (AHP is explained in detail and compared with WSM and WPM. Authors fi- nally used Teaching-Learning-Based Optimization (TLBO technique; which is a novel method for constrained antenna design optimization problems.

  4. Optimization-based topology identification of complex networks

    Institute of Scientific and Technical Information of China (English)

    Tang Sheng-Xue; Chen Li; He Yi-Gang

    2011-01-01

    In many cases,the topological structures of a complex network are unknown or uncertain,and it is of significance to identify the exact topological structure.An optimization-based method of identifying the topological structure of a complex network is proposed in this paper.Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network.Then,an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem.Compared with the previous adaptive synchronizationbased method,the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks.In some cases where the states of a complex network are only partially observable,the exact topological structure of a network can also be identified by using the proposed method.Finally,numerical simulations are provided to show the effectiveness of the proposed method.

  5. Teaching learning based optimization algorithm and its engineering applications

    CERN Document Server

    Rao, R Venkata

    2016-01-01

    Describing a new optimization algorithm, the “Teaching-Learning-Based Optimization (TLBO),” in a clear and lucid style, this book maximizes reader insights into how the TLBO algorithm can be used to solve continuous and discrete optimization problems involving single or multiple objectives. As the algorithm operates on the principle of teaching and learning, where teachers influence the quality of learners’ results, the elitist version of TLBO algorithm (ETLBO) is described along with applications of the TLBO algorithm in the fields of electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics and biotechnology. The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms.

  6. Optimal weight based on energy imbalance and utility maximization

    Science.gov (United States)

    Sun, Ruoyan

    2016-01-01

    This paper investigates the optimal weight for both male and female using energy imbalance and utility maximization. Based on the difference of energy intake and expenditure, we develop a state equation that reveals the weight gain from this energy gap. We ​construct an objective function considering food consumption, eating habits and survival rate to measure utility. Through applying mathematical tools from optimal control methods and qualitative theory of differential equations, we obtain some results. For both male and female, the optimal weight is larger than the physiologically optimal weight calculated by the Body Mass Index (BMI). We also study the corresponding trajectories to steady state weight respectively. Depending on the value of a few parameters, the steady state can either be a saddle point with a monotonic trajectory or a focus with dampened oscillations.

  7. EUD-based biological optimization for carbon ion therapy

    Energy Technology Data Exchange (ETDEWEB)

    Brüningk, Sarah C., E-mail: sarah.brueningk@icr.ac.uk; Kamp, Florian; Wilkens, Jan J. [Department of Radiation Oncology, Technische Universität München, Klinikum rechts der Isar, Ismaninger Str. 22, München 81675, Germany and Physik-Department, Technische Universität München, James-Franck-Str. 1, Garching 85748 (Germany)

    2015-11-15

    Purpose: Treatment planning for carbon ion therapy requires an accurate modeling of the biological response of each tissue to estimate the clinical outcome of a treatment. The relative biological effectiveness (RBE) accounts for this biological response on a cellular level but does not refer to the actual impact on the organ as a whole. For photon therapy, the concept of equivalent uniform dose (EUD) represents a simple model to take the organ response into account, yet so far no formulation of EUD has been reported that is suitable to carbon ion therapy. The authors introduce the concept of an equivalent uniform effect (EUE) that is directly applicable to both ion and photon therapies and exemplarily implemented it as a basis for biological treatment plan optimization for carbon ion therapy. Methods: In addition to a classical EUD concept, which calculates a generalized mean over the RBE-weighted dose distribution, the authors propose the EUE to simplify the optimization process of carbon ion therapy plans. The EUE is defined as the biologically equivalent uniform effect that yields the same probability of injury as the inhomogeneous effect distribution in an organ. Its mathematical formulation is based on the generalized mean effect using an effect-volume parameter to account for different organ architectures and is thus independent of a reference radiation. For both EUD concepts, quadratic and logistic objective functions are implemented into a research treatment planning system. A flexible implementation allows choosing for each structure between biological effect constraints per voxel and EUD constraints per structure. Exemplary treatment plans are calculated for a head-and-neck patient for multiple combinations of objective functions and optimization parameters. Results: Treatment plans optimized using an EUE-based objective function were comparable to those optimized with an RBE-weighted EUD-based approach. In agreement with previous results from photon

  8. Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization

    Directory of Open Access Journals (Sweden)

    Na Tian

    2015-01-01

    Full Text Available A study on pareto-ranking based quantum-behaved particle swarm optimization (QPSO for multiobjective optimization problems is presented in this paper. During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen. The comparison between different elitist selection strategies (preference order, sigma value, and random selection is performed on four benchmark functions and two metrics. The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives. Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.

  9. Optimization of fused deposition modeling process using teaching-learning-based optimization algorithm

    Directory of Open Access Journals (Sweden)

    R. Venkata Rao

    2016-03-01

    Full Text Available The performance of rapid prototyping (RP processes is often measured in terms of build time, product quality, dimensional accuracy, cost of production, mechanical and tribological properties of the models and energy consumed in the process. The success of any RP process in terms of these performance measures entails selection of the optimum combination of the influential process parameters. Thus, in this work the single-objective and multi-objective optimization problems of a widely used RP process, namely, fused deposition modeling (FDM, are formulated, and the same are solved using the teaching-learning-based optimization (TLBO algorithm and non-dominated Sorting TLBO (NSTLBO algorithm, respectively. The results of the TLBO algorithm are compared with those obtained using genetic algorithm (GA, and quantum behaved particle swarm optimization (QPSO algorithm. The TLBO algorithm showed better performance as compared to GA and QPSO algorithms. The NSTLBO algorithm proposed to solve the multi-objective optimization problems of the FDM process in this work is a posteriori version of the TLBO algorithm. The NSTLBO algorithm is incorporated with non-dominated sorting concept and crowding distance assignment mechanism to obtain a dense set of Pareto optimal solutions in a single simulation run. The results of the NSTLBO algorithm are compared with those obtained using non-dominated sorting genetic algorithm (NSGA-II and the desirability function approach. The Pareto-optimal set of solutions for each problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for the FDM process.

  10. Rapid Optimal Generation Algorithm for Terrain Following Trajectory Based on Optimal Control

    Institute of Scientific and Technical Information of China (English)

    杨剑影; 张海; 谢邦荣; 尹健

    2004-01-01

    Based on the optimal control theory, a 3-dimensionnal direct generation algorithm is proposed for anti-ground low altitude penetration tasks under complex terrain. By optimizing the terrain following(TF) objective function,terrain coordinate system, missile dynamic model and control vector, the TF issue is turning into the improved optimal control problem whose mathmatical model is simple and need not solve the second order terrain derivative. Simulation results prove that this method is reasonable and feasible. The TF precision is in the scope from 0.3 m to 3.0 m,and the planning time is less than 30 min. This method have the strongpionts such as rapidness, precision and has great application value.

  11. A single loop reliability-based design optimization using EPM and MPP-based PSO

    OpenAIRE

    Liao, Kuo-Wei; Ivan,Gautama

    2014-01-01

    A reliability-based design optimization (RBDO) incorporates a probabilistic analysis with an optimization technique to find a best design within a reliable design space. However, the computational cost of an RBDO task is often expensive compared to a deterministic optimization, which is mainly due to the reliability analysis performed inside the optimization loop. Theoretically, the reliability of a given design point can be obtained through a multidimensional integration. Integration with mu...

  12. Reliability-based design optimization strategies based on FORM: a review

    OpenAIRE

    Lopez, Rafael Holdorf; BECK, André Teófilo

    2012-01-01

    In deterministic optimization, the uncertainties of the structural system (i.e. dimension, model, material, loads, etc) are not explicitly taken into account. Hence, resulting optimal solutions may lead to reduced reliability levels. The objective of reliability based design optimization (RBDO) is to optimize structures guaranteeing that a minimum level of reliability, chosen a priori by the designer, is maintained. Since reliability analysis using the First Order Reliability Method (FORM) is...

  13. Optimization of Component Based Software Engineering Model Using Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar

    2014-10-01

    Full Text Available The goal of Component Based Software Engineering (CBSE is to deliver high quality, more reliable and more maintainable software systems in a shorter time and within limited budget by reusing and combining existing quality components. A high quality system can be achieved by using quality components, framework and integration process that plays a significant role. So, techniques and methods used for quality assurance and assessment of a component based system is different from those of the traditional software engineering methodology. In this paper, we are presenting a model for optimizing Chidamber and Kemerer (CK metric values of component-based software. A deep analysis of a series of CK metrics of the software components design patterns is done and metric values are drawn from them. By using unsupervised neural network- Self Organizing Map, we have proposed a model that provides an optimized model for Software Component engineering model based on reusability that depends on CK metric values. Average, standard deviated and optimized values for the CK metric are compared and evaluated to show the optimized reusability of component based model.

  14. ODVBA: optimally-discriminative voxel-based analysis.

    Science.gov (United States)

    Zhang, Tianhao; Davatzikos, Christos

    2011-08-01

    Gaussian smoothing of images prior to applying voxel-based statistics is an important step in voxel-based analysis and statistical parametric mapping (VBA-SPM) and is used to account for registration errors, to Gaussianize the data and to integrate imaging signals from a region around each voxel. However, it has also become a limitation of VBA-SPM based methods, since it is often chosen empirically and lacks spatial adaptivity to the shape and spatial extent of the region of interest, such as a region of atrophy or functional activity. In this paper, we propose a new framework, named optimally-discriminative voxel-based analysis (ODVBA), for determining the optimal spatially adaptive smoothing of images, followed by applying voxel-based group analysis. In ODVBA, nonnegative discriminative projection is applied regionally to get the direction that best discriminates between two groups, e.g., patients and controls; this direction is equivalent to local filtering by an optimal kernel whose coefficients define the optimally discriminative direction. By considering all the neighborhoods that contain a given voxel, we then compose this information to produce the statistic for each voxel. Finally, permutation tests are used to obtain a statistical parametric map of group differences. ODVBA has been evaluated using simulated data in which the ground truth is known and with data from an Alzheimer's disease (AD) study. The experimental results have shown that the proposed ODVBA can precisely describe the shape and location of structural abnormality.

  15. Design Optimization of Mechanical Components Using an Enhanced Teaching-Learning Based Optimization Algorithm with Differential Operator

    Directory of Open Access Journals (Sweden)

    B. Thamaraikannan

    2014-01-01

    Full Text Available This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimization problems are considered in this research: firstly, to optimize the weight in a belt-pulley drive, secondly, to optimize the volume in a closed coil helical spring, and finally to optimize the weight in a hollow shaft. have been demonstrated. Simulation result on the optimization (mechanical components problems reveals the ability of the proposed methodology to find better optimal solutions compared to other optimization algorithms.

  16. Length scale and manufacturability in density-based topology optimization

    DEFF Research Database (Denmark)

    Lazarov, Boyan Stefanov; Wang, Fengwen; Sigmund, Ole

    2016-01-01

    Since its original introduction in structural design, density-based topology optimization has been applied to a number of other fields such as microelectromechanical systems, photonics, acoustics and fluid mechanics. The methodology has been well accepted in industrial design processes where it c......, well-defined designs with robust performances. The overview discusses the limitations, the advantages and the associated computational costs. The review is completed with optimized designs for minimum compliance, mechanism design and heat transfer.......Since its original introduction in structural design, density-based topology optimization has been applied to a number of other fields such as microelectromechanical systems, photonics, acoustics and fluid mechanics. The methodology has been well accepted in industrial design processes where it can...

  17. Structural eigenfrequency optimization based on local sub-domain "frequencies"

    DEFF Research Database (Denmark)

    Pedersen, Pauli; Pedersen, Niels Leergaard

    2013-01-01

    The engineering approach of fully stressed design is a practical tool with a theoretical foundation. The analog approach to structural eigenfrequency optimization is presented here with its theoretical foundation. A numerical redesign procedure is proposed and illustrated with examples.......For the ideal case, an optimality criterion is fulfilled if the design have the same sub-domain ”frequency” (local Rayleigh quotient). Sensitivity analysis shows an important relation between squared system eigenfrequency and squared local sub-domain frequency for a given eigenmode. Higher order...... eigenfrequencies may also be controlled in this manner.The presented examples are based on 2D finite element models with the use of subspace iteration for analysis and a recursive design procedure based on the derived optimality condition. The design that maximize a frequency depend on the total amount...

  18. ENERGY OPTIMIZATION IN CLUSTER BASED WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    T. SHANKAR

    2014-04-01

    Full Text Available Wireless sensor networks (WSN are made up of sensor nodes which are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue. To prolong the networks lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. Optimizing energy consumption is the main concern for designing and planning the operation of the WSN. Clustering technique is one of the methods utilized to extend lifetime of the network by applying data aggregation and balancing energy consumption among sensor nodes of the network. This paper proposed new version of Low Energy Adaptive Clustering Hierarchy (LEACH, protocols called Advanced Optimized Low Energy Adaptive Clustering Hierarchy (AOLEACH, Optimal Deterministic Low Energy Adaptive Clustering Hierarchy (ODLEACH, and Varying Probability Distance Low Energy Adaptive Clustering Hierarchy (VPDL combination with Shuffled Frog Leap Algorithm (SFLA that enables selecting best optimal adaptive cluster heads using improved threshold energy distribution compared to LEACH protocol and rotating cluster head position for uniform energy dissipation based on energy levels. The proposed algorithm optimizing the life time of the network by increasing the first node death (FND time and number of alive nodes, thereby increasing the life time of the network.

  19. Robust Collaborative Optimization Method Based on Dual-response Surface

    Institute of Scientific and Technical Information of China (English)

    WANG Wei; FAN Wenhui; CHANG Tianqing; YUAN Yuming

    2009-01-01

    A novel method for robust collaborative design of complex products based on dual-response surface (DRS-RCO) is proposed to solve multidisciplinary design optimization (MDO) problems under uncertainty. Collaborative optimization (CO) which decomposes the whole system into a double-level nonlinear optimization problem is widely Accepted as an efficient method to solve MDO problems. In order to improve the quality of complex product in design process, robust collaborative optimization (RCO) is developed to solve those problems under uncertain conditions. RCO does opfmiTation on the linear sum of mean and standard deviation of objective function and gets an optimal solution with high robustnmess. Response surfaces method is an important way to do approximation in robust design. DRS-RCO is an improved RCO method in which dual-response surface replaces system uncertainty analysis module of CO. The dual-response surface is the approximate model of mean and standard deviation of objective function respectively. In DRS-RCO, All the information of subsystems is included in dual-response surfaces. As an additional item, the standard deviation of objective function is added to the subsystem optimization. This item guarantee both the mean and standard deviation of this subsystem is reaching the minima at the same time. Finally, a test problem with two coupled subsystems is conducted to verify the feasibility and effectiveness of DRS-RCO.

  20. Electrochemical model based charge optimization for lithium-ion batteries

    Science.gov (United States)

    Pramanik, Sourav; Anwar, Sohel

    2016-05-01

    In this paper, we propose the design of a novel optimal strategy for charging the lithium-ion battery based on electrochemical battery model that is aimed at improved performance. A performance index that aims at minimizing the charging effort along with a minimum deviation from the rated maximum thresholds for cell temperature and charging current has been defined. The method proposed in this paper aims at achieving a faster charging rate while maintaining safe limits for various battery parameters. Safe operation of the battery is achieved by including the battery bulk temperature as a control component in the performance index which is of critical importance for electric vehicles. Another important aspect of the performance objective proposed here is the efficiency of the algorithm that would allow higher charging rates without compromising the internal electrochemical kinetics of the battery which would prevent abusive conditions, thereby improving the long term durability. A more realistic model, based on battery electro-chemistry has been used for the design of the optimal algorithm as opposed to the conventional equivalent circuit models. To solve the optimization problem, Pontryagins principle has been used which is very effective for constrained optimization problems with both state and input constraints. Simulation results show that the proposed optimal charging algorithm is capable of shortening the charging time of a lithium ion cell while maintaining the temperature constraint when compared with the standard constant current charging. The designed method also maintains the internal states within limits that can avoid abusive operating conditions.

  1. Adjoint-based optimization of a foam EOR process

    NARCIS (Netherlands)

    Namdar Zanganeh, M.; Kraaijevanger, J.F.B.M.; Buurman, H.W.; Jansen, J.D.; Rossen, W.R.

    2012-01-01

    We apply adjoint-based optimization to a Surfactant-Alternating-Gas foam process using a linear foam model introducing gradual changes in gas mobility and a nonlinear foam model giving abrupt changes in gas mobility as function of oil and water saturations and surfactant concentration. For the

  2. Interactive Reliability-Based Optimization of Structural Systems

    DEFF Research Database (Denmark)

    Pedersen, Claus

    In order to introduce the basic concepts within the field of reliability-based structural optimization problems, this chapter is devoted to a brief outline of the basic theories. Therefore, this chapter is of a more formal nature and used as a basis for the remaining parts of the thesis. In secti...

  3. Reliability-Based Shape Optimization using Stochastic Finite Element Methods

    DEFF Research Database (Denmark)

    Enevoldsen, Ib; Sørensen, John Dalsgaard; Sigurdsson, G.

    1991-01-01

    Application of first-order reliability methods FORM (see Madsen, Krenk & Lind [8)) in structural design problems has attracted growing interest in recent years, see e.g. Frangopol [4), Murotsu, Kishi, Okada, Yonezawa & Taguchi [9) and Sørensen [14). In probabilistically based optimal design...

  4. MVMO-based approach for optimal placement and tuning of ...

    African Journals Online (AJOL)

    DR OKE

    This paper introduces an approach based on the Swarm Variant of the ... comprehensive learning particle swarm optimization (CLPSO), genetic ... DOI: http://dx.doi.org/10.4314/ijest.v7i3.12S ..... machine power systems: a comparative study.

  5. Runtime Optimizations for Tree-Based Machine Learning Models

    NARCIS (Netherlands)

    N. Asadi; J.J.P. Lin (Jimmy); A.P. de Vries (Arjen)

    2014-01-01

    htmlabstractTree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression

  6. Reality based optimization of steel monopod offshore-towers

    NARCIS (Netherlands)

    Vrouwenvelder, A.C.W.M.

    2008-01-01

    In this work, the implementation of reliability-based optimization (RBO) of a circular steel monopod-offshore-tower with constant and variable diameters (represented by segmentations) and thicknesses is presented. The tower is subjected to the extreme wave loading. For this purpose, the deterministi

  7. Reliability-Based Optimization of Series Systems of Parallel Systems

    DEFF Research Database (Denmark)

    Enevoldsen, I.; Sørensen, John Dalsgaard

    1993-01-01

    ) a sequential formulation based on optimality criteria; and (4) a sequential formulation including a new so-called bounds iteration method (BIM). Numerical tests indicate that the sequential technique including the BIM is particularly fast and stable. The B1M is not only effective in reliabilitybased...

  8. Optimal Model-Based Control in HVAC Systems

    DEFF Research Database (Denmark)

    Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik;

    2008-01-01

    This paper presents optimal model-based control of a heating, ventilating, and air-conditioning (HVAC) system. This HVAC system is made of two heat exchangers: an air-to-air heat exchanger (a rotary wheel heat recovery) and a water-to- air heat exchanger. First dynamic model of the HVAC system...

  9. Optimal PID Controller Tuning for Multivariable Aircraft Longitudinal Autopilot Based on Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Mostafa Lotfi Forushani

    2012-04-01

    Full Text Available This paper presents an optimized controller around the longitudinal axis of multivariable system in one of the aircraft flight conditions. The controller is introduced in order to control the angle of attack from the pitch attitude angle independently (that is required for designing a set of direct force-modes for the longitudinal axis based on particle swarm optimization (PSO algorithm. The autopilot system for military or civil aircraft is an essential component and in this paper, the autopilot system via 6 degree of freedom model for the control and guidance of aircraft in which the autopilot design will perform based on defining the longitudinal and the lateral-directional axes are supposed. The effectiveness of the proposed controller is illustrated by considering HIMAT aircraft. The simulation results verify merits of the proposed controller.

  10. QOS-BASED MULTICAST ROUTING OPTIMIZATION ALGORITHMS FOR INTERNET

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Most of the multimedia applications require strict Quality-of-Service (QoS) guarantee during the communication between a single source and multiple destinations. The paper mainly presents a QoS Multicast Routing algorithms based on Genetic Algorithm (QMRGA). Simulation results demonstrate that the algorithm is capable of discovering a set of QoS-based near optimized, non-dominated multicast routes within a few iterations, even for the networks environment with uncertain parameters.

  11. Arc Based Ant Colony Optimization Algorithm for optimal design of gravitational sewer networks

    Directory of Open Access Journals (Sweden)

    R. Moeini

    2017-06-01

    Full Text Available In this paper, constrained and unconstrained versions of a new formulation of Ant Colony Optimization Algorithm (ACOA named Arc Based Ant Colony Optimization Algorithm (ABACOA are augmented with the Tree Growing Algorithm (TGA and used for the optimal layout and pipe size design of gravitational sewer networks. The main advantages offered by the proposed ABACOA formulation are proper definition of heuristic information, a useful component of the ant-based algorithms, and proper trade-off between the two conflicting search attributes of exploration and exploitation. In both the formulations, the TGA is used to incrementally construct feasible tree-like layouts out of the base layout. In the first formulation, unconstrained version of ABACOA is used to determine the nodal cover depths of sewer pipes while in the second formulation, a constrained version of ABACOA is used to determine the nodal cover depths of sewer pipes which satisfy the pipe slopes constraint. Three different methods of cut determination are also proposed to complete the construction of a tree-like network containing all base layout pipes, here. The proposed formulations are used to solve three test examples of different scales and the results are presented and compared with other available results in the literature. Comparison of the results shows that best results are obtained using the third cutting method in both the formulations. In addition, the results indicate the ability of the proposed methods and in particular the constrained version of ABACOA equipped with TGA to solve sewer networks design optimization problem. To be specific, the constrained version of ABACOA has been able to produce results 0.1%, 1% and 2.1% cheaper than those obtained by the unconstrained version of ABACOA for the first, second and the third test examples, respectively.

  12. Constructal multidisciplinary optimization of electromagnet based on entransy dissipation minimization

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    Based on entransy dissipation, the mean temperature difference of solenoid (electromagnet) with high thermal conductivity material inserted is deduced, which can be taken as the fundament for heat transfer optimization using the extremum principle of entransy dissipation. Then, the electromagnet working at steady state (constant magnetic field, constant heat generating rate per unit volume) is optimized for entransy dissipation minimization (i.e. mean temperature difference minimization) with and without volume constraint. Besides, the effect of high thermal conductivity material on the magnetic field is analyzed, and the minimum mean temperature versus volume and magnetic induction characteristic are also studied.

  13. Method of Fire Image Identification Based on Optimization Theory

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on the optimization theory to identifying fire image characteristics. First the optimization of BP neural network adopting Levenberg-Marquardt algorithm with the property of quadratic convergence is discussed, and then a new system of fire image identification is devised. Plenty of experiments and field tests have proved that this system can detect the early-stage fire flame quickly and reliably.

  14. Optimal Route Selection Method Based on Vague Sets

    Institute of Scientific and Technical Information of China (English)

    GUO Rui; DU Li min; WANG Chun

    2015-01-01

    Optimal route selection is an important function of vehicle trac flow guidance system. Its core is to determine the index weight for measuring the route merits and to determine the evaluation method for selecting route. In this paper, subjective weighting method which relies on driver preference is used to determine the weight and the paper proposes the multi-criteria weighted decision method based on vague sets for selecting the optimal route. Examples show that, the usage of vague sets to describe route index value can provide more decision-making information for route selection.

  15. Parallel Harmony Search Based Distributed Energy Resource Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Ceylan, Oguzhan [ORNL; Liu, Guodong [ORNL; Tomsovic, Kevin [University of Tennessee, Knoxville (UTK)

    2015-01-01

    This paper presents a harmony search based parallel optimization algorithm to minimize voltage deviations in three phase unbalanced electrical distribution systems and to maximize active power outputs of distributed energy resources (DR). The main contribution is to reduce the adverse impacts on voltage profile during a day as photovoltaics (PVs) output or electrical vehicles (EVs) charging changes throughout a day. The IEEE 123- bus distribution test system is modified by adding DRs and EVs under different load profiles. The simulation results show that by using parallel computing techniques, heuristic methods may be used as an alternative optimization tool in electrical power distribution systems operation.

  16. SECURE STEGANOGRAPHY BASED ON BINARY PARTICLE SWARM OPTIMIZATION

    Institute of Scientific and Technical Information of China (English)

    Guo Yanqing; Kong Xiangwei; You Xingang

    2009-01-01

    The objective of steganography is to hide message securely in cover objects for secret communication. How to design a secure steganographic algorithm is still major challenge in this research field. In this letter, developing secure steganography is formulated as solving a constrained IP (Integer Programming) problem, which takes the relative entropy of cover and stego distributions as the objective function. Furthermore, a novel method is introduced based on BPSO (Binary Particle Swarm Optimization) for achieving the optimal solution of this programming problem. Experimental results show that the proposed method can achieve excellent performance on preserving neighboring co-occurrence features for JPEG steganography.

  17. Optimization based tuning approach for offset free MPC

    DEFF Research Database (Denmark)

    Olesen, Daniel Haugård; Huusom, Jakob Kjøbsted; Jørgensen, John Bagterp

    2012-01-01

    We present an optimization based tuning procedure with certain robustness properties for an offset free Model Predictive Controller (MPC). The MPC is designed for multivariate processes that can be represented by an ARX model. The advantage of ARX model representations is that standard system...... identifiation techniques using convex optimization can be used for identification of such models from input-output data. The stochastic model of the ARX model identified from input-output data is modified with an ARMA model designed as part of the MPC-design procedure to ensure offset-free control. The ARMAX...

  18. Investment Strategies Optimization based on a SAX-GA Methodology

    CERN Document Server

    Canelas, António M L; Horta, Nuno C G

    2013-01-01

    This book presents a new computational finance approach combining a Symbolic Aggregate approXimation (SAX) technique with an optimization kernel based on genetic algorithms (GA). While the SAX representation is used to describe the financial time series, the evolutionary optimization kernel is used in order to identify the most relevant patterns and generate investment rules. The proposed approach considers several different chromosomes structures in order to achieve better results on the trading platform The methodology presented in this book has great potential on investment markets.

  19. Optimal high speed CMOS inverter design using craziness based Particle Swarm Optimization Algorithm

    Science.gov (United States)

    De, Bishnu P.; Kar, Rajib; Mandal, Durbadal; Ghoshal, Sakti P.

    2015-07-01

    The inverter is the most fundamental logic gate that performs a Boolean operation on a single input variable. In this paper, an optimal design of CMOS inverter using an improved version of particle swarm optimization technique called Craziness based Particle Swarm Optimization (CRPSO) is proposed. CRPSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: it has fast, nearglobal convergence, and it uses nearly robust control parameters. The performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problems. To overcome these problems the PSO algorithm has been modiffed to CRPSO in this paper and is used for CMOS inverter design. In birds' flocking or ffsh schooling, a bird or a ffsh often changes direction suddenly. In the proposed technique, the sudden change of velocity is modelled by a direction reversal factor associated with the previous velocity and a "craziness" velocity factor associated with another direction reversal factor. The second condition is introduced depending on a predeffned craziness probability to maintain the diversity of particles. The performance of CRPSO is compared with real code.gnetic algorithm (RGA), and conventional PSO reported in the recent literature. CRPSO based design results are also compared with the PSPICE based results. The simulation results show that the CRPSO is superior to the other algorithms for the examples considered and can be efficiently used for the CMOS inverter design.

  20. Bare-Bones Teaching-Learning-Based Optimization

    Directory of Open Access Journals (Sweden)

    Feng Zou

    2014-01-01

    Full Text Available Teaching-learning-based optimization (TLBO algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

  1. Reliability-Based Structural Optimization of Wave Energy Converters

    Directory of Open Access Journals (Sweden)

    Simon Ambühl

    2014-12-01

    Full Text Available More and more wave energy converter (WEC concepts are reaching prototypelevel. Once the prototype level is reached, the next step in order to further decrease thelevelized cost of energy (LCOE is optimizing the overall system with a focus on structuraland maintenance (inspection costs, as well as on the harvested power from the waves.The target of a fully-developed WEC technology is not maximizing its power output,but minimizing the resulting LCOE. This paper presents a methodology to optimize thestructural design of WECs based on a reliability-based optimization problem and the intentto maximize the investor’s benefits by maximizing the difference between income (e.g., fromselling electricity and the expected expenses (e.g., structural building costs or failure costs.Furthermore, different development levels, like prototype or commercial devices, may havedifferent main objectives and will be located at different locations, as well as receive varioussubsidies. These points should be accounted for when performing structural optimizationsof WECs. An illustrative example on the gravity-based foundation of the Wavestar deviceis performed showing how structural design can be optimized taking target reliability levelsand different structural failure modes due to extreme loads into account.

  2. Optimal diabatic dynamics of Majorana-based quantum gates

    Science.gov (United States)

    Rahmani, Armin; Seradjeh, Babak; Franz, Marcel

    2017-08-01

    In topological quantum computing, unitary operations on qubits are performed by adiabatic braiding of non-Abelian quasiparticles, such as Majorana zero modes, and are protected from local environmental perturbations. In the adiabatic regime, with timescales set by the inverse gap of the system, the errors can be made arbitrarily small by performing the process more slowly. To enhance the performance of quantum information processing with Majorana zero modes, we apply the theory of optimal control to the diabatic dynamics of Majorana-based qubits. While we sacrifice complete topological protection, we impose constraints on the optimal protocol to take advantage of the nonlocal nature of topological information and increase the robustness of our gates. By using the Pontryagin's maximum principle, we show that robust equivalent gates to perfect adiabatic braiding can be implemented in finite times through optimal pulses. In our implementation, modifications to the device Hamiltonian are avoided. Focusing on thermally isolated systems, we study the effects of calibration errors and external white and 1 /f (pink) noise on Majorana-based gates. While a noise-induced antiadiabatic behavior, where a slower process creates more diabatic excitations, prohibits indefinite enhancement of the robustness of the adiabatic scheme, our fast optimal protocols exhibit remarkable stability to noise and have the potential to significantly enhance the practical performance of Majorana-based information processing.

  3. Parametric Optimization of Regenerative Organic Rankine Cycle System for Diesel Engine Based on Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Hongjin Wang

    2015-09-01

    Full Text Available To efficiently recover the waste heat from a diesel engine exhaust, a regenerative organic Rankine cycle (RORC system was employed, and butane, R124, R416A, and R134a were used as the working fluids. The resulting diesel engine-RORC combined system was defined and the relevant evaluation indexes were proposed. First, the variation tendency of the exhaust energy rate under various diesel engine operating conditions was analyzed using experimental data. The thermodynamic model of the RORC system was established based on the first and second laws of thermodynamics, and the net power output and exergy destruction rate of the RORC system were selected as the objective functions. A particle swarm optimization (PSO algorithm was used to optimize the operating parameters of the RORC system, including evaporating pressure, intermediate pressure, and degree of superheat. The operating performances of the RORC system and diesel engine-RORC combined system were studied for the four selected working fluids under various operating conditions of the diesel engine. The results show that the operating performances of the RORC system and the combined system using butane are optimal on the basis of optimizing the operating parameters; when the engine speed is 2200 r/min and engine torque is 1215 N·m, the net power output of the RORC system using butane is 36.57 kW, and the power output increasing ratio (POIR of the combined system using butane is 11.56%.

  4. Silvicultural decisions based on simulation-optimization systems

    Energy Technology Data Exchange (ETDEWEB)

    Cao, Tianjian

    2010-05-15

    Forest management is facing new challenges under climate change. By adjusting thinning regimes, conventional forest management can be adapted to various objectives of utilization of forest resources, such as wood quality, forest bioenergy, and carbon sequestration. This thesis aims to develop and apply a simulation-optimization system as a tool for an interdisciplinary understanding of the interactions between wood science, forest ecology, and forest economics. In this thesis, the OptiFor software was developed for forest resources management. The OptiFor simulation-optimization system integrated the process-based growth model PipeQual, wood quality models, biomass production and carbon emission models, as well as energy wood and commercial logging models into a single optimization model. Osyczka s direct and random search algorithm was employed to identify optimal values for a set of decision variables. The numerical studies in this thesis broadened our current knowledge and understanding of the relationships between wood science, forest ecology, and forest economics. The results for timber production show that optimal thinning regimes depend on site quality and initial stand characteristics. Taking wood properties into account, our results show that increasing the intensity of thinning resulted in lower wood density and shorter fibers. The addition of nutrients accelerated volume growth, but lowered wood quality for Norway spruce. Integrating energy wood harvesting into conventional forest management showed that conventional forest management without energy wood harvesting was still superior in sparse stands of Scots pine. Energy wood from pre-commercial thinning turned out to be optimal for dense stands. When carbon balance is taken into account, our results show that changing carbon assessment methods leads to very different optimal thinning regimes and average carbon stocks. Raising the carbon price resulted in longer rotations and a higher mean annual

  5. Optimal Analysis of Irreversible Carnot Cycle Based on Entransy Dissipation

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Kyoung Hoon [Kumoh Nat’l Institute of Technology, Gumi (Korea, Republic of)

    2017-02-15

    The concept of entransy has been proposed recently as a potential heat transfer mechanism and could be useful in analyzing and optimizing the heat-work conversion systems. This work presents an entransy analysis for the irreversible Carnot cycle by systematic balance formulations of the entransy loss, work entransy, and entransy dissipations, which are consistent with exergy balances. Additionally, several forms of system efficiency are introduced based on entransy for the appreciation of the optimal system performance. The effects of the source temperature and irreversible efficiencies on the optimal conditions for system efficiencies are systematically investigated for both dumping and non-dumping cases of used source fluid. The results show different trends in entransy efficiencies when compared to the conventional efficiencies of energy and exergy, and represent another method to assess the effective use of heat source in power generation systems.

  6. A danger-theory-based immune network optimization algorithm.

    Science.gov (United States)

    Zhang, Ruirui; Li, Tao; Xiao, Xin; Shi, Yuanquan

    2013-01-01

    Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones. By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies' concentrations through its own danger signals and then triggers immune responses of self-regulation. So the population diversity can be maintained. Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population. Compared with influential optimization algorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times.

  7. Electronic Commerce Logistics Network Optimization Based on Swarm Intelligent Algorithm

    Directory of Open Access Journals (Sweden)

    Yabing Jiao

    2013-09-01

    Full Text Available This article establish an efficient electronic commerce logistics operation system to reduce distribution costs and build a logistics network operation model based on around the B2C electronic commerce enterprise logistics network operation system. B2C electronic commerce transactions features in the enterprise network platform. To solve the NP-hard problem this article use hybrid ant colony algorithm, particle swarm algorithm and group swarm intelligence algorithm to get a best solution. According to the intelligent algorithm, design of electronic commerce logistics network optimization system, enter the national 22 electronic commerce logistics network for validation. Through the experiment to verify the optimized logistics cost greatly decreased. This research can help B2C electronic commerce enterprise logistics network to optimize decision-making under the premise of ensuring the interests of consumers and service levels also can be an effective way for enterprises to improve the efficiency of logistics services and reduce operation costs

  8. PCNN document segmentation method based on bacterial foraging optimization algorithm

    Science.gov (United States)

    Liao, Yanping; Zhang, Peng; Guo, Qiang; Wan, Jian

    2014-04-01

    Pulse Coupled Neural Network(PCNN) is widely used in the field of image processing, but it is a difficult task to define the relative parameters properly in the research of the applications of PCNN. So far the determination of parameters of its model needs a lot of experiments. To deal with the above problem, a document segmentation based on the improved PCNN is proposed. It uses the maximum entropy function as the fitness function of bacterial foraging optimization algorithm, adopts bacterial foraging optimization algorithm to search the optimal parameters, and eliminates the trouble of manually set the experiment parameters. Experimental results show that the proposed algorithm can effectively complete document segmentation. And result of the segmentation is better than the contrast algorithms.

  9. A correlation consistency based multivariate alarm thresholds optimization approach.

    Science.gov (United States)

    Gao, Huihui; Liu, Feifei; Zhu, Qunxiong

    2016-11-01

    Different alarm thresholds could generate different alarm data, resulting in different correlations. A new multivariate alarm thresholds optimization methodology based on the correlation consistency between process data and alarm data is proposed in this paper. The interpretative structural modeling is adopted to select the key variables. For the key variables, the correlation coefficients of process data are calculated by the Pearson correlation analysis, while the correlation coefficients of alarm data are calculated by kernel density estimation. To ensure the correlation consistency, the objective function is established as the sum of the absolute differences between these two types of correlations. The optimal thresholds are obtained using particle swarm optimization algorithm. Case study of Tennessee Eastman process is given to demonstrate the effectiveness of proposed method.

  10. Voronoi Diagram Based Optimization of Dynamic Reactive Power Sources

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Weihong [University of Tennessee (UT); Sun, Kai [University of Tennessee (UT); Qi, Junjian [University of Tennessee (UT); Xu, Yan [ORNL

    2015-01-01

    Dynamic var sources can effectively mitigate fault-induced delayed voltage recovery (FIDVR) issues or even voltage collapse. This paper proposes a new approach to optimization of the sizes of dynamic var sources at candidate locations by a Voronoi diagram based algorithm. It first disperses sample points of potential solutions in a searching space, evaluates a cost function at each point by barycentric interpolation for the subspaces around the point, and then constructs a Voronoi diagram about cost function values over the entire space. Accordingly, the final optimal solution can be obtained. Case studies on the WSCC 9-bus system and NPCC 140-bus system have validated that the new approach can quickly identify the boundary of feasible solutions in searching space and converge to the global optimal solution.

  11. An optimal scheduling algorithm based on task duplication

    Institute of Scientific and Technical Information of China (English)

    Ruan Youlin; Liu Gan; Zhu Guangxi; Lu Xiaofeng

    2005-01-01

    When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O ( v2 ), where v represents the number of tasks.

  12. Analog Circuit Design Optimization Based on Evolutionary Algorithms

    Directory of Open Access Journals (Sweden)

    Mansour Barari

    2014-01-01

    Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.

  13. Study of Coal Mine Ventilation System Optimization based on Ventsim

    Directory of Open Access Journals (Sweden)

    Zhang Jing Gang

    2016-01-01

    Full Text Available This article is based on the situation of too large coal mine ventilation resistance in the Majiagou coal mine. According to Majiagou coal mine late production plans, it measures resistance comprehensively, analyses the resistance distributions and the problems exist in the ventilation systems and comes up with targeted optimization programs. By studying the ventilation system model, as well as adjusting the system parameters, Ventsim software is applied to study ventilation system in Majiagou coal mine. Design of mine ventilation is proved practical in the mine ventilation system optimizations, thus Ventsim software can not only be used in the ventilation network calculation and merry-demand simulation and dynamic of wind flow, but also can be used to assist in the short-term and long-term planning for ventilation system, it is of a certain significance of guidance to find the problems in the mine management and optimizations of the ventilation network.

  14. Extended Range Guided Munition Parameter Optimization Based on Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Many factors influencing range of extended range guided munition (ERGM) are analyzed. The definition domain of the most important three parameters are ascertained by preparatory mathematical simulation, the optimized mathematical model of ERGM maximum range with boundary conditions is created, and parameter optimization based on genetic algorithm (GA) is adopted. In the GA design, three-point crossover is used and the best chromosome is kept so that the convergence speed becomes rapid. Simulation result shows that GA is feasible, the result is good and it can be easy to attain global optimization solution, especially when the objective function is not the convex one for independent variables and it is a multi-parameter problem.

  15. Optimization of Classical Hydraulic Engine Mounts Based on RMS Method

    Directory of Open Access Journals (Sweden)

    J. Christopherson

    2005-01-01

    Full Text Available Based on RMS averaging of the frequency response functions of the absolute acceleration and relative displacement transmissibility, optimal parameters describing the hydraulic engine mount are determined to explain the internal mount geometry. More specifically, it is shown that a line of minima exists to define a relationship between the absolute acceleration and relative displacement transmissibility of a sprung mass using a hydraulic mount as a means of suspension. This line of minima is used to determine several optimal systems developed on the basis of different clearance requirements, hence different relative displacement requirements, and compare them by means of their respective acceleration and displacement transmissibility functions. In addition, the transient response of the mount to a step input is also investigated to show the effects of the optimization upon the time domain response of the hydraulic mount.

  16. GPU-based ultra fast IMRT plan optimization

    CERN Document Server

    Men, Chunhua; Choi, Dongju; Majumdar, Amitava; Zheng, Ziyi; Mueller, Klaus; Jiang, Steve B

    2009-01-01

    The widespread adoption of on-board volumetric imaging in cancer radiotherapy has stimulated research efforts to develop online adaptive radiotherapy techniques to handle the inter-fraction variation of the patient's geometry. Such efforts face major technical challenges to perform treatment planning in real-time. To overcome this challenge, we are developing a supercomputing online re-planning environment (SCORE) at the University of California San Diego (UCSD). As part of the SCORE project, this paper presents our work on the implementation of an intensity modulated radiation therapy (IMRT) optimization algorithm on graphics processing units (GPUs). We adopt a penalty-based quadratic optimization model, which is solved by using a gradient projection method with Armijo's line search rule. Our optimization algorithm has been implemented in CUDA for parallel GPU computing as well as in C for serial CPU computing for comparison purpose. A prostate IMRT case with various beamlet and voxel sizes was used to evalu...

  17. Joint Optimization in UMTS-Based Video Transmission

    Directory of Open Access Journals (Sweden)

    Attila Zsiros

    2007-01-01

    Full Text Available A software platform is exposed, which was developed to enable demonstration and capacity testing. The platform simulates a joint optimized wireless video transmission. The development succeeded within the frame of the IST-PHOENIX project and is based on the system optimization model of the project. One of the constitutive parts of the model, the wireless network segment, is changed to a detailed, standard UTRA network simulation module. This paper consists of (1 a brief description of the projects simulation chain, (2 brief description of the UTRAN system, and (3 the integration of the two segments. The role of the UTRAN part in the joint optimization is described, with the configuration and control of this element. Finally, some simulation results are shown. In the conclusion, we show how our simulation results translate into real-world performance gains.

  18. Visibility-based optimal path and motion planning

    CERN Document Server

    Wang, Paul Keng-Chieh

    2015-01-01

    This monograph deals with various visibility-based path and motion planning problems motivated by real-world applications such as exploration and mapping planetary surfaces, environmental surveillance using stationary or mobile robots, and imaging of global air/pollutant circulation. The formulation and solution of these problems call for concepts and methods from many areas of applied mathematics including computational geometry, set-covering, non-smooth optimization, combinatorial optimization and optimal control. Emphasis is placed on the formulation of new problems and methods of approach to these problems. Since geometry and visualization play important roles in the understanding of these problems, intuitive interpretations of the basic concepts are presented before detailed mathematical development. The development of a particular topic begins with simple cases illustrated by specific examples, and then progresses forward to more complex cases. The intended readers of this monograph are primarily studen...

  19. Component-based integration of chemistry and optimization software.

    Science.gov (United States)

    Kenny, Joseph P; Benson, Steven J; Alexeev, Yuri; Sarich, Jason; Janssen, Curtis L; McInnes, Lois Curfman; Krishnan, Manojkumar; Nieplocha, Jarek; Jurrus, Elizabeth; Fahlstrom, Carl; Windus, Theresa L

    2004-11-15

    Typical scientific software designs make rigid assumptions regarding programming language and data structures, frustrating software interoperability and scientific collaboration. Component-based software engineering is an emerging approach to managing the increasing complexity of scientific software. Component technology facilitates code interoperability and reuse. Through the adoption of methodology and tools developed by the Common Component Architecture Forum, we have developed a component architecture for molecular structure optimization. Using the NWChem and Massively Parallel Quantum Chemistry packages, we have produced chemistry components that provide capacity for energy and energy derivative evaluation. We have constructed geometry optimization applications by integrating the Toolkit for Advanced Optimization, Portable Extensible Toolkit for Scientific Computation, and Global Arrays packages, which provide optimization and linear algebra capabilities. We present a brief overview of the component development process and a description of abstract interfaces for chemical optimizations. The components conforming to these abstract interfaces allow the construction of applications using different chemistry and mathematics packages interchangeably. Initial numerical results for the component software demonstrate good performance, and highlight potential research enabled by this platform.

  20. Nozzle Mounting Method Optimization Based on Robot Kinematic Analysis

    Science.gov (United States)

    Chen, Chaoyue; Liao, Hanlin; Montavon, Ghislain; Deng, Sihao

    2016-08-01

    Nowadays, the application of industrial robots in thermal spray is gaining more and more importance. A desired coating quality depends on factors such as a balanced robot performance, a uniform scanning trajectory and stable parameters (e.g. nozzle speed, scanning step, spray angle, standoff distance). These factors also affect the mass and heat transfer as well as the coating formation. Thus, the kinematic optimization of all these aspects plays a key role in order to obtain an optimal coating quality. In this study, the robot performance was optimized from the aspect of nozzle mounting on the robot. An optimized nozzle mounting for a type F4 nozzle was designed, based on the conventional mounting method from the point of view of robot kinematics validated on a virtual robot. Robot kinematic parameters were obtained from the simulation by offline programming software and analyzed by statistical methods. The energy consumptions of different nozzle mounting methods were also compared. The results showed that it was possible to reasonably assign the amount of robot motion to each axis during the process, so achieving a constant nozzle speed. Thus, it is possible optimize robot performance and to economize robot energy.

  1. An efficient approach for reliability-based topology optimization

    Science.gov (United States)

    Kanakasabai, Pugazhendhi; Dhingra, Anoop K.

    2016-01-01

    This article presents an efficient approach for reliability-based topology optimization (RBTO) in which the computational effort involved in solving the RBTO problem is equivalent to that of solving a deterministic topology optimization (DTO) problem. The methodology presented is built upon the bidirectional evolutionary structural optimization (BESO) method used for solving the deterministic optimization problem. The proposed method is suitable for linear elastic problems with independent and normally distributed loads, subjected to deflection and reliability constraints. The linear relationship between the deflection and stiffness matrices along with the principle of superposition are exploited to handle reliability constraints to develop an efficient algorithm for solving RBTO problems. Four example problems with various random variables and single or multiple applied loads are presented to demonstrate the applicability of the proposed approach in solving RBTO problems. The major contribution of this article comes from the improved efficiency of the proposed algorithm when measured in terms of the computational effort involved in the finite element analysis runs required to compute the optimum solution. For the examples presented with a single applied load, it is shown that the CPU time required in computing the optimum solution for the RBTO problem is 15-30% less than the time required to solve the DTO problems. The improved computational efficiency allows for incorporation of reliability considerations in topology optimization without an increase in the computational time needed to solve the DTO problem.

  2. Small Signal Stability Improvement of Power Systems Using Optimal Load Responses in Competitive Electricity Markets

    DEFF Research Database (Denmark)

    Hu, Weihao; Su, Chi; Chen, Zhe;

    2011-01-01

    Since the hourly spot market price is available one day ahead in Denmark, the price could be transferred to the consumers and they may shift some of their loads from high price periods to the low price periods in order to save their energy costs. The optimal load response to an electricity price...... price is proposed. A 17-bus power system with high wind power penetrations, which resembles the Eastern Danish power system, is chosen as the study case. Simulation results show that the optimal load response to electricity prices is an effective measure to improve the small signal stability of power...

  3. CFSO3: A New Supervised Swarm-Based Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Antonino Laudani

    2013-01-01

    Full Text Available We present CFSO3, an optimization heuristic within the class of the swarm intelligence, based on a synergy among three different features of the Continuous Flock-of-Starlings Optimization. One of the main novelties is that this optimizer is no more a classical numerical algorithm since it now can be seen as a continuous dynamic system, which can be treated by using all the mathematical instruments available for managing state equations. In addition, CFSO3 allows passing from stochastic approaches to supervised deterministic ones since the random updating of parameters, a typical feature for numerical swam-based optimization algorithms, is now fully substituted by a supervised strategy: in CFSO3 the tuning of parameters is a priori designed for obtaining both exploration and exploitation. Indeed the exploration, that is, the escaping from a local minimum, as well as the convergence and the refinement to a solution can be designed simply by managing the eigenvalues of the CFSO state equations. Virtually in CFSO3, just the initial values of positions and velocities of the swarm members have to be randomly assigned. Both standard and parallel versions of CFSO3 together with validations on classical benchmarks are presented.

  4. Chaos Time Series Prediction Based on Membrane Optimization Algorithms

    Directory of Open Access Journals (Sweden)

    Meng Li

    2015-01-01

    Full Text Available This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ,m and least squares support vector machine (LS-SVM (γ,σ by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE, root mean square error (RMSE, and mean absolute percentage error (MAPE.

  5. Mars Mission Optimization Based on Collocation of Resources

    Science.gov (United States)

    Chamitoff, G. E.; James, G. H.; Barker, D. C.; Dershowitz, A. L.

    2003-01-01

    This paper presents a powerful approach for analyzing Martian data and for optimizing mission site selection based on resource collocation. This approach is implemented in a program called PROMT (Planetary Resource Optimization and Mapping Tool), which provides a wide range of analysis and display functions that can be applied to raw data or imagery. Thresholds, contours, custom algorithms, and graphical editing are some of the various methods that can be used to process data. Output maps can be created to identify surface regions on Mars that meet any specific criteria. The use of this tool for analyzing data, generating maps, and collocating features is demonstrated using data from the Mars Global Surveyor and the Odyssey spacecraft. The overall mission design objective is to maximize a combination of scientific return and self-sufficiency based on utilization of local materials. Landing site optimization involves maximizing accessibility to collocated science and resource features within a given mission radius. Mission types are categorized according to duration, energy resources, and in-situ resource utilization. Optimization results are shown for a number of mission scenarios.

  6. Computer Based Porosity Design by Multi Phase Topology Optimization

    Science.gov (United States)

    Burblies, Andreas; Busse, Matthias

    2008-02-01

    A numerical simulation technique called Multi Phase Topology Optimization (MPTO) based on finite element method has been developed and refined by Fraunhofer IFAM during the last five years. MPTO is able to determine the optimum distribution of two or more different materials in components under thermal and mechanical loads. The objective of optimization is to minimize the component's elastic energy. Conventional topology optimization methods which simulate adaptive bone mineralization have got the disadvantage that there is a continuous change of mass by growth processes. MPTO keeps all initial material concentrations and uses methods adapted from molecular dynamics to find energy minimum. Applying MPTO to mechanically loaded components with a high number of different material densities, the optimization results show graded and sometimes anisotropic porosity distributions which are very similar to natural bone structures. Now it is possible to design the macro- and microstructure of a mechanical component in one step. Computer based porosity design structures can be manufactured by new Rapid Prototyping technologies. Fraunhofer IFAM has applied successfully 3D-Printing and Selective Laser Sintering methods in order to produce very stiff light weight components with graded porosities calculated by MPTO.

  7. Group Elevator Peak Scheduling Based on Robust Optimization Model

    Directory of Open Access Journals (Sweden)

    ZHANG, J.

    2013-08-01

    Full Text Available Scheduling of Elevator Group Control System (EGCS is a typical combinatorial optimization problem. Uncertain group scheduling under peak traffic flows has become a research focus and difficulty recently. RO (Robust Optimization method is a novel and effective way to deal with uncertain scheduling problem. In this paper, a peak scheduling method based on RO model for multi-elevator system is proposed. The method is immune to the uncertainty of peak traffic flows, optimal scheduling is realized without getting exact numbers of each calling floor's waiting passengers. Specifically, energy-saving oriented multi-objective scheduling price is proposed, RO uncertain peak scheduling model is built to minimize the price. Because RO uncertain model could not be solved directly, RO uncertain model is transformed to RO certain model by elevator scheduling robust counterparts. Because solution space of elevator scheduling is enormous, to solve RO certain model in short time, ant colony solving algorithm for elevator scheduling is proposed. Based on the algorithm, optimal scheduling solutions are found quickly, and group elevators are scheduled according to the solutions. Simulation results show the method could improve scheduling performances effectively in peak pattern. Group elevators' efficient operation is realized by the RO scheduling method.

  8. An Improved Particle Swarm Optimization Algorithm Based on Ensemble Technique

    Institute of Scientific and Technical Information of China (English)

    SHI Yan; HUANG Cong-ming

    2006-01-01

    An improved particle swarm optimization (PSO) algorithm based on ensemble technique is presented. The algorithm combines some previous best positions (pbest) of the particles to get an ensemble position (Epbest), which is used to replace the global best position (gbest). It is compared with the standard PSO algorithm invented by Kennedy and Eberhart and some improved PSO algorithms based on three different benchmark functions. The simulation results show that the improved PSO based on ensemble technique can get better solutions than the standard PSO and some other improved algorithms under all test cases.

  9. Multicast Routing Problem Using Tree-Based Cuckoo Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Mahmood Sardarpour

    2016-06-01

    Full Text Available The problem of QoS multicast routing is to find a multicast tree with the least expense/cost which would meet the limitations such as band width, delay and loss rate. This is a NP-Complete problem. To solve the problem of multicast routing, the entire routes from the source node to every destination node are often recognized. Then the routes are integrated and changed into a single multicast tree. But they are slow and complicated methods. The present paper introduces a new tree-based optimization method to overcome such weaknesses. The recommended method directly optimizes the multicast tree. Therefore a tree-based typology including several spanning trees is created which combines the trees two by two. For this purpose, the Cuckoo Algorithm is used which is proved to be well converged and makes quick calculations. The simulation conducted on different types of network typologies proved that it is a practical and influential algorithm.

  10. FEM Optimal Design of Wind Energy-based Heater

    Directory of Open Access Journals (Sweden)

    Tiberiu Tudorache

    2009-07-01

    Full Text Available This paper deals with the finite element based optimal design of a wind energybased heater. The proposed device ensures the conversion of the wind kinetic energy intoheat by means of Joule effect of eddy currents induced in the wall of a tubular stator due tothe rotating magnetic field produced by rotor permanent magnets. The transientelectromagnetic field problem associated to the operation of the device is solved using a2D finite element approach based on vector potential formulation. A simplified method forthe 2D heat transfer analysis of the device is also proposed. The influence of stator wallmaterial and thickness, number of poles, the airgap thickness and the geometricalparameters of the permanent magnets is analyzed in the aim of optimizing the studiedheater.

  11. On combining Laplacian and optimization-based mesh smoothing techniques

    Energy Technology Data Exchange (ETDEWEB)

    Freitag, L.A.

    1997-07-01

    Local mesh smoothing algorithms have been shown to be effective in repairing distorted elements in automatically generated meshes. The simplest such algorithm is Laplacian smoothing, which moves grid points to the geometric center of incident vertices. Unfortunately, this method operates heuristically and can create invalid meshes or elements of worse quality than those contained in the original mesh. In contrast, optimization-based methods are designed to maximize some measure of mesh quality and are very effective at eliminating extremal angles in the mesh. These improvements come at a higher computational cost, however. In this article the author proposes three smoothing techniques that combine a smart variant of Laplacian smoothing with an optimization-based approach. Several numerical experiments are performed that compare the mesh quality and computational cost for each of the methods in two and three dimensions. The author finds that the combined approaches are very cost effective and yield high-quality meshes.

  12. Optimal reliability-based design of offshore wind turbine parks

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard

    2006-01-01

    that wind turbines are parked for wind speeds larger than 25 m/s resulting in reduced wind loads. Basic relationships are described for the mean wind velocity and turbulence intensity in wind turbine parks with emphasis on the spatial correlation. The expected total failure costs for the wind turbine park......A basic formulation for optimal reliability-based design of wind turbine parks is presented. Based on this model a probabilistic model and representative limit state equations for structural failure of wind turbine towers are formulated. The probability of failure is determined taking into account...... are estimated and normalised with a situation with only one wind turbine taking into account the spatial correlation. A sensitivity analysis is made with respect to parameters modelling the spatial correlation. Further, an optimization problem is formulated where a design parameter is the distance between...

  13. Parameter optimization in differential geometry based solvation models.

    Science.gov (United States)

    Wang, Bao; Wei, G W

    2015-10-01

    Differential geometry (DG) based solvation models are a new class of variational implicit solvent approaches that are able to avoid unphysical solvent-solute boundary definitions and associated geometric singularities, and dynamically couple polar and non-polar interactions in a self-consistent framework. Our earlier study indicates that DG based non-polar solvation model outperforms other methods in non-polar solvation energy predictions. However, the DG based full solvation model has not shown its superiority in solvation analysis, due to its difficulty in parametrization, which must ensure the stability of the solution of strongly coupled nonlinear Laplace-Beltrami and Poisson-Boltzmann equations. In this work, we introduce new parameter learning algorithms based on perturbation and convex optimization theories to stabilize the numerical solution and thus achieve an optimal parametrization of the DG based solvation models. An interesting feature of the present DG based solvation model is that it provides accurate solvation free energy predictions for both polar and non-polar molecules in a unified formulation. Extensive numerical experiment demonstrates that the present DG based solvation model delivers some of the most accurate predictions of the solvation free energies for a large number of molecules.

  14. LTE/MVNO NETWORKS STRUCTURE OPTIMIZATION BASED ON TENSOR DECOMPOSITION

    OpenAIRE

    Strelkovskaya, Iryna; Solovskaya, Iryna

    2015-01-01

    The usage of tensor methods on the decomposition basis is offered for the tasks solution of structure optimization for LTE/MVNO networks mobile communication. The choice problem of optimum topology of e-Node B base stations connectionsin the radio access of E-UTRAN/LTE network was solved. The assessment problem of QoS quality characteristics of complex LTE/MVNO network architecture was solved.

  15. Information fusion based optimal control for large civil aircraft system.

    Science.gov (United States)

    Zhen, Ziyang; Jiang, Ju; Wang, Xinhua; Gao, Chen

    2015-03-01

    Wind disturbance has a great influence on landing security of Large Civil Aircraft. Through simulation research and engineering experience, it can be found that PID control is not good enough to solve the problem of restraining the wind disturbance. This paper focuses on anti-wind attitude control for Large Civil Aircraft in landing phase. In order to improve the riding comfort and the flight security, an information fusion based optimal control strategy is presented to restrain the wind in landing phase for maintaining attitudes and airspeed. Data of Boeing707 is used to establish a nonlinear mode with total variables of Large Civil Aircraft, and then two linear models are obtained which are divided into longitudinal and lateral equations. Based on engineering experience, the longitudinal channel adopts PID control and C inner control to keep longitudinal attitude constant, and applies autothrottle system for keeping airspeed constant, while an information fusion based optimal regulator in the lateral control channel is designed to achieve lateral attitude holding. According to information fusion estimation, by fusing hard constraint information of system dynamic equations and the soft constraint information of performance index function, optimal estimation of the control sequence is derived. Based on this, an information fusion state regulator is deduced for discrete time linear system with disturbance. The simulation results of nonlinear model of aircraft indicate that the information fusion optimal control is better than traditional PID control, LQR control and LQR control with integral action, in anti-wind disturbance performance in the landing phase. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Methods for reliability based design optimization of structural components

    OpenAIRE

    Dersjö, Tomas

    2012-01-01

    Cost and quality are key properties of a product, possibly even the two most important. Onedefinition of quality is fitness for purpose. Load-bearing products, i.e. structural components,loose their fitness for purpose if they fail. Thus, the ability to withstand failure is a fundamentalmeasure of quality for structural components. Reliability based design optimization(RBDO) is an approach for development of structural components which aims to minimizethe cost while constraining the probabili...

  17. Routing Optimization Based on Taboo Search Algorithm for Logistic Distribution

    OpenAIRE

    Hongxue Yang; Lingling Xuan

    2014-01-01

    Along with the widespread application of the electronic commerce in the modern business, the logistic distribution has become increasingly important. More and more enterprises recognize that the logistic distribution plays an important role in the process of production and sales. A good routing for logistic distribution can cut down transport cost and improve efficiency. In order to cut down transport cost and improve efficiency, a routing optimization based on taboo search for logistic distr...

  18. Parameter optimization for tandemregenerative system based on critical path

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    For a tandem queue system, the regenerative path is constructed. In an inter-regeneration cycle, the sensitivity value of performance measure with respect to the adjustable parameter θ can be acquired based on a fixed length of observation. Furthermore, a new algorithm of parameter optimization for the tandem queue system is given,which requires less simulation and no analysis for the perturbation transmission and makes a better estimation for the sen sitivity.

  19. Optimal design of SAW-based gyroscope to improve sensitivity

    Science.gov (United States)

    Oh, Haekwan; Yang, Sangsik; Lee, Keekeun

    2010-02-01

    A surface acoustic wave (SAW)-based gyroscope was developed on a piezoelectric substrate. The developed gyroscope consists of two SAW oscillators, metallic dots, and absorber. Coupling of mode (COM) modeling was conducted to determine the optimal device parameters prior to fabrication. Depending on the angular velocity, the difference of the oscillation frequency was modulated. The obtained sensitivity was approximately 52.35 Hz/deg.s at an angular rate range of 0~1000 deg/s.

  20. A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

    NARCIS (Netherlands)

    Yao, W.; Chen, X.; Ouyang, Q.; Van Tooren, M.

    2011-01-01

    Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with

  1. Model-based dynamic control and optimization of gas networks

    Energy Technology Data Exchange (ETDEWEB)

    Hofsten, Kai

    2001-07-01

    by a structured sequential quadratic programming algorithm of Newton type. Each open loop problem is specified using a nonlinear prediction model. For each iteration of the quadratic programming procedure, a linear time variant prediction model is formulated. The suggested controller also handles time varying source capacity. Potential problems such as infeasibility and the security of the supply when facing a change in the status of the infrastructure of the transmission system under a transient customer load are treated. Comments on the infeasibility due to errors such as load forecast error, model error and state estimation error are also discussed. A simplified nonlinear model called the creep flow model is used to describe the fluid dynamics inside a natural gas transmission line. Different assumptions and reformulations of this model yield the different control, simulation and optimization models used in this thesis. The control of a single gas transmission line is investigated using linear model predictive control based on instant linearization of the nonlinear model. Model predictive control using a bi quadratic optimization model formulated from the creep flow model is also investigated. A distributed parameter control model of the gas dynamics for a transmission line is formulated. An analytic solution of this model is given with both Neuman boundary conditions and distributed supplies and loads. A transfer function model is developed expressing the dynamics between the defined output and the control and disturbance inputs of the transmission line. Based on the qualitative behaviour observed from the step responses of the solutions of the distributed parameter model formulated in this thesis, simplified transfer function models were developed. These control models expresses the dynamics of a natural gas transmission line with Neuman boundary control and load. Further, these models were used to design a control law, which is a combination of a Smith

  2. Optimal control and optimal trajectories of regional macroeconomic dynamics based on the Pontryagin maximum principle

    Science.gov (United States)

    Bulgakov, V. K.; Strigunov, V. V.

    2009-05-01

    The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.

  3. Tour Route Multiobjective Optimization Design Based on the Tourist Satisfaction

    Directory of Open Access Journals (Sweden)

    Yan Han

    2014-01-01

    Full Text Available The question prompted is how to design the tour route to make the tourists get the maximum satisfactions considering the tourists’ demand. The influence factors of the tour route choices of tourists were analyzed and tourists’ behavior characteristics and psychological preferences were regarded as the important influence factors based on the tourist behavioral theories. A questionnaire of tourists’ tour route information and satisfaction degree was carried out. Some information about the scene spot and tourists demand and tour behaviors characteristic such as visit frequency, number of attractions visited was obtained and analyzed. Based on the convey datum, tour routes multiobjective optimization functions were prompted for the tour route design regarding the maximum satisfaction and the minimum tour distance as the optimal objective. The available routes are listed and categorized. Based on the particle swarm optimization model, the priorities of the tour route are calculated and finally the suggestion depth tour route and quick route tour routes are given considering the different tour demands of tourists. The results can offer constructive suggestions on how to design tour routes on the part of tourism enterprises and how to choose a proper tour route on the part of tourists.

  4. Modeling the crop transpiration using an optimality-based approach

    Institute of Scientific and Technical Information of China (English)

    Stanislaus; J.Schymanski; Murugesu; Sivapalan

    2008-01-01

    Evapotranspiration constitutes more than 80% of the long-term water balance in Northern China.In this area,crop transpiration due to large areas of agriculture and irrigation is responsible for the majority of evapotranspiration.A model for crop transpiration is therefore essential for estimating the agricultural water consumption and understanding its feedback to the environment.However,most existing hydrological models usually calculate transpiration by relying on parameter calibration against local observations,and do not take into account crop feedback to the ambient environment.This study presents an optimality-based ecohydrology model that couples an ecological hypothesis,the photosynthetic process,stomatal movement,water balance,root water uptake and crop senescence,with the aim of predicting crop characteristics,CO2 assimilation and water balance based only on given meteorological data.Field experiments were conducted in the Weishan Irrigation District of Northern China to evaluate performance of the model.Agreement between simulation and measurement was achieved for CO2 assimilation,evapotranspiration and soil moisture content.The vegetation optimality was proven valid for crops and the model was applicable for both C3 and C4 plants.Due to the simple scheme of the optimality-based approach as well as its capability for modeling dynamic interactions between crops and the water cycle without prior vegetation information,this methodology is potentially useful to couple with the distributed hydrological model for application at the watershed scale.

  5. Genetic based optimization for multicast routing algorithm for MANET

    Indian Academy of Sciences (India)

    C Rajan; N Shanthi

    2015-12-01

    Mobile Ad hoc Network (MANET) is established for a limited period, for special extemporaneous services related to mobile applications. This ad hoc network is set up for a limited period, in environments that change with the application. While in Internet the TCP/IP protocol suite supports a wide range of application, in MANETs protocols are tuned to specific customer/application. Multicasting is emerging as a popular communication format where the same packet is sent to multiple nodes in a network. Routing in multicasting involves maintaining routes and finding new node locations in a group and is NP-complete due to the dynamic nature of the network. In this paper, a Hybrid Genetic Based Optimization for Multicast Routing algorithm is proposed. The proposed algorithm uses the best features of Genetic Algorithm (GA) and particle swarm optimization (PSO) to improve the solution. Simulations were conducted by varying number of mobile nodes and results compared with Multicast AODV (MAODV) protocol, PSO based and GA based solution. The proposed optimization improves jitter, end to end delay and Packet Delivery Ratio (PDR) with faster convergence.

  6. Survey on Power Optimization for Disk Based Systems

    Directory of Open Access Journals (Sweden)

    G. Ravikumar

    2011-09-01

    Full Text Available Energy optimization has become a growing concern in the present world. Energy optimization can influence the overall system design and reliability. Power can greatly influence the performance of the disk, as power dissipation generates heat that affects stability and reliability of the component, particularly for large server systems. Hence, developers concentrate on the configuration of disk arrays which can deliver extremely high performance. Though, there are several significant techniques for tackling disk power for laptops and workstations, using them in a server environment are a considerable challenge, especially under stringent performance needs. Excessive power consumption is a major barrier to the market acceptance of hard disks in mobile electronic devices. Studying and reducing power consumption, however, often comprises running time intensive disk traces on real hardware with specialized power-monitoring equipment. Most of the conventional energy optimization techniques are based on architectural level techniques and is found to be effective only in certain scenarios. This paper proposes a survey on the disk energy optimization techniques. This paper analyses the functionalities, advantages and the disadvantages of the various techniques for the disk power consumption.

  7. Motion Structural Optimization Strategy for Rhombic Element Based Foldable Structure

    Directory of Open Access Journals (Sweden)

    Seung Hyun Jeong

    2015-02-01

    Full Text Available This research presents a new systematical design approach of foldable structure composed of several rhombic elements by applying genetic algorithm. As structural shapes represented by a foldable structure can be easily and dramatically morphed by manipulating rotational directions and angle of joints, the foldable structure has been used for various elementary structural members and engineering mechanisms. However a systematic design approach determining detail rotational angle and directions of unit cells for arbitrary shaped target areas has not been proposed yet. This research contributes to it by developing a new structural optimization method determining optimal angle and rotation directions to cover arbitrary shaped target areas of interest with aggregated rhombic elements. To achieve this purpose, we present an optimization formulation minimizing the sum of distances between each reference joint of an arbitrary shaped target area and its closest outer joints of foldable structure. To find out the outer joint set of a given foldable structure, an efficient geometric analysis method based on Delaunay triangulation is also developed and implemented. To show the validity and limitations of the present approach, several foldable structure design problems for two-dimensional arbitrary shaped target areas are solved with the present optimization procedure.

  8. An Optimized Analogy-Based Project Effort Estimation

    Directory of Open Access Journals (Sweden)

    Mohammad Azzeh

    2014-05-01

    Full Text Available despite the predictive performance of Analogy-Based Estimation (ABE in generating better effort estimates, there is no consensus on: (1 how to predetermine the appropriate number of analogies, (2 which adjustment technique produces better estimates. Yet, there is no prior works attempted to optimize both number of analogies and feature distance weights for each test project. Perhaps rather than using fixed number, it is better to optimize this value for each project individually and then adjust the retrieved analogies by optimizing and approximating complex relationships between features and reflects that approximation on the final estimate. The Artificial Bees Algorithm is utilized to find, for each test project, the appropriate number of closest projects and features distance weights that is used to adjust those analogies’ efforts. The proposed technique has been applied and validated to 8 publically datasets from PROMISE repository. Results obtained show that: (1 the predictive performance of ABE has noticeably been improved, (2 the number of analogies was remarkably variable for each test project. While there are many techniques to adjust ABE, Using optimization algorithm provides two solutions in one technique and appeared useful for datasets with complex structure.

  9. Parameter Optimization of Linear Quadratic Controller Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    LI Jimin; SHANG Chaoxuan; ZOU Minghu

    2007-01-01

    The selection of weighting matrix in design of the linear quadratic optimal controller is an important topic in the control theory. In this paper, an approach based on genetic algorithm is presented for selecting the weighting matrix for the optimal controller. Genetic algorithm is adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this algorithm, the fitness function is used to evaluate individuals and reproductive success varies with fitness. In the design of the linear quadratic optimal controller, the fitness function has relation to the anticipated step response of the system. Not only can the controller designed by this approach meet the demand of the performance indexes of linear quadratic controller, but also satisfy the anticipated step response of close-loop system. The method possesses a higher calculating efficiency and provides technical support for the optimal controller in engineering application. The simulation of a three-order single-input single-output (SISO) system has demonstrated the feasibility and validity of the approach.

  10. Genetic Algorithm (GA)-Based Inclinometer Layout Optimization.

    Science.gov (United States)

    Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo

    2015-04-17

    This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors.

  11. Simulated Annealing-Based Krill Herd Algorithm for Global Optimization

    Directory of Open Access Journals (Sweden)

    Gai-Ge Wang

    2013-01-01

    Full Text Available Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH, for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH method is proposed for optimization tasks. A new krill selecting (KS operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA. In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.

  12. Cat swarm optimization based evolutionary framework for multi document summarization

    Science.gov (United States)

    Rautray, Rasmita; Balabantaray, Rakesh Chandra

    2017-07-01

    Today, World Wide Web has brought us enormous quantity of on-line information. As a result, extracting relevant information from massive data has become a challenging issue. In recent past text summarization is recognized as one of the solution to extract useful information from vast amount documents. Based on number of documents considered for summarization, it is categorized as single document or multi document summarization. Rather than single document, multi document summarization is more challenging for the researchers to find accurate summary from multiple documents. Hence in this study, a novel Cat Swarm Optimization (CSO) based multi document summarizer is proposed to address the problem of multi document summarization. The proposed CSO based model is also compared with two other nature inspired based summarizer such as Harmony Search (HS) based summarizer and Particle Swarm Optimization (PSO) based summarizer. With respect to the benchmark Document Understanding Conference (DUC) datasets, the performance of all algorithms are compared in terms of different evaluation metrics such as ROUGE score, F score, sensitivity, positive predicate value, summary accuracy, inter sentence similarity and readability metric to validate non-redundancy, cohesiveness and readability of the summary respectively. The experimental analysis clearly reveals that the proposed approach outperforms the other summarizers included in the study.

  13. DNA Sequence Optimization Based on Continuous Particle Swarm Optimization for Reliable DNA Computing and DNA Nanotechnology

    Directory of Open Access Journals (Sweden)

    N. K. Khalid

    2008-01-01

    Full Text Available Problem statement: In DNA based computation and DNA nanotechnology, the design of good DNA sequences has turned out to be an essential problem and one of the most practical and important research topics. Basically, the DNA sequence design problem is a multi-objective problem and it can be evaluated using four objective functions, namely, Hmeasure, similarity, continuity and hairpin. Approach: There are several ways to solve multi-objective problem, however, in order to evaluate the correctness of PSO algorithm in DNA sequence design, this problem is converted into single objective problem. Particle Swarm Optimization (PSO is proposed to minimize the objective in the problem, subjected to two constraints: melting temperature and GCcontent. A model is developed to present the DNA sequence design based on PSO computation. Results: Based on experiments and researches done, 20 particles are used in the implementation of the optimization process, where the average values and the standard deviation for 100 runs are shown along with comparison to other existing methods. Conclusion: The results achieve verified that PSO can suitably solves the DNA sequence design problem using the proposed method and model, comparatively better than other approaches.

  14. Analyze the optimal solutions of optimization problems by means of fractional gradient based system using VIM

    Directory of Open Access Journals (Sweden)

    Firat Evirgen

    2016-04-01

    Full Text Available In this paper, a class of Nonlinear Programming problem is modeled with gradient based system of fractional order differential equations in Caputo's sense. To see the overlap between the equilibrium point of the fractional order dynamic system and theoptimal solution of the NLP problem in a longer timespan the Multistage Variational İteration Method isapplied. The comparisons among the multistage variational iteration method, the variationaliteration method and the fourth order Runge-Kutta method in fractional and integer order showthat fractional order model and techniques can be seen as an effective and reliable tool for finding optimal solutions of Nonlinear Programming problems.

  15. Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization

    Directory of Open Access Journals (Sweden)

    Xiangzhu He

    2016-01-01

    Full Text Available Recently, teaching-learning-based optimization (TLBO, as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.

  16. Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization.

    Science.gov (United States)

    He, Xiangzhu; Huang, Jida; Rao, Yunqing; Gao, Liang

    2016-01-01

    Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.

  17. An Optimal Rubrics-Based Approach to Real Estate Appraisal

    Directory of Open Access Journals (Sweden)

    Zhangcheng Chen

    2017-05-01

    Full Text Available Traditional real estate appraisal methods obtain estimates of real estate by using mathematical modeling to analyze the existing sample data. However, the information of sample data sometimes cannot fully reflect the real-time quotes. For example, in a thin real estate market, the correlated sample data for estimated object is lacking, which limits the estimates of these traditional methods. In this paper, an optimal rubrics-based approach to real estate appraisal is proposed, which brings in crowdsourcing. The valuation estimate can serve as a market indication for the potential real estate buyers or sellers. It is not only based on the information of the existing sample data (just like these traditional methods, but also on the extra real-time market information from online crowdsourcing feedback, which makes the estimated result close to that of the market. The proposed method constructs the rubrics model from sample data. Based on this, the cosine similarity function is used to calculate the similarity between each rubric for selecting the optimal rubrics. The selected optimal rubrics and the estimated point are posted on a crowdsourcing platform. After comparing the information of the estimated point with the optimal rubrics on the crowdsourcing platform, those users who are connected with the estimated object complete the appraisal with their knowledge of the real estate market. The experiment results show that the average accuracy of the proposed approach is over 70%; the maximum accuracy is 90%. This supports that the proposed method can easily provide a valuable market reference for the potential real estate buyers or sellers, and is an attempt to use the human-computer interaction in the real estate appraisal field.

  18. Attitude Optimal Backstepping Controller Based Quaternion for a UAV

    Directory of Open Access Journals (Sweden)

    Kaddouri Djamel

    2016-01-01

    Full Text Available A hierarchical controller design based on nonlinear H∞ theory and backstepping technique is developed for a nonlinear and coupled dynamic attitude system using conventional quaternion based method. The derived controller combines the attractive features of H∞ optimal controller and the advantages of the backstepping technique leading to a control law which avoids winding phenomena. Performance issues of the controller are illustrated in a simulation study made for a four-rotor vertical take-off and landing (VTOL aerial robot prototype known as the quadrotor aircraft.

  19. Stochastically optimized monocular vision-based navigation and guidance

    Science.gov (United States)

    Watanabe, Yoko

    -effort guidance (MEG) law for multiple target tracking is applied for a guidance design to achieve the mission. Through simulations, it is shown that the control effort can be reduced by using the MEG-based guidance design instead of a conventional proportional navigation-based one. The navigation and guidance designs are implemented and evaluated in a 6 DoF UAV flight simulation. Furthermore, the vision-based obstacle avoidance system is also tested in a flight test using a balloon as an obstacle. For monocular vision-based control problems, it is well-known that the separation principle between estimation and control does not hold. In other words, that vision-based estimation performance highly depends on the relative motion of the vehicle with respect to the target. Therefore, this thesis aims to derive an optimal guidance law to achieve a given mission under the condition of using the EKF-based relative navigation. Unlike many other works on observer trajectory optimization, this thesis suggests a stochastically optimized guidance design that minimizes the expected value of a cost function of the guidance error and the control effort subject to the EKF prediction and update procedures. A suboptimal guidance law is derived based on an idea of the one-step-ahead (OSA) optimization, in which the optimization is performed under the assumption that there will be only one more final measurement at the one time step ahead. The OSA suboptimal guidance law is applied to problems of vision-based rendezvous and vision-based obstacle avoidance. Simulation results are presented to show that the suggested guidance law significantly improves the guidance performance. The OSA suboptimal optimization approach is generalized as the n-step-ahead (nSA) optimization for an arbitrary number of n. Furthermore, the nSA suboptimal guidance law is extended to the p %-ahead suboptimal guidance by changing the value of n at each time step depending on the current time. The nSA (including the OSA) and

  20. RISK LOAN PORTFOLIO OPTIMIZATION MODEL BASED ON CVAR RISK MEASURE

    Directory of Open Access Journals (Sweden)

    Ming-Chang LEE

    2015-07-01

    Full Text Available In order to achieve commercial banks liquidity, safety and profitability objective requirements, loan portfolio risk analysis based optimization decisions are rational allocation of assets.  The risk analysis and asset allocation are the key technology of banking and risk management.  The aim of this paper, build a loan portfolio optimization model based on risk analysis.  Loan portfolio rate of return by using Value-at-Risk (VaR and Conditional Value-at-Risk (CVaR constraint optimization decision model reflects the bank's risk tolerance, and the potential loss of direct control of the bank.  In this paper, it analyze a general risk management model applied to portfolio problems with VaR and CVaR risk measures by using Using the Lagrangian Algorithm.  This paper solves the highly difficult problem by matrix operation method.  Therefore, the combination of this paper is easy understanding the portfolio problems with VaR and CVaR risk model is a hyperbola in mean-standard deviation space.  It is easy calculation in proposed method.

  1. Evaluating the Usability of Optimizing Text-based CAPTCHA Generation

    Directory of Open Access Journals (Sweden)

    Suliman A. Alsuhibany

    2016-08-01

    Full Text Available A CAPTCHA is a test that can, automatically, tell human and computer programs apart. It is a mechanism widely used nowadays for protecting web applications, interfaces, and services from malicious users and automated spammers. Usability and robustness are two fundamental aspects with CAPTCHA, where the usability aspect is the ease with which humans pass its challenges, while the robustness is the strength of its segmentation-resistance mechanism. The collapsing mechanism, which is removing the space between characters to prevent segmentation, has been shown to be reasonably resistant to known attacks. On the other hand, this mechanism drops considerably the human-solvability of text-based CAPTCHAs. Accordingly, an optimizer has previously been proposed that automatically enhances the usability of a CAPTCHA generation without sacrificing its robustness level. However, this optimizer has not yet been evaluated in terms of improving the usability. This paper, therefore, evaluates the usability of this optimizer by conducting an experimental study. The results of this evaluation showed that a statistically significant enhancement is found in the usability of text-based CAPTCHA generation.

  2. Optimizing legacy molecular dynamics software with directive-based offload

    Science.gov (United States)

    Michael Brown, W.; Carrillo, Jan-Michael Y.; Gavhane, Nitin; Thakkar, Foram M.; Plimpton, Steven J.

    2015-10-01

    Directive-based programming models are one solution for exploiting many-core coprocessors to increase simulation rates in molecular dynamics. They offer the potential to reduce code complexity with offload models that can selectively target computations to run on the CPU, the coprocessor, or both. In this paper, we describe modifications to the LAMMPS molecular dynamics code to enable concurrent calculations on a CPU and coprocessor. We demonstrate that standard molecular dynamics algorithms can run efficiently on both the CPU and an x86-based coprocessor using the same subroutines. As a consequence, we demonstrate that code optimizations for the coprocessor also result in speedups on the CPU; in extreme cases up to 4.7X. We provide results for LAMMPS benchmarks and for production molecular dynamics simulations using the Stampede hybrid supercomputer with both Intel® Xeon Phi™ coprocessors and NVIDIA GPUs. The optimizations presented have increased simulation rates by over 2X for organic molecules and over 7X for liquid crystals on Stampede. The optimizations are available as part of the "Intel package" supplied with LAMMPS.

  3. Variance optimal sampling based estimation of subset sums

    CERN Document Server

    Cohen, Edith; Kaplan, Haim; Lund, Carsten; Thorup, Mikkel

    2008-01-01

    From a high volume stream of weighted items, we want to maintain a generic sample of a certain limited size $k$ that we can later use to estimate the total weight of arbitrary subsets. This is the classic context of on-line reservoir sampling, thinking of the generic sample as a reservoir. We present a reservoir sampling scheme providing variance optimal estimation of subset sums. More precisely, if we have seen $n$ items of the stream, then for any subset size $m$, our scheme based on $k$ samples minimizes the average variance over all subsets of size $m$. In fact, the optimality is against any off-line sampling scheme tailored for the concrete set of items seen: no off-line scheme based on $k$ samples can perform better than our on-line scheme when it comes to average variance over any subset size. Our scheme has no positive covariances between any pair of item estimates. Also, our scheme can handle each new item of the stream in $O(\\log k)$ time, which is optimal even on the word RAM.

  4. Parametric optimization of optical devices based on strong photonic localization

    Science.gov (United States)

    Gui, Minmin; Yang, Xiangbo

    2017-07-01

    Symmetric two-segment-connected triangular defect waveguide networks (STSCTDWNs) can produce strong photonic localization, which is useful for designing highly efficient energy storage devices, high power superluminescent light emitting diodes, all-optical switches, and more. Although STSCTDWNs have been studied in previous works, in this paper we systematically optimize the parameters of STSCTDWNs to further enhance photonic localization so that the function of optical devices based on strong photonic localization can be improved. When optimizing the parameters, we find a linear relationship between the logarithm of photonic localization and the broken degree of networks. Furthermore, the slope and intercept of the linear relationship are larger than previous results. This means that the increasing speed of photonic localization is improved. The largest intensity of photonic localizations can reach 1036, which is 16 orders of magnitude larger than previous reported results. These optimized networks provide practical solutions for all optical devices based on strong photonic localization in the low frequency range, such as nanostructured devices.

  5. Optimization Design System for Composite Structures Based on Grid Technology

    Institute of Scientific and Technical Information of China (English)

    CHENG Wen-yuan; CHANG Yan; CUI De-gang; XIE Xiang-hui

    2007-01-01

    To solve the topology optimization of complicated multi-objective continuous/discrete design variables in aircraft structure design, a Parallel Pareto Genetic Algorithm (PPGA) is presented based on grid platform in this paper. In the algorithm, the commercial finite element analysis (FEA) software is integrated as the calculating tool for analyzing the objective functions and the filter of Pareto solution set based on weight information is introduced to deal with the relationships among all objectives. Grid technology is utilized in PPGA to realize the distributed computations and the user interface is developed to realize the job submission and job management locally/remotely. Taking the aero-elastic tailoring of a composite wing for optimization as an example, a set of Pareto solutions are obtained for the decision-maker. The numerical results show that the aileron reversal problem can be solved by adding the limited skin weight in this system. The algorithm can be used to solve complicated topology optimization for composite structures in engineering and the computation efficiency can be improved greatly by using the grid platform that aggregates numerous idle resources.

  6. Model-based optimization of tapered free-electron lasers

    Directory of Open Access Journals (Sweden)

    Alan Mak

    2015-04-01

    Full Text Available The energy extraction efficiency is a figure of merit for a free-electron laser (FEL. It can be enhanced by the technique of undulator tapering, which enables the sustained growth of radiation power beyond the initial saturation point. In the development of a single-pass x-ray FEL, it is important to exploit the full potential of this technique and optimize the taper profile a_{w}(z. Our approach to the optimization is based on the theoretical model by Kroll, Morton, and Rosenbluth, whereby the taper profile a_{w}(z is not a predetermined function (such as linear or exponential but is determined by the physics of a resonant particle. For further enhancement of the energy extraction efficiency, we propose a modification to the model, which involves manipulations of the resonant particle’s phase. Using the numerical simulation code GENESIS, we apply our model-based optimization methods to a case of the future FEL at the MAX IV Laboratory (Lund, Sweden, as well as a case of the LCLS-II facility (Stanford, USA.

  7. Planar Thinned Arrays: Optimization and Subarray Based Adaptive Processing

    Directory of Open Access Journals (Sweden)

    P. Lombardo

    2013-01-01

    Full Text Available A new approach is presented for the optimized design of a planar thinned array; the proposed strategy works with single antenna elements or with small sets of different subarray types, properly located on a planar surface. The optimization approach is based on the maximization of an objective function accounting for side lobe level and considering a fixed number of active elements/subarrays. The proposed technique is suitable for different shapes of the desired output array, allowing the achievement of the desired directivity properties on the corresponding antenna pattern. The use of subarrays with a limited number of different shapes is relevant for industrial production, which would benefit from reduced design and manufacturing costs. The resulting modularity allows scalable antenna designs for different applications. Moreover, subarrays can be arranged in a set of subapertures, each connected to an independent receiving channel. Therefore, adaptive processing techniques could be applied to cope with and mitigate clutter echoes and external electromagnetic interferences. The performance of adaptive techniques with subapertures taken from the optimized thinned array is evaluated against assigned clutter and jamming scenarios and compared to the performance achievable considering a subarray based filled array with the same number of active elements.

  8. Neural network based optimal control of HVAC&R systems

    Science.gov (United States)

    Ning, Min

    Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the

  9. Binary Particle Swarm Optimization based Biclustering of Web usage Data

    CERN Document Server

    Bagyamani, R Rathipriya K Thangavel J

    2011-01-01

    Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketin...

  10. Optimization of Surface Acoustic Wave-Based Rate Sensors

    Directory of Open Access Journals (Sweden)

    Fangqian Xu

    2015-10-01

    Full Text Available The optimization of an surface acoustic wave (SAW-based rate sensor incorporating metallic dot arrays was performed by using the approach of partial-wave analysis in layered media. The optimal sensor chip designs, including the material choice of piezoelectric crystals and metallic dots, dot thickness, and sensor operation frequency were determined theoretically. The theoretical predictions were confirmed experimentally by using the developed SAW sensor composed of differential delay line-oscillators and a metallic dot array deposited along the acoustic wave propagation path of the SAW delay lines. A significant improvement in sensor sensitivity was achieved in the case of 128° YX LiNbO3, and a thicker Au dot array, and low operation frequency were used to structure the sensor.

  11. Optimism in Reinforcement Learning Based on Kullback-Leibler Divergence

    CERN Document Server

    Filippi, Sarah; Garivier, Aurélien

    2010-01-01

    We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carrying out extended value iterations, under a constraint of consistency with the estimated model transition probabilities. In this paper, we strongly argue in favor of using the Kullback-Leibler (KL) divergence for this purpose. By study- ing the linear maximization problem under KL constraints, we provide an efficient algorithm for solving KL-optimistic extended value iteration. When implemented within the structure of UCRL2, the near-optimal method introduced by [Auer et al, 2008], this algorithm also achieves bounded regrets in the undiscounted case. We however provide some geometric arguments as well as a concrete illustration on a simulated example to explain the observed improved practical behavior, particularly when the MDP has reduced connectivity. To analyze this new algorithm, termed KL-UCRL, we also rely on recent deviation bou...

  12. Electret-based cantilever energy harvester: design and optimization

    CERN Document Server

    Boisseau, S; Sylvestre, A

    2011-01-01

    We report in this paper the design, the optimization and the fabrication of an electret-based cantilever energy harvester. We develop the mechanical and the electrostatic equations of such a device and its implementation using Finite Elements (FEM) and Matlab in order to get an accurate model. This model is then used in an optimization process. A macroscopic prototype (3.2cm^{2}) was built with a silicon cantilever and a Teflon\\textregistered electret. Thanks to this prototype, we manage to harvest 17\\muW with ambient-type vibrations of 0.2g on a load of 210M{\\Omega}. The experimental results are consistent with simulation results.

  13. Celestial Navigation Fix Based on Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Tsou Ming-Cheng

    2015-09-01

    Full Text Available A technique for solving celestial fix problems is proposed in this study. This method is based on Particle Swarm Optimization from the field of swarm intelligence, utilizing its superior optimization and searching abilities to obtain the most probable astronomical vessel position. In addition to being applicable to two-body fix, multi-body fix, and high-altitude observation problems, it is also less reliant on the initial dead reckoning position. Moreover, by introducing spatial data processing and display functions in a Geographical Information System, calculation results and chart work used in Circle of Position graphical positioning can both be integrated. As a result, in addition to avoiding tedious and complicated computational and graphical procedures, this work has more flexibility and is more robust when compared to other analytical approaches.

  14. R2-Based Multi/Many-Objective Particle Swarm Optimization

    Science.gov (United States)

    Toscano, Gregorio; Barron-Zambrano, Jose Hugo; Tello-Leal, Edgar

    2016-01-01

    We propose to couple the R2 performance measure and Particle Swarm Optimization in order to handle multi/many-objective problems. Our proposal shows that through a well-designed interaction process we could maintain the metaheuristic almost inalterable and through the R2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approach is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed algorithm produces results that are competitive with respect to those obtained by four well-known MOEAs. Additionally, we validate our proposal in many-objective optimization problems. In these problems, our approach showed its main strength, since it could outperform another well-known indicator-based MOEA. PMID:27656200

  15. Adaptive Estimation of Intravascular Shear Rate Based on Parameter Optimization

    Science.gov (United States)

    Nitta, Naotaka; Takeda, Naoto

    2008-05-01

    The relationships between the intravascular wall shear stress, controlled by flow dynamics, and the progress of arteriosclerosis plaque have been clarified by various studies. Since the shear stress is determined by the viscosity coefficient and shear rate, both factors must be estimated accurately. In this paper, an adaptive method for improving the accuracy of quantitative shear rate estimation was investigated. First, the parameter dependence of the estimated shear rate was investigated in terms of the differential window width and the number of averaged velocity profiles based on simulation and experimental data, and then the shear rate calculation was optimized. The optimized result revealed that the proposed adaptive method of shear rate estimation was effective for improving the accuracy of shear rate calculation.

  16. Multi-objective optimization of process based on resource capability

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    To improve the practicability, suitability and accuracy of the trade-off among time, cost and quality of a process, a method based on resource capability is introduced. Through analyzing the relationship between an activity and its' supporting resource, the model trades off the time, cost and quality by changing intensity of labor or changing the types of supporting resource or units of labor of resource in a certain time respectively according to the different types of its' supporting resources. Through contrasting this method with the model of unit time cost corresponding to different quality levels and inter-related linear programming model of time, cost and quality for process optimizing, it is shown that this model does not only cover the above two models but also can describe some conditions the above two models can not express. The method supports to select different function to optimize a process according to different types of its supporting resource.

  17. Optimal Source-Based Filtering of Malicious Traffic

    CERN Document Server

    Soldo, Fabio; Markopoulou, Athina

    2010-01-01

    In this paper, we consider the problem of blocking malicious traffic on the Internet, via source-based filtering. In particular, we consider filtering via access control lists (ACLs): these are already available at the routers today but are a scarce resource because they are stored in the expensive ternary content addressable memory (TCAM). Aggregation (by filtering source prefixes instead of individual IP addresses) helps reduce the number of filters, but comes also at the cost of blocking legitimate traffic originating from the filtered prefixes. We show how to optimally choose which source prefixes to filter, for a variety of realistic attack scenarios and operators' policies. In each scenario, we design optimal, yet computationally efficient, algorithms. Using logs from Dshield.org, we evaluate the algorithms and demonstrate that they bring significant benefit in practice.

  18. Optimization of integer wavelet transforms based on difference correlation structures.

    Science.gov (United States)

    Li, Hongliang; Liu, Guizhong; Zhang, Zhongwei

    2005-11-01

    In this paper, a novel lifting integer wavelet transform based on difference correlation structure (DCCS-LIWT) is proposed. First, we establish a relationship between the performance of a linear predictor and the difference correlations of an image. The obtained results provide a theoretical foundation for the following construction of the optimal lifting filters. Then, the optimal prediction lifting coefficients in the sense of least-square prediction error are derived. DCCS-LIWT puts heavy emphasis on image inherent dependence. A distinct feature of this method is the use of the variance-normalized autocorrelation function of the difference image to construct a linear predictor and adapt the predictor to varying image sources. The proposed scheme also allows respective calculations of the lifting filters for the horizontal and vertical orientations. Experimental evaluation shows that the proposed method produces better results than the other well-known integer transforms for the lossless image compression.

  19. Optimization-based design of a heat flux concentrator

    Science.gov (United States)

    Peralta, Ignacio; Fachinotti, Víctor D.; Ciarbonetti, Ángel A.

    2017-01-01

    To gain control over the diffusive heat flux in a given domain, one needs to engineer a thermal metamaterial with a specific distribution of the generally anisotropic thermal conductivity throughout the domain. Until now, the appropriate conductivity distribution was usually determined using transformation thermodynamics. By this way, only a few particular cases of heat flux control in simple domains having simple boundary conditions were studied. Thermal metamaterials based on optimization algorithm provides superior properties compared to those using the previous methods. As a more general approach, we propose to define the heat control problem as an optimization problem where we minimize the error in guiding the heat flux in a given way, taking as design variables the parameters that define the variable microstructure of the metamaterial. In the present study we numerically demonstrate the ability to manipulate heat flux by designing a device to concentrate the thermal energy to its center without disturbing the temperature profile outside it. PMID:28084451

  20. Sequential optimization and reliability assessment based on dimension reduction method for accurate and efficient reliability-based design optimization

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Jong Min; Lee, Byung Chai; Lee, Ik Jin [KAIST, Daejeon (Korea, Republic of)

    2015-04-15

    This study develops an efficient and accurate methodology for reliability-based design optimization (RBDO) by combining the most probable point (MPP)-based dimension reduction method (DRM) to enhance accuracy and the sequential optimization and reliability assessment (SORA) to enhance efficiency. In many researches, first-order reliability method (FORM) has been utilized for RBDO methods due to its efficiency and simplicity. However, it might not be accurate enough for highly nonlinear performance functions. Therefore, the MPP-based DRM is introduced for the accurate reliability assessment in this study. Even though the MPP-based DRM significantly improves the accuracy, additional computations for the moment-based integration are required. It is desirable to reduce the number of reliability analyses in the RBDO process. Since decoupled approaches such as SORA reduce necessary reliability analyses considerably, DRM-based SORA is proposed in this study for accurate and efficient RBDO. Furthermore, convex linearization is introduced to approximate inactive probabilistic constraints to additionally improve the efficiency. The efficiency and accuracy of the proposed method are verified through numerical examples.

  1. A New Approach to Optimal Allocation of Reactive Power Ancillary Service in Distribution Systems in the Presence of Distributed Energy Resources

    Directory of Open Access Journals (Sweden)

    Abouzar Samimi

    2015-11-01

    Full Text Available One of the most important Distribution System Operators (DSO schemes addresses the Volt/Var control (VVC problem. Developing a cost-based reactive power dispatch model for distribution systems, in which the reactive powers are appropriately priced, can motivate Distributed Energy Resources (DERs to participate actively in VVC. In this paper, new reactive power cost models for DERs, including synchronous machine-based DGs and wind turbines (WTs, are formulated based on their capability curves. To address VVC in the context of competitive electricity markets in distribution systems, first, in a day-ahead active power market, the initial active power dispatch of generation units is estimated considering environmental and economic aspects. Based on the results of the initial active power dispatch, the proposed VVC model is executed to optimally allocate reactive power support among all providers. Another novelty of this paper lies in the pricing scheme that rewards transformers and capacitors for tap and step changing, respectively, while incorporating the reactive power dispatch model. A Benders decomposition algorithm is employed as a solution method to solve the proposed reactive power dispatch, which is a mixed integer non-linear programming (MINLP problem. Finally, a typical 22-bus distribution network is used to verify the efficiency of the proposed method.

  2. Optimization based tumor classification from microarray gene expression data.

    Directory of Open Access Journals (Sweden)

    Onur Dagliyan

    Full Text Available BACKGROUND: An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types. METHODOLOGY/PRINCIPAL FINDINGS: We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL, small round blue cell tumors (SRBCT to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described. CONCLUSIONS/SIGNIFICANCE: The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on

  3. 通过电动汽车与电网互动减少弃风的商业模式与日前优化调度策略%Business Model and Day-ahead Dispatch Strategy to Reduce Wind Power Curtailment Through Vehicle-to-Grid

    Institute of Scientific and Technical Information of China (English)

    项顶; 胡泽春; 宋永华; 丁华杰

    2015-01-01

    电动汽车接受运营商调控参与车网互动(vehicle-to-grid,V2G)能够更好地与间歇性新能源发电相配合,从而创造巨大的经济效益和社会效益,而这种V2G模式需要相应的商业模式作为支撑.通过分析受控电动汽车的特征,提出电动汽车用户、运营商(Aggregator)、电网公司和弃风电场合作的V2G商业合同模式.建立以运营商期望收益最大化为目标,以满足用户个体需求和电费补偿约束,考虑弃风功率限制、机组调节速率限制的运营商日前优化调度模型.提出了针对所建非线性混合整数规划问题的求解算法.以京津唐电网为例,验证了所提合同模式、调度策略及求解算法的有效性和准确性.%Vehicle-to-Grid (V2G) under the management of electric vehicle (EV) aggregators has great potential to interact with renewable energy generation. This interactive mode could create enormous economic and social benefits, however, it lacks appropriate business model as a support. In this paper, a business model based on cooperation among EV users, aggregators, electric companies and wind farms was proposed. Under constraints of EV users' charging demand and discharging compensation, curtailed wind power profile and unit ramp rate, aday-ahead optimal scheduling formulation for aggregators to maximize their expected revenue was established. An algorithm which can solve this kind of nonlinear mixed integer programming problem was also proposed. Simulation based on data from the Beijing- Tianjin-Tangshan power grid is conducted and the results prove the effectiveness of the proposed business and scheduling models and the accuracy of proposed algorithm.

  4. Optimism

    Science.gov (United States)

    Carver, Charles S.; Scheier, Michael F.; Segerstrom, Suzanne C.

    2010-01-01

    Optimism is an individual difference variable that reflects the extent to which people hold generalized favorable expectancies for their future. Higher levels of optimism have been related prospectively to better subjective well-being in times of adversity or difficulty (i.e., controlling for previous well-being). Consistent with such findings, optimism has been linked to higher levels of engagement coping and lower levels of avoidance, or disengagement, coping. There is evidence that optimism is associated with taking proactive steps to protect one's health, whereas pessimism is associated with health-damaging behaviors. Consistent with such findings, optimism is also related to indicators of better physical health. The energetic, task-focused approach that optimists take to goals also relates to benefits in the socioeconomic world. Some evidence suggests that optimism relates to more persistence in educational efforts and to higher later income. Optimists also appear to fare better than pessimists in relationships. Although there are instances in which optimism fails to convey an advantage, and instances in which it may convey a disadvantage, those instances are relatively rare. In sum, the behavioral patterns of optimists appear to provide models of living for others to learn from. PMID:20170998

  5. Optimization of hydrofoil for tidal current turbine based on particle swarm optimization and computational fluid dynamic method

    OpenAIRE

    Zhang De-Sheng; Chen Jian; Shi Wei-Dong; Shi Lei; Geng Lin-Lin

    2016-01-01

    Both efficiency and cavitation performance of the hydrofoil are the key technologies to design the tidal current turbine. In this paper, the hydrofoil efficiency and lift coefficient were improved based on particle swarm optimization method and XFoil codes. The cavitation performance of the optimized hydrofoil was also discussed by the computational fluid dynamic. Numerical results show the efficiency of the optimized hydrofoil was improved 11% ranging from...

  6. Simulation-based optimal Bayesian experimental design for nonlinear systems

    KAUST Repository

    Huan, Xun

    2013-01-01

    The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. We propose a general mathematical framework and an algorithmic approach for optimal experimental design with nonlinear simulation-based models; in particular, we focus on finding sets of experiments that provide the most information about targeted sets of parameters.Our framework employs a Bayesian statistical setting, which provides a foundation for inference from noisy, indirect, and incomplete data, and a natural mechanism for incorporating heterogeneous sources of information. An objective function is constructed from information theoretic measures, reflecting expected information gain from proposed combinations of experiments. Polynomial chaos approximations and a two-stage Monte Carlo sampling method are used to evaluate the expected information gain. Stochastic approximation algorithms are then used to make optimization feasible in computationally intensive and high-dimensional settings. These algorithms are demonstrated on model problems and on nonlinear parameter inference problems arising in detailed combustion kinetics. © 2012 Elsevier Inc.

  7. Reliability-Based Optimal Design for Very Large Floating Structure

    Institute of Scientific and Technical Information of China (English)

    ZHANG Shu-hua(张淑华); FUJIKUBO Masahiko

    2003-01-01

    Costs and losses induced by possible future extreme environmental conditions and difficulties in repairing post-yielding damage strongly suggest the need for proper consideration in design rather than just life loss prevention. This can be addressed through the development of design methodology that balances the initial cost of the very large floating structure (VLFS) against the expected potential losses resulting from future extreme wave-induced structural damage. Here, the development of a methodology for determining optimal, cost-effective design will be presented and applied to a VLFS located in the Tokyo bay. Optimal design criteria are determined based on the total expected life-cycle cost and acceptable damage probability and curvature of the structure, and a set of sizes of the structure are obtained. The methodology and applications require expressions of the initial cost and the expected life-cycle damage cost as functions of the optimal design variables. This study includes the methodology, total life-cycle cost function, structural damage modeling, and reliability analysis.

  8. Reliability-based design optimization for nonlinear energy harvesters

    Science.gov (United States)

    Seong, Sumin; Lee, Soobum; Hu, Chao

    2015-03-01

    The power output of a vibration energy harvesting device is highly sensitive to uncertainties in materials, manufacturing, and operating conditions. Although the use of a nonlinear spring (e.g., snap-through mechanism) in energy harvesting device has been reported to reduce the sensitivity of power output with respect to the excitation frequency, the nonlinear spring characteristic remains significantly sensitive and it causes unreliable power generation. In this paper, we present a reliability-based design optimization (RBDO) study of vibration energy harvesters. For a nonlinear harvester, a purely mechanical nonlinear spring design implemented in the middle of cantilever beam harvester is considered in the study. This design has the curved section in the center of beam that causes bi-stable configuration. When vibrating, the inertia of the tip mass activates the curved shell to cause snap-through buckling and make the nature of vibration nonlinear. In this paper, deterministic optimization (DO) is performed to obtain deterministic optimum of linear and nonlinear energy harvester configuration. As a result of the deterministic optimization, an optimum bi-stable vibration configuration of nonlinear harvester can be obtained for reliable power generation despite uncertainty on input vibration condition. For the linear harvester, RBDO is additionally performed to find the optimum design that satisfies a target reliability on power generation, while accounting for uncertainty in material properties and geometric parameters.

  9. Corner Sort for Pareto-Based Many-Objective Optimization.

    Science.gov (United States)

    Wang, Handing; Yao, Xin

    2014-01-01

    Nondominated sorting plays an important role in Pareto-based multiobjective evolutionary algorithms (MOEAs). When faced with many-objective optimization problems multiobjective optimization problems (MOPs) with more than three objectives, the number of comparisons needed in nondominated sorting becomes very large. In view of this, a new corner sort is proposed in this paper. Corner sort first adopts a fast and simple method to obtain a nondominated solution from the corner solutions, and then uses the nondominated solution to ignore the solutions dominated by it to save comparisons. Obtaining the nondominated solutions requires much fewer objective comparisons in corner sort. In order to evaluate its performance, several state-of-the-art nondominated sorts are compared with our corner sort on three kinds of artificial solution sets of MOPs and the solution sets generated from MOEAs on benchmark problems. On one hand, the experiments on artificial solution sets show the performance on the solution sets with different distributions. On the other hand, the experiments on the solution sets generated from MOEAs show the influence that different sorts bring to MOEAs. The results show that corner sort performs well, especially on many-objective optimization problems. Corner sort uses fewer comparisons than others.

  10. Weather forecast-based optimization of integrated energy systems.

    Energy Technology Data Exchange (ETDEWEB)

    Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

    2009-03-01

    In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.

  11. Robustness-Based Design Optimization Under Data Uncertainty

    Science.gov (United States)

    Zaman, Kais; McDonald, Mark; Mahadevan, Sankaran; Green, Lawrence

    2010-01-01

    This paper proposes formulations and algorithms for design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information), from the perspective of system robustness. The proposed formulations deal with epistemic uncertainty arising from both sparse and interval data without any assumption about the probability distributions of the random variables. A decoupled approach is proposed in this paper to un-nest the robustness-based design from the analysis of non-design epistemic variables to achieve computational efficiency. The proposed methods are illustrated for the upper stage design problem of a two-stage-to-orbit (TSTO) vehicle, where the information on the random design inputs are only available as sparse point and/or interval data. As collecting more data reduces uncertainty but increases cost, the effect of sample size on the optimality and robustness of the solution is also studied. A method is developed to determine the optimal sample size for sparse point data that leads to the solutions of the design problem that are least sensitive to variations in the input random variables.

  12. Performance Optimization based Spectrum Analysis on OFRA and EDFA Devices

    Directory of Open Access Journals (Sweden)

    Liu Liying

    2013-07-01

    Full Text Available As the key devices, erbium doped fiber amplifier (EDFA and optical Raman fiber amplifier (OFRA have been widely applied in the fields of optical communication, sensing and measurement. However, the performance optimization is always one of the hot topics in the study of optical fiber amplifiers, because its output characteristics are hardly dependent to the key designing parameters. In this paper, in order to cope with such problem, we adopt the novel analysis based spectrum to study the output performance of EDFA and OFRA systems, respectively. Through simulating the operation of the two amplifying system, their output characteristics are first demonstrated with the various parameters. And according to the numerical results obtained, the key designing parameters of EDFA and OFRA systems are determinate, and the performance of amplifying systems are improved and optimized obviously in terms of output power, signal noise ratio, and the level of gain flatness.   Keywords: Fiber Raman Amplifier, Erbium Doped Fiber Amplifier, Performance optimization, Spectrum analysis, Simulation.  

  13. Optimal sensor placement using FRFs-based clustering method

    Science.gov (United States)

    Li, Shiqi; Zhang, Heng; Liu, Shiping; Zhang, Zhe

    2016-12-01

    The purpose of this work is to develop an optimal sensor placement method by selecting the most relevant degrees of freedom as actual measure position. Based on observation matrix of a structure's frequency response, two optimal criteria are used to avoid the information redundancy of the candidate degrees of freedom. By using principal component analysis, the frequency response matrix can be decomposed into principal directions and their corresponding singular. A relatively small number of principal directions will maintain a system's dominant response information. According to the dynamic similarity of each degree of freedom, the k-means clustering algorithm is designed to classify the degrees of freedom, and effective independence method deletes the sensors which are redundant of each cluster. Finally, two numerical examples and a modal test are included to demonstrate the efficient of the derived method. It is shown that the proposed method provides a way to extract sub-optimal sets and the selected sensors are well distributed on the whole structure.

  14. Energy Optimal Control Strategy of PHEV Based on PMP Algorithm

    Directory of Open Access Journals (Sweden)

    Tiezhou Wu

    2017-01-01

    Full Text Available Under the global voice of “energy saving” and the current boom in the development of energy storage technology at home and abroad, energy optimal control of the whole hybrid electric vehicle power system, as one of the core technologies of electric vehicles, is bound to become a hot target of “clean energy” vehicle development and research. This paper considers the constraints to the performance of energy storage system in Parallel Hybrid Electric Vehicle (PHEV, from which lithium-ion battery frequently charges/discharges, PHEV largely consumes energy of fuel, and their are difficulty in energy recovery and other issues in a single cycle; the research uses lithium-ion battery combined with super-capacitor (SC, which is hybrid energy storage system (Li-SC HESS, working together with internal combustion engine (ICE to drive PHEV. Combined with PSO-PI controller and Li-SC HESS internal power limited management approach, the research proposes the PHEV energy optimal control strategy. It is based on revised Pontryagin’s minimum principle (PMP algorithm, which establishes the PHEV vehicle simulation model through ADVISOR software and verifies the effectiveness and feasibility. Finally, the results show that the energy optimization control strategy can improve the instantaneity of tracking PHEV minimum fuel consumption track, implement energy saving, and prolong the life of lithium-ion batteries and thereby can improve hybrid energy storage system performance.

  15. Optimization-based mesh correction with volume and convexity constraints

    Science.gov (United States)

    D'Elia, Marta; Ridzal, Denis; Peterson, Kara J.; Bochev, Pavel; Shashkov, Mikhail

    2016-05-01

    We consider the problem of finding a mesh such that 1) it is the closest, with respect to a suitable metric, to a given source mesh having the same connectivity, and 2) the volumes of its cells match a set of prescribed positive values that are not necessarily equal to the cell volumes in the source mesh. This volume correction problem arises in important simulation contexts, such as satisfying a discrete geometric conservation law and solving transport equations by incremental remapping or similar semi-Lagrangian transport schemes. In this paper we formulate volume correction as a constrained optimization problem in which the distance to the source mesh defines an optimization objective, while the prescribed cell volumes, mesh validity and/or cell convexity specify the constraints. We solve this problem numerically using a sequential quadratic programming (SQP) method whose performance scales with the mesh size. To achieve scalable performance we develop a specialized multigrid-based preconditioner for optimality systems that arise in the application of the SQP method to the volume correction problem. Numerical examples illustrate the importance of volume correction, and showcase the accuracy, robustness and scalability of our approach.

  16. Production Cost Optimization Model Based on CODP in Mass Customization

    Directory of Open Access Journals (Sweden)

    Yanhong Qin

    2013-01-01

    Full Text Available The key for enterprises to implement the postponement strategy is the right decision on the location of Customer Order Decoupling Point (CODP so as to achieve the scope economics of mass customization and scale economics of mass production fully. To deal with production cost optimization problem of postponement system based on various situation of CODP, a basic model of production cost and its M/M/1 extended model are proposed and compared so as to optimize the overall production cost of the postponement system. The production modes can be classified as MTS (make to stock, ATO (assemble to order, MTO (make to order and ETO (engineering to order according to the inventory location, and the postponed production system considered here includes manufacturing cost, semi-finished inventory cost and customer waiting cost caused by delaying delivery. By Matlab simulation, we can compute the optimal location of CODP in each production mode, which can provide some management insight for the manufacturer to decide the right production mode and utilize the resources efficiently.

  17. Optimal Path Planning for Minimizing Base Disturbance of Space Robot

    Directory of Open Access Journals (Sweden)

    Xiao-Peng Wei

    2016-03-01

    Full Text Available The path planning of free-floating space robot in space on-orbit service has been paid more and more attention. The problem is more complicated because of the interaction between the space robot and base. Therefore, it is necessary to minimize the base position and attitude disturbance to improve the path planning of free-floating space robot, reducing the fuel consumption for the position and attitude maintenance. In this paper, a reasonable path planning method to solve the problem is presented, which is feasible and relatively simple. First, the kinematic model of 6 degrees of freedom free-floating space robot is established. And then the joint angles are parameterized using the 7th order polynomial sine functions. The fitness function is defined according to the position and attitude of minimizing base disturbance and constraints of space robot. Furthermore, an improved chaotic particle swarm optimization (ICPSO is presented. The proposed algorithm is compared with the standard PSO and CPSO algorithm in the literature by the experimental simulation. The simulation results demonstrate that the proposed algorithm is more effective than the two other approaches, such as easy to find the optimal solution, and this method could provide a satisfactory path for the free-floating space robot.

  18. Optimal Path Planning for Minimizing Base Disturbance of Space Robot

    Directory of Open Access Journals (Sweden)

    Xiao-Peng Wei

    2016-03-01

    Full Text Available The path planning of free-floating space robot in space on-orbit service has been paid more and more attention. The problem is more complicated because of the interaction between the space robot and base. Therefore, it is necessary to minimize the base position and attitude disturbance to improve the path planning of free-floating space robot, reducing the fuel consumption for the position and attitude maintenance. In this paper, a reasonable path planning method to solve the problem is presented, which is feasible and relatively simple. First, the kinematic model of 6 degrees of freedom free-floating space robot is established. And then the joint angles are parameterized using the 7th order polynomial sine functions. The fitness function is defined according to the position and attitude of minimizing base disturbance and constraints of space robot. Furthermore, an improved chaotic particle swarm optimization (ICPSO is presented. The proposed algorithm is compared with the standard PSO and CPSO algorithm in the literature by the experimental simulation. The simulation results demonstrate that the proposed algorithm is more effective than the two other approaches, such as easy to find the optimal solution, and this method could provide a satisfactory path for the free-floating space robot.

  19. Price-based Optimal Control of Electrical Power Systems

    Energy Technology Data Exchange (ETDEWEB)

    Jokic, A.

    2007-09-10

    The research presented in this thesis is motivated by the following issue of concern for the operation of future power systems: Future power systems will be characterized by significantly increased uncertainties at all time scales and, consequently, their behavior in time will be difficult to predict. In Chapter 2 we will present a novel explicit, dynamic, distributed feedback control scheme that utilizes nodal-prices for real-time optimal power balance and network congestion control. The term explicit means that the controller is not based on solving an optimization problem on-line. Instead, the nodal prices updates are based on simple, explicitly defined and easily comprehensible rules. We prove that the developed control scheme, which acts on the measurements from the current state of the system, always provide the correct nodal prices. In Chapter 3 we will develop a novel, robust, hybrid MPC control (model predictive controller) scheme for power balance control with hard constraints on line power flows and network frequency deviations. The developed MPC controller acts in parallel with the explicit controller from Chapter 2, and its task is to enforce the constraints during the transient periods following suddenly occurring power imbalances in the system. In Chapter 4 the concept of autonomous power networks will be presented as a concise formulation to deal with economic, technical and reliability issues in power systems with a large penetration of distributed generating units. With autonomous power networks as new market entities, we propose a novel operational structure of ancillary service markets. In Chapter 5 we will consider the problem of controlling a general linear time-invariant dynamical system to an economically optimal operating point, which is defined by a multiparametric constrained convex optimization problem related with the steady-state operation of the system. The parameters in the optimization problem are values of the exogenous inputs to

  20. Utilization-Based Modeling and Optimization for Cognitive Radio Networks

    Science.gov (United States)

    Liu, Yanbing; Huang, Jun; Liu, Zhangxiong

    The cognitive radio technique promises to manage and allocate the scarce radio spectrum in the highly varying and disparate modern environments. This paper considers a cognitive radio scenario composed of two queues for the primary (licensed) users and cognitive (unlicensed) users. According to the Markov process, the system state equations are derived and an optimization model for the system is proposed. Next, the system performance is evaluated by calculations which show the rationality of our system model. Furthermore, discussions among different parameters for the system are presented based on the experimental results.

  1. Traffic optimization in transport networks based on local routing

    Science.gov (United States)

    Scellato, S.; Fortuna, L.; Frasca, M.; Gómez-Gardeñes, J.; Latora, V.

    2010-01-01

    Congestion in transport networks is a topic of theoretical interest and practical importance. In this paper we study the flow of vehicles in urban street networks. In particular, we use a cellular automata model on a complex network to simulate the motion of vehicles along streets, coupled with a congestion-aware routing at street crossings. Such routing makes use of the knowledge of agents about traffic in nearby roads and allows the vehicles to dynamically update the routes towards their destinations. By implementing the model in real urban street patterns of various cities, we show that it is possible to achieve a global traffic optimization based on local agent decisions.

  2. Simulation Based Optimization for World Line Card Production System

    Directory of Open Access Journals (Sweden)

    Sinan APAK

    2012-07-01

    Full Text Available Simulation based decision support system is one of the commonly used tool to examine complex production systems. The simulation approach provides process modules which can be adjusted with certain parameters by using data relatively easily obtainable in production process. World Line Card production system simulation is developed to evaluate the optimality of existing production line via using discrete event simulation model with variaty of alternative proposals. The current production system is analysed by a simulation model emphasizing the bottlenecks and the poorly utilized production line. Our analysis identified some improvements and efficient solutions for the existing system.

  3. TOPOLOGY DESIGN OPTIMIZATION BASED ON BIOTIC BRANCH NET

    Institute of Scientific and Technical Information of China (English)

    Ding Xiaohong; Li Guojie; Yamazaki Koestu

    2005-01-01

    The biotic branch nets are extreme high-tech product. In order to achieve a certain functional objective, they can adjust their growth direction and growth velocity by according to the varying growth environment. An innovative and effective methodology of topology design optimization based on the growth mechanism of biotic branch nets is suggested, and it is applied to a layout design problem of a conductive cooling channel in a heat transfer system. The effectiveness of the method is validated by the FEM analysis.

  4. Stability analysis of underground engineering based on multidisciplinary design optimization

    Institute of Scientific and Technical Information of China (English)

    MA Rong; ZHOU Ke-ping; GAO Feng

    2008-01-01

    Aiming at characteristics of underground engineering,analyzed the feasibility of Multidisciplinary Design Optimization (MDO) used in underground engineering,and put forward a modularization-based MDO method and the idea of MDO to resolve problems in stability analysis,proving the validity and feasibility of using MDO in underground engineering.Characteristics of uncertainty,complexity and nonlinear become bottle-neck to carry on underground engineering stability analysis by MDO.Therefore,the application of MDO in underground engineering stability analysis is still at a stage of exploration,which need some deep research.

  5. Stability analysis of underground engineering based on multidisciplinary design optimization

    Institute of Scientific and Technical Information of China (English)

    MA Rong; ZHOU Ke-ping; GAO Feng

    2008-01-01

    Aiming at characteristics of underground engineering, analyzed the feasibility of Multidisciplinary Design Optimization (MDO) used in underground engineering, and put forward a modularization-based MDO method and the idea of MDO to resolve problems in stability analysis, proving the validity and feasibility of using MDO in underground engi-neering. Characteristics of uncertainty, complexity and nonlinear become bottle-neck to carry on underground engineering stability analysis by MDO. Therefore, the application of MDO in underground engineering stability analysis is still at a stage of exploration, which need some deep research.

  6. Optimization

    CERN Document Server

    Pearce, Charles

    2009-01-01

    Focuses on mathematical structure, and on real-world applications. This book includes developments in several optimization-related topics such as decision theory, linear programming, turnpike theory, duality theory, convex analysis, and queuing theory.

  7. An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization

    Directory of Open Access Journals (Sweden)

    Feng Zou

    2016-01-01

    Full Text Available An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO, which is considering the teacher’s behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.

  8. Optimal multi-floor plant layout based on the mathematical programming and particle swarm optimization.

    Science.gov (United States)

    Lee, Chang Jun

    2015-01-01

    In the fields of researches associated with plant layout optimization, the main goal is to minimize the costs of pipelines and pumping between connecting equipment under various constraints. However, what is the lacking of considerations in previous researches is to transform various heuristics or safety regulations into mathematical equations. For example, proper safety distances between equipments have to be complied for preventing dangerous accidents on a complex plant. Moreover, most researches have handled single-floor plant. However, many multi-floor plants have been constructed for the last decade. Therefore, the proper algorithm handling various regulations and multi-floor plant should be developed. In this study, the Mixed Integer Non-Linear Programming (MINLP) problem including safety distances, maintenance spaces, etc. is suggested based on mathematical equations. The objective function is a summation of pipeline and pumping costs. Also, various safety and maintenance issues are transformed into inequality or equality constraints. However, it is really hard to solve this problem due to complex nonlinear constraints. Thus, it is impossible to use conventional MINLP solvers using derivatives of equations. In this study, the Particle Swarm Optimization (PSO) technique is employed. The ethylene oxide plant is illustrated to verify the efficacy of this study.

  9. Statistical Testing of Optimality Conditions in Multiresponse Simulation-Based Optimization (Replaced by Discussion Paper 2007-45)

    NARCIS (Netherlands)

    Bettonvil, B.W.M.; Del Castillo, E.; Kleijnen, Jack P.C.

    2005-01-01

    This paper derives a novel procedure for testing the Karush-Kuhn-Tucker (KKT) first-order optimality conditions in models with multiple random responses.Such models arise in simulation-based optimization with multivariate outputs.This paper focuses on expensive simulations, which have small sample

  10. Probability-Based Software for Grid Optimization: Improved Power System Operations Using Advanced Stochastic Optimization

    Energy Technology Data Exchange (ETDEWEB)

    None

    2012-02-24

    GENI Project: Sandia National Laboratories is working with several commercial and university partners to develop software for market management systems (MMSs) that enable greater use of renewable energy sources throughout the grid. MMSs are used to securely and optimally determine which energy resources should be used to service energy demand across the country. Contributions of electricity to the grid from renewable energy sources such as wind and solar are intermittent, introducing complications for MMSs, which have trouble accommodating the multiple sources of price and supply uncertainties associated with bringing these new types of energy into the grid. Sandia’s software will bring a new, probability-based formulation to account for these uncertainties. By factoring in various probability scenarios for electricity production from renewable energy sources in real time, Sandia’s formula can reduce the risk of inefficient electricity transmission, save ratepayers money, conserve power, and support the future use of renewable energy.

  11. Optimal operation strategy of battery energy storage system to real-time electricity price in Denmark

    DEFF Research Database (Denmark)

    Hu, Weihao; Chen, Zhe; Bak-Jensen, Birgitte

    2010-01-01

    Since the hourly spot market price is available one day ahead, the price could be transferred to the consumers and they may have some motivations to install an energy storage system in order to save their energy costs. This paper presents an optimal operation strategy for a battery energy storage...... system (BESS) in relation to the real-time electricity price in order to achieve the maximum profits of the BESS. The western Danish power system, which is currently the grid area in the world that has the largest share of wind power in its generation profiles and may represent the future of electricity...

  12. A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model.

    Science.gov (United States)

    Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao

    2014-09-01

    Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.

  13. Optimal placement of dampers and actuators based on stochastic approach

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    A general method is developed for optimal application of dampers and actuators by installing them at optimal location on seismic-resistant structures. The study includes development of a statistical criterion, formulation of a general optimization problem and establishment of a solution procedure. Numerical analysis of the seismic response in time-history of controlled structures is used to verify the proposed method for optimal device application and to demonstrate the effectiveness of seismic response control with optimal device location. This study shows that the proposed method for the optimal device application is simple and general, and that the optimally applied dampers and actuators are very efficient for seismic response reduction.

  14. Coherent Network Optimizing of Rail-Based Urban Mass Transit

    Directory of Open Access Journals (Sweden)

    Ying Zhang

    2012-01-01

    Full Text Available An efficient public transport is more than ever a crucial factor when it comes to the quality of life and competitiveness of many cities and regions in Asia. In recent years, the rail-based urban mass transit has been regarded as one of the key means to overcoming the great challenges in Chinese megacities. The purpose of this study is going to develop a coherent network optimizing for rail-based urban mass transit to find the best alternatives for the user and to demonstrate how to meet sustainable development needs and to match the enormous capacity requirements simultaneously. This paper presents an introduction to the current situation of the important lines, and transfer points in the metro system Shanghai. The insufficient aspects are analyzed and evaluated; while the optimizing ideas and measurements are developed and concreted. A group of examples are used to illustrate the approach. The whole study could be used for the latest reference for other megacities which have to be confronted with the similar situations and processes with enormous dynamic travel and transport demands.

  15. Design optimization of PVDF-based piezoelectric energy harvesters.

    Science.gov (United States)

    Song, Jundong; Zhao, Guanxing; Li, Bo; Wang, Jin

    2017-09-01

    Energy harvesting is a promising technology that powers the electronic devices via scavenging the ambient energy. Piezoelectric energy harvesters have attracted considerable interest for their high conversion efficiency and easy fabrication in minimized sensors and transducers. To improve the output capability of energy harvesters, properties of piezoelectric materials is an influential factor, but the potential of the material is less likely to be fully exploited without an optimized configuration. In this paper, an optimization strategy for PVDF-based cantilever-type energy harvesters is proposed to achieve the highest output power density with the given frequency and acceleration of the vibration source. It is shown that the maximum power output density only depends on the maximum allowable stress of the beam and the working frequency of the device, and these two factors can be obtained by adjusting the geometry of piezoelectric layers. The strategy is validated by coupled finite-element-circuit simulation and a practical device. The fabricated device within a volume of 13.1 mm(3) shows an output power of 112.8 μW which is comparable to that of the best-performing piezoceramic-based energy harvesters within the similar volume reported so far.

  16. Utility-based optimization of phase II/III programs.

    Science.gov (United States)

    Kirchner, Marietta; Kieser, Meinhard; Götte, Heiko; Schüler, Armin

    2016-01-30

    Phase II and phase III trials play a crucial role in drug development programs. They are costly and time consuming and, because of high failure rates in late development stages, at the same time risky investments. Commonly, sample size calculation of phase III is based on the treatment effect observed in phase II. Therefore, planning of phases II and III can be linked. The performance of the phase II/III program crucially depends on the allocation of the resources to phases II and III by appropriate choice of the sample size and the rule applied to decide whether to stop the program after phase II or to proceed. We present methods for a program-wise phase II/III planning that aim at determining optimal phase II sample sizes and go/no-go decisions in a time-to-event setting. Optimization is based on a utility function that takes into account (fixed and variable) costs of the drug development program and potential gains after successful launch. The proposed methods are illustrated by application to a variety of scenarios typically met in oncology drug development.

  17. A Localization Method for Multistatic SAR Based on Convex Optimization.

    Directory of Open Access Journals (Sweden)

    Xuqi Zhong

    Full Text Available In traditional localization methods for Synthetic Aperture Radar (SAR, the bistatic range sum (BRS estimation and Doppler centroid estimation (DCE are needed for the calculation of target localization. However, the DCE error greatly influences the localization accuracy. In this paper, a localization method for multistatic SAR based on convex optimization without DCE is investigated and the influence of BRS estimation error on localization accuracy is analysed. Firstly, by using the information of each transmitter and receiver (T/R pair and the target in SAR image, the model functions of T/R pairs are constructed. Each model function's maximum is on the circumference of the ellipse which is the iso-range for its model function's T/R pair. Secondly, the target function whose maximum is located at the position of the target is obtained by adding all model functions. Thirdly, the target function is optimized based on gradient descent method to obtain the position of the target. During the iteration process, principal component analysis is implemented to guarantee the accuracy of the method and improve the computational efficiency. The proposed method only utilizes BRSs of a target in several focused images from multistatic SAR. Therefore, compared with traditional localization methods for SAR, the proposed method greatly improves the localization accuracy. The effectivity of the localization approach is validated by simulation experiment.

  18. A Localization Method for Multistatic SAR Based on Convex Optimization.

    Science.gov (United States)

    Zhong, Xuqi; Wu, Junjie; Yang, Jianyu; Sun, Zhichao; Huang, Yuling; Li, Zhongyu

    2015-01-01

    In traditional localization methods for Synthetic Aperture Radar (SAR), the bistatic range sum (BRS) estimation and Doppler centroid estimation (DCE) are needed for the calculation of target localization. However, the DCE error greatly influences the localization accuracy. In this paper, a localization method for multistatic SAR based on convex optimization without DCE is investigated and the influence of BRS estimation error on localization accuracy is analysed. Firstly, by using the information of each transmitter and receiver (T/R) pair and the target in SAR image, the model functions of T/R pairs are constructed. Each model function's maximum is on the circumference of the ellipse which is the iso-range for its model function's T/R pair. Secondly, the target function whose maximum is located at the position of the target is obtained by adding all model functions. Thirdly, the target function is optimized based on gradient descent method to obtain the position of the target. During the iteration process, principal component analysis is implemented to guarantee the accuracy of the method and improve the computational efficiency. The proposed method only utilizes BRSs of a target in several focused images from multistatic SAR. Therefore, compared with traditional localization methods for SAR, the proposed method greatly improves the localization accuracy. The effectivity of the localization approach is validated by simulation experiment.

  19. Optimization-based multiple-point geostatistics: A sparse way

    Science.gov (United States)

    Kalantari, Sadegh; Abdollahifard, Mohammad Javad

    2016-10-01

    In multiple-point simulation the image should be synthesized consistent with the given training image and hard conditioning data. Existing sequential simulation methods usually lead to error accumulation which is hardly manageable in future steps. Optimization-based methods are capable of handling inconsistencies by iteratively refining the simulation grid. In this paper, the multiple-point stochastic simulation problem is formulated in an optimization-based framework using a sparse model. Sparse model allows each patch to be constructed as a superposition of a few atoms of a dictionary formed using training patterns, leading to a significant increase in the variability of the patches. To control the creativity of the model, a local histogram matching method is proposed. Furthermore, effective solutions are proposed for different issues arisen in multiple-point simulation. In order to handle hard conditioning data a weighted matching pursuit method is developed in this paper. Moreover, a simple and efficient thresholding method is developed which allows working with categorical variables. The experiments show that the proposed method produces acceptable realizations in terms of pattern reproduction, increases the variability of the realizations, and properly handles numerous conditioning data.

  20. Optimal pattern distributions in Rete-based production systems

    Science.gov (United States)

    Scott, Stephen L.

    1994-01-01

    Since its introduction into the AI community in the early 1980's, the Rete algorithm has been widely used. This algorithm has formed the basis for many AI tools, including NASA's CLIPS. One drawback of Rete-based implementation, however, is that the network structures used internally by the Rete algorithm make it sensitive to the arrangement of individual patterns within rules. Thus while rules may be more or less arbitrarily placed within source files, the distribution of individual patterns within these rules can significantly affect the overall system performance. Some heuristics have been proposed to optimize pattern placement, however, these suggestions can be conflicting. This paper describes a systematic effort to measure the effect of pattern distribution on production system performance. An overview of the Rete algorithm is presented to provide context. A description of the methods used to explore the pattern ordering problem area are presented, using internal production system metrics such as the number of partial matches, and coarse-grained operating system data such as memory usage and time. The results of this study should be of interest to those developing and optimizing software for Rete-based production systems.

  1. Multi-objective reliability-based optimization with stochastic metamodels.

    Science.gov (United States)

    Coelho, Rajan Filomeno; Bouillard, Philippe

    2011-01-01

    This paper addresses continuous optimization problems with multiple objectives and parameter uncertainty defined by probability distributions. First, a reliability-based formulation is proposed, defining the nondeterministic Pareto set as the minimal solutions such that user-defined probabilities of nondominance and constraint satisfaction are guaranteed. The formulation can be incorporated with minor modifications in a multiobjective evolutionary algorithm (here: the nondominated sorting genetic algorithm-II). Then, in the perspective of applying the method to large-scale structural engineering problems--for which the computational effort devoted to the optimization algorithm itself is negligible in comparison with the simulation--the second part of the study is concerned with the need to reduce the number of function evaluations while avoiding modification of the simulation code. Therefore, nonintrusive stochastic metamodels are developed in two steps. First, for a given sampling of the deterministic variables, a preliminary decomposition of the random responses (objectives and constraints) is performed through polynomial chaos expansion (PCE), allowing a representation of the responses by a limited set of coefficients. Then, a metamodel is carried out by kriging interpolation of the PCE coefficients with respect to the deterministic variables. The method has been tested successfully on seven analytical test cases and on the 10-bar truss benchmark, demonstrating the potential of the proposed approach to provide reliability-based Pareto solutions at a reasonable computational cost.

  2. CFD-Based Design Optimization Tool Developed for Subsonic Inlet

    Science.gov (United States)

    1995-01-01

    The traditional approach to the design of engine inlets for commercial transport aircraft is a tedious process that ends with a less-than-optimum design. With the advent of high-speed computers and the availability of more accurate and reliable computational fluid dynamics (CFD) solvers, numerical optimization processes can effectively be used to design an aerodynamic inlet lip that enhances engine performance. The designers' experience at Boeing Corporation showed that for a peak Mach number on the inlet surface beyond some upper limit, the performance of the engine degrades excessively. Thus, our objective was to optimize efficiency (minimize the peak Mach number) at maximum cruise without compromising performance at other operating conditions. Using a CFD code NPARC, the NASA Lewis Research Center, in collaboration with Boeing, developed an integrated procedure at Lewis to find the optimum shape of a subsonic inlet lip and a numerical optimization code, ADS. We used a GRAPE-based three-dimensional grid generator to help automate the optimization procedure. The inlet lip shape at the crown and the keel was described as a superellipse, and the superellipse exponents and radii ratios were considered as design variables. Three operating conditions: cruise, takeoff, and rolling takeoff, were considered in this study. Three-dimensional Euler computations were carried out to obtain the flow field. At the initial design, the peak Mach numbers for maximum cruise, takeoff, and rolling takeoff conditions were 0.88, 1.772, and 1.61, respectively. The acceptable upper limits on the takeoff and rolling takeoff Mach numbers were 1.55 and 1.45. Since the initial design provided by Boeing was found to be optimum with respect to the maximum cruise condition, the sum of the peak Mach numbers at takeoff and rolling takeoff were minimized in the current study while the maximum cruise Mach number was constrained to be close to that at the existing design. With this objective, the

  3. Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    Muhammad Imran

    2014-01-01

    Full Text Available One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF coupled with support vector machine (SVM has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO. The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.

  4. 75 FR 42380 - Orders Finding That the SP-15 Financial Day-Ahead LMP Peak Contract and SP-15 Financial Day-Ahead...

    Science.gov (United States)

    2010-07-21

    ... the contract is a SPDC. The issuance of an affirmative order signals the effectiveness of the...''), Electric Power Supply Association (``EPSA''), Financial Institutions Energy Group (``FIEG''), Working Group...''), Edison Electric Institute (``EEI''), Western Power Trading Forum (``WPTF'') and Public Utility...

  5. Market-Based and System-Wide Fuel Cycle Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Wilson, Paul Philip Hood [Univ. of Wisconsin, Madison, WI (United States); Scopatz, Anthony [Univ. of South Carolina, Columbia, SC (United States); Gidden, Matthew [Univ. of Wisconsin, Madison, WI (United States); Carlsen, Robert [Univ. of Wisconsin, Madison, WI (United States); Mouginot, Baptiste [Univ. of Wisconsin, Madison, WI (United States); Flanagan, Robert [Univ. of South Carolina, Columbia, SC (United States)

    2017-06-13

    This work introduces automated optimization into fuel cycle simulations in the Cyclus platform. This includes system-level optimizations, seeking a deployment plan that optimizes the performance over the entire transition, and market-level optimization, seeking an optimal set of material trades at each time step. These concepts were introduced in a way that preserves the flexibility of the Cyclus fuel cycle framework, one of its most important design principles.

  6. Design Process Optimization Based on Design Process Gene Mapping

    Institute of Scientific and Technical Information of China (English)

    LI Bo; TONG Shu-rong

    2011-01-01

    The idea of genetic engineering is introduced into the area of product design to improve the design efficiency. A method towards design process optimization based on the design process gene is proposed through analyzing the correlation between the design process gene and characteristics of the design process. The concept of the design process gene is analyzed and categorized into five categories that are the task specification gene, the concept design gene, the overall design gene, the detailed design gene and the processing design gene in the light of five design phases. The elements and their interactions involved in each kind of design process gene signprocess gene mapping is drawn with its structure disclosed based on its function that process gene.

  7. Vision-based coaching: Optimizing resources for leader development

    Directory of Open Access Journals (Sweden)

    Angela M. Passarelli

    2015-04-01

    Full Text Available Leaders develop in the direction of their dreams, not in the direction of their deficits. Yet many coaching interactions intended to promote a leader’s development fail to leverage the developmental benefits of the individual’s personal vision. Drawing on Intentional Change Theory, this article postulates that coaching interactions that emphasize a leader’s personal vision (future aspirations and core identity evoke a psychophysiological state characterized by positive emotions, cognitive openness, and optimal neurobiological functioning for complex goal pursuit. Vision-based coaching, via this psychophysiological state, generates a host of relational and motivational resources critical to the developmental process. These resources include: formation of a positive coaching relationship, expansion of the leader’s identity, increased vitality, activation of learning goals, and a promotion-orientation. Organizational outcomes as well as limitations to vision-based coaching are discussed.

  8. Vision-based coaching: optimizing resources for leader development.

    Science.gov (United States)

    Passarelli, Angela M

    2015-01-01

    Leaders develop in the direction of their dreams, not in the direction of their deficits. Yet many coaching interactions intended to promote a leader's development fail to leverage the benefits of the individual's personal vision. Drawing on intentional change theory, this article postulates that coaching interactions that emphasize a leader's personal vision (future aspirations and core identity) evoke a psychophysiological state characterized by positive emotions, cognitive openness, and optimal neurobiological functioning for complex goal pursuit. Vision-based coaching, via this psychophysiological state, generates a host of relational and motivational resources critical to the developmental process. These resources include: formation of a positive coaching relationship, expansion of the leader's identity, increased vitality, activation of learning goals, and a promotion-orientation. Organizational outcomes as well as limitations to vision-based coaching are discussed.

  9. Optimization of an Image-Based Talking Head System

    Directory of Open Access Journals (Sweden)

    Kang Liu

    2009-01-01

    Full Text Available This paper presents an image-based talking head system, which includes two parts: analysis and synthesis. The audiovisual analysis part creates a face model of a recorded human subject, which is composed of a personalized 3D mask as well as a large database of mouth images and their related information. The synthesis part generates natural looking facial animations from phonetic transcripts of text. A critical issue of the synthesis is the unit selection which selects and concatenates these appropriate mouth images from the database such that they match the spoken words of the talking head. Selection is based on lip synchronization and the similarity of consecutive images. The unit selection is refined in this paper, and Pareto optimization is used to train the unit selection. Experimental results of subjective tests show that most people cannot distinguish our facial animations from real videos.

  10. Comparison of risk-based optimization models for reservoir management

    Energy Technology Data Exchange (ETDEWEB)

    Mahootchi, M. [Amirkabir Univ. of Technology, Tehran (Iran, Islamic Republic of). Dept. of Industrial Engineering; Ponnambalam, K.; Tizhoosh, H.R. [Waterloo Univ., ON (Canada). Dept. of Systems Design Engineering

    2010-01-15

    The stochastic nature of input variables in water resource management problems must be carefully considered during decision-making processes. This paper used a single reservoir optimization problem in which 2-stage stochastic programming (TSP) and Fletcher-Ponnambalam (FP) were used to generate open-loop policies where inflows and water prices were uncertain. A simulation was performed to measure the performance of FP and TSP techniques in embedding risk and producing comparable policies. A simulation-based Q-learning algorithm based on reinforcement learning (RL) was used to determine the squared action-value function or each action-state pair produced by the algorithm. The methods were used to produce the trade-off curve between expected benefits and standard deviations of benefits. The study showed that the FP method does not require simulation, while the TSP and Q-learning methods both required simulations. 39 refs., 9 tabs., 4 figs.

  11. Cantilever-Based Microwave Biosensors: Analysis, Designs and Optimizations

    DEFF Research Database (Denmark)

    Jiang, Chenhui; Johansen, Tom Keinicke; Jónasson, Sævar Þór;

    2011-01-01

    This paper presents a novel microwave readout scheme for measuring deflection of cantilevers in nanometer range. The cantilever deflection can be sensed by the variation of transmission levels or resonant frequencies of microwave signals. The sensitivity of the cantilever biosensor based on LC...... resonators is at first theoretically analyzed. A LC resonator based biosensor with beams is designed and optimized by using 3D electromagnetic (EM) simulations, where the beam is a typical variation of cantilevers. The sensitivity of the lossless biosensor is predicted as 4.6MHz/nm. The 3-dB bandwidths...... of the resonances are narrowed for improving the resolution of distinguishing resonances by reducing conductive loss of electrodes. The lossy biosensor can achieve the highest sensitivity as 5.6 MHz/nm and narrowest 3-dB bandwidth as 5 GHz....

  12. Optimize Etching Based Single Mode Fiber Optic Temperature Sensor

    Directory of Open Access Journals (Sweden)

    Ajay Kumar

    2014-02-01

    Full Text Available This paper presents a description of etching process for fabrication single mode optical fiber sensors. The process of fabrication demonstrates an optimized etching based method to fabricate single mode fiber (SMF optic sensors in specified constant time and temperature. We propose a single mode optical fiber based temperature sensor, where the temperature sensing region is obtained by etching its cladding diameter over small length to a critical value. It is observed that the light transmission through etched fiber at 1550 nm wavelength optical source becomes highly temperature sensitive, compared to the temperature insensitive behavior observed in un-etched fiber for the range on 30ºC to 100ºC at 1550 nm. The sensor response under temperature cycling is repeatable and, proposed to be useful for low frequency analogue signal transmission over optical fiber by means of inline thermal modulation approach.

  13. Grid Computing based on Game Optimization Theory for Networks Scheduling

    Directory of Open Access Journals (Sweden)

    Peng-fei Zhang

    2014-05-01

    Full Text Available The resource sharing mechanism is introduced into grid computing algorithm so as to solve complex computational tasks in heterogeneous network-computing problem. However, in the Grid environment, it is required for the available resource from network to reasonably schedule and coordinate, which can get a good workflow and an appropriate network performance and network response time. In order to improve the performance of resource allocation and task scheduling in grid computing method, a game model based on non-cooperation game is proposed. Setting the time and cost of user’s resource allocation can increase the performance of networks, and incentive resource of networks uses an optimization scheduling algorithm, which minimizes the time and cost of resource scheduling. Simulation experiment results show the feasibility and suitability of model. In addition, we can see from the experiment result that model-based genetic algorithm is the best resource scheduling algorithm

  14. Model-based dynamic control and optimization of gas networks

    Energy Technology Data Exchange (ETDEWEB)

    Hofsten, Kai

    2001-07-01

    by a structured sequential quadratic programming algorithm of Newton type. Each open loop problem is specified using a nonlinear prediction model. For each iteration of the quadratic programming procedure, a linear time variant prediction model is formulated. The suggested controller also handles time varying source capacity. Potential problems such as infeasibility and the security of the supply when facing a change in the status of the infrastructure of the transmission system under a transient customer load are treated. Comments on the infeasibility due to errors such as load forecast error, model error and state estimation error are also discussed. A simplified nonlinear model called the creep flow model is used to describe the fluid dynamics inside a natural gas transmission line. Different assumptions and reformulations of this model yield the different control, simulation and optimization models used in this thesis. The control of a single gas transmission line is investigated using linear model predictive control based on instant linearization of the nonlinear model. Model predictive control using a bi quadratic optimization model formulated from the creep flow model is also investigated. A distributed parameter control model of the gas dynamics for a transmission line is formulated. An analytic solution of this model is given with both Neuman boundary conditions and distributed supplies and loads. A transfer function model is developed expressing the dynamics between the defined output and the control and disturbance inputs of the transmission line. Based on the qualitative behaviour observed from the step responses of the solutions of the distributed parameter model formulated in this thesis, simplified transfer function models were developed. These control models expresses the dynamics of a natural gas transmission line with Neuman boundary control and load. Further, these models were used to design a control law, which is a combination of a Smith

  15. Genetics algorithm optimization of DWT-DCT based image Watermarking

    Science.gov (United States)

    Budiman, Gelar; Novamizanti, Ledya; Iwut, Iwan

    2017-01-01

    Data hiding in an image content is mandatory for setting the ownership of the image. Two dimensions discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed as transform method in this paper. First, the host image in RGB color space is converted to selected color space. We also can select the layer where the watermark is embedded. Next, 2D-DWT transforms the selected layer obtaining 4 subband. We select only one subband. And then block-based 2D-DCT transforms the selected subband. Binary-based watermark is embedded on the AC coefficients of each block after zigzag movement and range based pixel selection. Delta parameter replacing pixels in each range represents embedded bit. +Delta represents bit “1” and –delta represents bit “0”. Several parameters to be optimized by Genetics Algorithm (GA) are selected color space, layer, selected subband of DWT decomposition, block size, embedding range, and delta. The result of simulation performs that GA is able to determine the exact parameters obtaining optimum imperceptibility and robustness, in any watermarked image condition, either it is not attacked or attacked. DWT process in DCT based image watermarking optimized by GA has improved the performance of image watermarking. By five attacks: JPEG 50%, resize 50%, histogram equalization, salt-pepper and additive noise with variance 0.01, robustness in the proposed method has reached perfect watermark quality with BER=0. And the watermarked image quality by PSNR parameter is also increased about 5 dB than the watermarked image quality from previous method.

  16. Nonlinear model predictive control based on collective neurodynamic optimization.

    Science.gov (United States)

    Yan, Zheng; Wang, Jun

    2015-04-01

    In general, nonlinear model predictive control (NMPC) entails solving a sequential global optimization problem with a nonconvex cost function or constraints. This paper presents a novel collective neurodynamic optimization approach to NMPC without linearization. Utilizing a group of recurrent neural networks (RNNs), the proposed collective neurodynamic optimization approach searches for optimal solutions to global optimization problems by emulating brainstorming. Each RNN is guaranteed to converge to a candidate solution by performing constrained local search. By exchanging information and iteratively improving the starting and restarting points of each RNN using the information of local and global best known solutions in a framework of particle swarm optimization, the group of RNNs is able to reach global optimal solutions to global optimization problems. The essence of the proposed collective neurodynamic optimization approach lies in the integration of capabilities of global search and precise local search. The simulation results of many cases are discussed to substantiate the effectiveness and the characteristics of the proposed approach.

  17. A Novel Global Path Planning Method for Mobile Robots Based on Teaching-Learning-Based Optimization

    Directory of Open Access Journals (Sweden)

    Zongsheng Wu

    2016-07-01

    Full Text Available The Teaching-Learning-Based Optimization (TLBO algorithm has been proposed in recent years. It is a new swarm intelligence optimization algorithm simulating the teaching-learning phenomenon of a classroom. In this paper, a novel global path planning method for mobile robots is presented, which is based on an improved TLBO algorithm called Nonlinear Inertia Weighted Teaching-Learning-Based Optimization (NIWTLBO algorithm in our previous work. Firstly, the NIWTLBO algorithm is introduced. Then, a new map model of the path between start-point and goal-point is built by coordinate system transformation. Lastly, utilizing the NIWTLBO algorithm, the objective function of the path is optimized; thus, a global optimal path is obtained. The simulation experiment results show that the proposed method has a faster convergence rate and higher accuracy in searching for the path than the basic TLBO and some other algorithms as well, and it can effectively solve the optimization problem for mobile robot global path planning.

  18. Parameter identifiability-based optimal observation remedy for biological networks.

    Science.gov (United States)

    Wang, Yulin; Miao, Hongyu

    2017-05-04

    To systematically understand the interactions between numerous biological components, a variety of biological networks on different levels and scales have been constructed and made available in public databases or knowledge repositories. Graphical models such as structural equation models have long been used to describe biological networks for various quantitative analysis tasks, especially key biological parameter estimation. However, limited by resources or technical capacities, partial observation is a common problem in experimental observations of biological networks, and it thus becomes an important problem how to select unobserved nodes for additional measurements such that all unknown model parameters become identifiable. To the best knowledge of our authors, a solution to this problem does not exist until this study. The identifiability-based observation problem for biological networks is mathematically formulated for the first time based on linear recursive structural equation models, and then a dynamic programming strategy is developed to obtain the optimal observation strategies. The efficiency of the dynamic programming algorithm is achieved by avoiding both symbolic computation and matrix operations as used in other studies. We also provided necessary theoretical justifications to the proposed method. Finally, we verified the algorithm using synthetic network structures and illustrated the application of the proposed method in practice using a real biological network related to influenza A virus infection. The proposed approach is the first solution to the structural identifiability-based optimal observation remedy problem. It is applicable to an arbitrary directed acyclic biological network (recursive SEMs) without bidirectional edges, and it is a computerizable method. Observation remedy is an important issue in experiment design for biological networks, and we believe that this study provides a solid basis for dealing with more challenging design

  19. Optimization-Based Approaches to Control of Probabilistic Boolean Networks

    Directory of Open Access Journals (Sweden)

    Koichi Kobayashi

    2017-02-01

    Full Text Available Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs, which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.

  20. Topological Optimization of Continuum Structure based on ANSYS

    Directory of Open Access Journals (Sweden)

    Li Xue-ping

    2017-01-01

    Full Text Available Topology optimization is at the phase of structural concept design and the result of it is foundation for succeeding design, therefore, structural topology optimization is more important to engineering design. in this thesis, in order to seek the optimal structure shape of the winch’s mounting bracket of ROV simulator, topology optimization design of it by finite element analysis software ANSYS was carried out. the results show that the topology optimization method is an effective optimization method and indicate that the method is correct and effective, it has a certain engineering application prospect.

  1. Tree-Based Visualization and Optimization for Image Collection.

    Science.gov (United States)

    Han, Xintong; Zhang, Chongyang; Lin, Weiyao; Xu, Mingliang; Sheng, Bin; Mei, Tao

    2016-06-01

    The visualization of an image collection is the process of displaying a collection of images on a screen under some specific layout requirements. This paper focuses on an important problem that is not well addressed by the previous methods: visualizing image collections into arbitrary layout shapes while arranging images according to user-defined semantic or visual correlations (e.g., color or object category). To this end, we first propose a property-based tree construction scheme to organize images of a collection into a tree structure according to user-defined properties. In this way, images can be adaptively placed with the desired semantic or visual correlations in the final visualization layout. Then, we design a two-step visualization optimization scheme to further optimize image layouts. As a result, multiple layout effects including layout shape and image overlap ratio can be effectively controlled to guarantee a satisfactory visualization. Finally, we also propose a tree-transfer scheme such that visualization layouts can be adaptively changed when users select different "images of interest." We demonstrate the effectiveness of our proposed approach through the comparisons with state-of-the-art visualization techniques.

  2. Source mask optimization study based on latest Nikon immersion scanner

    Science.gov (United States)

    Zhu, Jun; Wei, Fang; Chen, Lijun; Zhang, Chenming; Zhang, Wei; Nishinaga, Hisashi; El-Sewefy, Omar; Gao, Gen-Sheng; Lafferty, Neal; Meiring, Jason; Zhang, Recoo; Zhu, Cynthia

    2016-03-01

    The 2x nm logic foundry node has many challenges since critical levels are pushed close to the limits of low k1 ArF water immersion lithography. For these levels, improvements in lithographic performance can translate to decreased rework and increased yield. Source Mask Optimization (SMO) is one such route to realize these image fidelity improvements. During SMO, critical layout constructs are intensively optimized in both the mask and source domain, resulting in a solution for maximum lithographic entitlement. From the hardware side, advances in source technology have enabled free-form illumination. The approach allows highly customized illumination, enabling the practical application of SMO sources. The customized illumination sources can be adjusted for maximum versatility. In this paper, we present a study on a critical layer of an advanced foundry logic node using the latest ILT based SMO software, paired with state-of-the-art scanner hardware and intelligent illuminator. Performance of the layer's existing POR source is compared with the ideal SMO result and the installed source as realized on the intelligent illuminator of an NSR-S630D scanner. Both simulation and on-silicon measurements are used to confirm that the performance of the studied layer meets established specifications.

  3. CFD-Based Design Optimization for Single Element Rocket Injector

    Science.gov (United States)

    Vaidyanathan, Rajkumar; Tucker, Kevin; Papila, Nilay; Shyy, Wei

    2003-01-01

    To develop future Reusable Launch Vehicle concepts, we have conducted design optimization for a single element rocket injector, with overall goals of improving reliability and performance while reducing cost. Computational solutions based on the Navier-Stokes equations, finite rate chemistry, and the k-E turbulence closure are generated with design of experiment techniques, and the response surface method is employed as the optimization tool. The design considerations are guided by four design objectives motivated by the consideration in both performance and life, namely, the maximum temperature on the oxidizer post tip, the maximum temperature on the injector face, the adiabatic wall temperature, and the length of the combustion zone. Four design variables are selected, namely, H2 flow angle, H2 and O2 flow areas with fixed flow rates, and O2 post tip thickness. In addition to establishing optimum designs by varying emphasis on the individual objectives, better insight into the interplay between design variables and their impact on the design objectives is gained. The investigation indicates that improvement in performance or life comes at the cost of the other. Best compromise is obtained when improvements in both performance and life are given equal importance.

  4. Vanpool trip planning based on evolutionary multiple objective optimization

    Science.gov (United States)

    Zhao, Ming; Yang, Disheng; Feng, Shibing; Liu, Hengchang

    2017-08-01

    Carpool and vanpool draw a lot of researchers’ attention, which is the emphasis of this paper. A concrete vanpool operation definition is given, based on the given definition, this paper tackles vanpool operation optimization using user experience decline index(UEDI). This paper is focused on making each user having identical UEDI and the system having minimum sum of all users’ UEDI. Three contributions are made, the first contribution is a vanpool operation scheme diagram, each component of the scheme is explained in detail. The second contribution is getting all customer’s UEDI as a set, standard deviation and sum of all users’ UEDI set are used as objectives in multiple objective optimization to decide trip start address, trip start time and trip destination address. The third contribution is a trip planning algorithm, which tries to minimize the sum of all users’ UEDI. Geographical distribution of the charging stations and utilization rate of the charging stations are considered in the trip planning process.

  5. [Optimal allocation of irrigation water resources based on systematical strategy].

    Science.gov (United States)

    Cheng, Shuai; Zhang, Shu-qing

    2015-01-01

    With the development of the society and economy, as well as the rapid increase of population, more and more water is needed by human, which intensified the shortage of water resources. The scarcity of water resources and growing competition of water in different water use sectors reduce water availability for irrigation, so it is significant to plan and manage irrigation water resources scientifically and reasonably for improving water use efficiency (WUE) and ensuring food security. Many investigations indicate that WUE can be increased by optimization of water use. However, present studies focused primarily on a particular aspect or scale, which lack systematic analysis on the problem of irrigation water allocation. By summarizing previous related studies, especially those based on intelligent algorithms, this article proposed a multi-level, multi-scale framework for allocating irrigation water, and illustrated the basic theory of each component of the framework. Systematical strategy of optimal irrigation water allocation can not only control the total volume of irrigation water on the time scale, but also reduce water loss on the spatial scale. It could provide scientific basis and technical support for improving the irrigation water management level and ensuring the food security.

  6. Direct trajectory optimization based on a mapped Chebyshev pseudospectral method

    Institute of Scientific and Technical Information of China (English)

    Guo Xiao; Zhu Ming

    2013-01-01

    In view of generating optimal trajectories of Bolza problems,standard Chebyshev pseudospectral (PS) method makes the points' accumulation near the extremities and rarefaction of nodes close to the center of interval,which causes an ill-condition of differentiation matrix and an oscillation of the optimal solution.For improvement upon the difficulties,a mapped Chebyshev pseudospectral method is proposed.A conformal map is applied to Chebyshev points to move the points closer to equidistant nodes.Condition number and spectral radius of differentiation matrices from both methods are presented to show the improvement.Furthermore,the modification keeps the Chebyshev pseudospectral method's advantage,the spectral convergence rate.Based on three numerical examples,a comparison of the execution time,convergence and accuracy is presented among the standard Chebyshev pseudospectral method,other collocation methods and the proposed one.In one example,the error of results from mapped Chebyshev pseudospectral method is reduced to 5% of that from standard Chebyshev pseudospectral method.

  7. DOE Based Robust Optimization Considering Tolerance Bands of Design Parameters

    Science.gov (United States)

    Lee, Jongsoo; Ahn, Byongchul

    The paper describes a robust optimization method to account for the tolerance of design variable and the variation in problem parameter. The proposed post-optimization effort is initiated from the deterministic optimum as a baseline. The successive process to find search directions and step sizes toward the robust optimum is conducted by determining the worst design that has the highest level in constraint violation. During the selection of the worst design, an orthogonal array table in the context of design of experiemtns (DOE) is used to reduce the constraint function evaluations especially for higher dimensionality problem. The analysis of means (ANOM) is adopted in a case where the variation in problem parameter is considered. The measurement criterion to select the worst design is based on the degree of cumulative constraint violation. A mathematical function problem is first conducted to examine the tolerance of design variable. A cantilever beam problem described by four design variables and a bracket problem with seven design variables are subsequently explored by considering both tolerance of design variable and variation in problem parameter.

  8. Beam Pattern Synthesis Based on Hybrid Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    YU Yan-li; WANG Ying-min; LI Lei

    2010-01-01

    As conventional methods for beam pattern synthesis can not always obtain the desired optimum pattern for the arbitrary underwater acoustic sensor arrays, a hybrid numerical synthesis method based on adaptive principle and genetic algorithm was presented in this paper. First, based on the adaptive theory, a given array was supposed as an adaptive array and its sidelobes were reduced by assigning a number of interference signals in the sidelobe region. An initial beam pattern was obtained after several iterations and adjustments of the interference intensity, and based on its parameters, a desired pattern was created. Then, an objective function based on the difference between the designed and desired patterns can be constructed. The pattern can be optimized by using the genetic algorithm to minimize the objective function. A design example for a double-circular array demonstrates the effectiveness of this method. Compared with the approaches existing before, the proposed method can reduce the sidelobe effectively and achieve less synthesis magnitude error in the mainlobe.The method can search for optimum attainable pattern for the specific elements if the desired pattern can not be found.

  9. Optimal Control of Switched Systems based on Bezier Control Points

    OpenAIRE

    FatemeGhomanjani; Mohammad HadiFarahi

    2012-01-01

    This paper presents a new approach for solving optimal control problems for switched systems. We focus on problems in which a pre-specified sequence of active subsystems is given. For such problems, we need to seek both the optimal switching instants and the optimal continuous inputs. A Bezier control points method is applied for solving an optimal control problem which is supervised by a switched dynamic system. Two steps of approximation exist here. First, the time interval is divided into ...

  10. Multilevel Thresholding Segmentation Based on Harmony Search Optimization

    Directory of Open Access Journals (Sweden)

    Diego Oliva

    2013-01-01

    Full Text Available In this paper, a multilevel thresholding (MT algorithm based on the harmony search algorithm (HSA is introduced. HSA is an evolutionary method which is inspired in musicians improvising new harmonies while playing. Different to other evolutionary algorithms, HSA exhibits interesting search capabilities still keeping a low computational overhead. The proposed algorithm encodes random samples from a feasible search space inside the image histogram as candidate solutions, whereas their quality is evaluated considering the objective functions that are employed by the Otsu’s or Kapur’s methods. Guided by these objective values, the set of candidate solutions are evolved through the HSA operators until an optimal solution is found. Experimental results demonstrate the high performance of the proposed method for the segmentation of digital images.

  11. Density-based penalty parameter optimization on C-SVM.

    Science.gov (United States)

    Liu, Yun; Lian, Jie; Bartolacci, Michael R; Zeng, Qing-An

    2014-01-01

    The support vector machine (SVM) is one of the most widely used approaches for data classification and regression. SVM achieves the largest distance between the positive and negative support vectors, which neglects the remote instances away from the SVM interface. In order to avoid a position change of the SVM interface as the result of an error system outlier, C-SVM was implemented to decrease the influences of the system's outliers. Traditional C-SVM holds a uniform parameter C for both positive and negative instances; however, according to the different number proportions and the data distribution, positive and negative instances should be set with different weights for the penalty parameter of the error terms. Therefore, in this paper, we propose density-based penalty parameter optimization of C-SVM. The experiential results indicated that our proposed algorithm has outstanding performance with respect to both precision and recall.

  12. OPTIMIZATION OF LOCATION BASED QUERIES USING SPATIAL INDEXING

    Directory of Open Access Journals (Sweden)

    S. Geetha

    2014-04-01

    Full Text Available The recent development in the technology leads to the introduction of various mobile terminals and there is a demand that the client requires effective location based services. The valid regions expand and also query retrieval time increases which lead to poor performance of query processing. The spatial indexing techniques are one of the most effective optimization methods to improve the quality of services. In existing system NN queries and window queries are used. In that R-tree and grid indexing has been used for increasing the query efficiency. But the Grid-index technique support low memory and thus large databases cannot be handled effectively. In the proposed system we are using Ordered grid index and EVR-tree to minimize the query retrieval time and to decrease the depth of the search index. The Ordered grid index and EVR-tree to speed up the spatial query processing.

  13. OPTIMIZATION BASED ON LMPROVED REAL—CODED GENETIC ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    ShiYu; YuShenglin

    2002-01-01

    An improved real-coded genetic algorithm is pro-posed for global optimization of functionsl.The new algo-rithm is based om the judgement of the searching perfor-mance of basic real-coded genetic algorithm.The opera-tions of basic real-coded genetic algorithm are briefly dis-cussed and selected.A kind of chaos sequence is described in detail and added in the new algorithm ad a disturbance factor.The strategy of field partition is also used to im-prove the strcture of the new algorithm.Numerical ex-periment shows that the mew genetic algorithm can find the global optimum of complex funtions with satistaiting precision.

  14. Multimineral optimization processing method based on elemental capture spectroscopy logging

    Institute of Scientific and Technical Information of China (English)

    Feng Zhou; Li Xin-Tong; Wu Hong-Liang; Xia Shou-Ji; Liu Ying-Ming

    2014-01-01

    Calculating the mineral composition is a critical task in log interpretation. Elemental capture spectroscopy (ECS) log provides the weight percentages of twelve common elements, which lays the foundation for the accurate calculation of mineral compositions. Previous processing methods calculated the formation composition via the conversion relation between the formation chemistry and minerals. Thus, their applicability is limited and the method precision is relatively low. In this study, we present a multimineral optimization processing method based on the ECS log. We derived the ECS response equations for calculating the formation composition, then, determined the logging response values for the elements of common minerals using core data and theoretical calculations. Finally, a software module was developed. The results of the new method are consistent with core data and the mean absolute error is less than 10%.

  15. Perception-based transparency optimization for direct volume rendering.

    Science.gov (United States)

    Chan, Ming-Yuen; Wu, Yingcai; Mak, Wai-Ho; Chen, Wei; Qu, Huamin

    2009-01-01

    The semi-transparent nature of direct volume rendered images is useful to depict layered structures in a volume. However, obtaining a semi-transparent result with the layers clearly revealed is difficult and may involve tedious adjustment on opacity and other rendering parameters. Furthermore, the visual quality of layers also depends on various perceptual factors. In this paper, we propose an auto-correction method for enhancing the perceived quality of the semi-transparent layers in direct volume rendered images. We introduce a suite of new measures based on psychological principles to evaluate the perceptual quality of transparent structures in the rendered images. By optimizing rendering parameters within an adaptive and intuitive user interaction process, the quality of the images is enhanced such that specific user requirements can be met. Experimental results on various datasets demonstrate the effectiveness and robustness of our method.

  16. Reliability-Based Design Optimization Considering Variable Uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Woochul; Jang, Junyoung; Lee, Tae Hee [Hanyang University, Seoul (Korea, Republic of); Kim, Jungho; Na, Jongho; Lee, Changkun; Kim, Yongsuk [GM Korea, Incheon (Korea, Republic of)

    2014-06-15

    Although many reliability analysis and reliability-based design optimization (RBDO) methods have been developed to estimate system reliability, many studies assume the uncertainty of the design variable to be constant. In practice, because uncertainty varies with the design variable's value, this assumption results in inaccurate conclusions about the reliability of the optimum design. Therefore, uncertainty should be considered variable in RBDO. In this paper, we propose an RBDO method considering variable uncertainty. Variable uncertainty can modify uncertainty for each design point, resulting in accurate reliability estimation. Finally, a notable optimum design is obtained using the proposed method with variable uncertainty. A mathematical example and an engine cradle design are illustrated to verify the proposed method.

  17. Task scheduling based on ant colony optimization in cloud environment

    Science.gov (United States)

    Guo, Qiang

    2017-04-01

    In order to optimize the task scheduling strategy in cloud environment, we propose a cloud computing task scheduling algorithm based on ant colony algorithm. The main goal of this algorithm is to minimize the makespan and the total cost of the tasks, while making the system load more balanced. In this paper, we establish the objective function of the makespan and costs of the tasks, define the load balance function. Meanwhile, we also improve the initialization of the pheromone, the heuristic function and the pheromone update method in the ant colony algorithm. Then, some experiments were carried out on the Cloudsim platform, and the results were compared with algorithms of ACO and Min-Min. The results shows that the algorithm is more efficient than the other two algorithms in makespan, costs and system load balancing.

  18. Optimization-based Fluid Simulation on Unstructured Meshes

    DEFF Research Database (Denmark)

    Misztal, Marek Krzysztof; Bridson, Robert; Erleben, Kenny;

    We present a novel approach to fluid simulation, allowing us to take into account the surface energy in a pre- cise manner. This new approach combines a novel, topology-adaptive approach to deformable interface track- ing, called the deformable simplicial complexes method (DSC) with an optimization......-based, linear finite element method for solving the incompressible Euler equations. The deformable simplicial complexes track the surface of the fluid: the fluid-air interface is represented explicitly as a piecewise linear surface which is a subset of tetra- hedralization of the space, such that the interface...... can be also represented implicitly as a set of faces separating tetrahedra marked as inside from the ones marked as outside. This representation introduces insignificant and con- trollable numerical diffusion, allows robust topological adaptivity and provides both a volumetric finite element mesh...

  19. SVM-based glioma grading: Optimization by feature reduction analysis.

    Science.gov (United States)

    Zöllner, Frank G; Emblem, Kyrre E; Schad, Lothar R

    2012-09-01

    We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity=89%, specificity=84%) when reducing the feature vector from 101 (100-bins rCBV histogram+age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values (∼87%) while reducing the number of features by up to 98%.

  20. Optimization-based particle filter for state and parameter estimation

    Institute of Scientific and Technical Information of China (English)

    Li Fu; Qi Fei; Shi Guangming; Zhang Li

    2009-01-01

    In recent years, the theory of particle filter has been developed and widely used for state and parameter estimation in nonlinear/non-Gaussian systems. Choosing good importance density is a critical issue in particle filter design. In order to improve the approximation of posterior distribution, this paper provides an optimization-based algorithm (the steepest descent method) to generate the proposal distribution and then sample particles from the distribution. This algorithm is applied in 1-D case, and the simulation results show that the proposed particle filter performs better than the extended Kalman filter (EKF), the standard particle filter (PF), the extended Kalman particle filter (PF-EKF) and the unscented particle filter (UPF) both in efficiency and in estimation precision.

  1. Integrated Reliability-Based Optimal Design of Structures

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Thoft-Christensen, Palle

    1987-01-01

    the reliability decreases with time it is often necessary to design an inspection and repair programme. For example the reliability of offshore steel structures decreases with time due to corrosion and development of fatigue cracks. Until now most inspection and repair strategies are based on experience rather......In conventional optimal design of structural systems the weight or the initial cost of the structure is usually used as objective function. Further, the constraints require that the stresses and/or strains at some critical points have to be less than some given values. Finally, all variables...... and parameters are assumed to be deterministic quantities. In this paper a probabilistic formulation is used. Some of the quantities specifying the load and the strength of the structure are modelled as random variables, and the constraints specify that the reliability of the structure has to exceed some given...

  2. Regularized Regression and Density Estimation based on Optimal Transport

    KAUST Repository

    Burger, M.

    2012-03-11

    The aim of this paper is to investigate a novel nonparametric approach for estimating and smoothing density functions as well as probability densities from discrete samples based on a variational regularization method with the Wasserstein metric as a data fidelity. The approach allows a unified treatment of discrete and continuous probability measures and is hence attractive for various tasks. In particular, the variational model for special regularization functionals yields a natural method for estimating densities and for preserving edges in the case of total variation regularization. In order to compute solutions of the variational problems, a regularized optimal transport problem needs to be solved, for which we discuss several formulations and provide a detailed analysis. Moreover, we compute special self-similar solutions for standard regularization functionals and we discuss several computational approaches and results. © 2012 The Author(s).

  3. Oracle-based online robust optimization via online learning

    NARCIS (Netherlands)

    Ben-Tal, A.; Hazan, E.; Koren, T.; Shie, M.

    2015-01-01

    Robust optimization is a common optimization framework under uncertainty when problem parameters are unknown, but it is known that they belong to some given uncertainty set. In the robust optimization framework, a min-max problem is solved wherein a solution is evaluated according to its performance

  4. POSSIBILITY AND EVIDENCE-BASED RELIABILITY ANALYSIS AND DESIGN OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    Hong-Zhong Huang

    2013-01-01

    Full Text Available Engineering design under uncertainty has gained considerable attention in recent years. A great multitude of new design optimization methodologies and reliability analysis approaches are put forth with the aim of accommodating various uncertainties. Uncertainties in practical engineering applications are commonly classified into two categories, i.e., aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty arises because of unpredictable variation in the performance and processes of systems, it is irreducible even adding more data or knowledge. On the other hand, epistemic uncertainty stems from lack of knowledge of the system due to limited data, measurement limitations, or simplified approximations in modeling system behavior and it can be reduced by obtaining more data or knowledge. More specifically, aleatory uncertainty is naturally represented by a statistical distribution and its associated parameters can be characterized by sufficient data. If, however, the data is limited and can be quantified in a statistical sense, epistemic uncertainty can be considered as an alternative tool in such a situation. Of the several optional treatments for epistemic uncertainty, possibility theory and evidence theory have proved to be the most computationally efficient and stable for reliability analysis and engineering design optimization. This study first attempts to provide a better understanding of uncertainty in engineering design by giving a comprehensive overview of its classifications, theories and design considerations. Then a review is conducted of general topics such as the foundations and applications of possibility theory and evidence theory. This overview includes the most recent results from theoretical research, computational developments and performance improvement of possibility theory and evidence theory with an emphasis on revealing the capability and characteristics of quantifying uncertainty from different perspectives

  5. Optimization of conditions for gene delivery system based on PEI

    Directory of Open Access Journals (Sweden)

    Roya Cheraghi

    2017-01-01

    Full Text Available Objective(s: PEI based nanoparticle (NP due to dual capabilities of proton sponge and DNA binding is known as powerful tool for nucleic acid delivery to cells. However, serious cytotoxicity and complicated conditions, which govern NPs properties and its interactions with cells practically, hindered achievement to high transfection efficiency. Here, we have tried to optimize the properties of PEI/ firefly luciferase plasmid complexes and cellular condition to improve transfection efficiency. Materials and Methods: For this purpose, firefly luciferase, as a robust gene reporter, was complexed with PEI to prepare NPs with different size and charge. The physicochemical properties of nanoparticles were evaluated using agarose gel retardation and dynamic light scattering.  MCF7 and BT474 cells at different confluency were also transfected with prepared nanoparticles at various concentrations for short and long times. Results: The branched PEI can instantaneously bind to DNA and form cationic NPs. The results demonstrated the production of nanoparticles with size about 100-500 nm dependent on N/P ratio. Moreover, increase of nanoparticles concentration on the cell surface drastically improved the transfection rate, so at a concentration of 30 ng/ìl, the highest transfection efficiency was achieved. On the other side, at confluency between 40-60%, the maximum efficiency was obtained. The result demonstrated that N/P ratio of 12 could establish an optimized ratio between transfection efficiency and cytotoxicity of PEI/plasmid nanoparticles. The increase of NPs N/P ratio led to significant cytotoxicity. Conclusion: Obtained results verified the optimum conditions for PEI based gene delivery in different cell lines.

  6. Optimal selection of regularization parameter for l1-based image restoration based on SURE

    Science.gov (United States)

    Xue, Feng; Liu, Xin; Liu, Hongyan; Liu, Jiaqi

    2016-10-01

    To exploit the sparsity in transform domain (e.g. wavelets), the image deconvolution can be typically formulated as a l1-penalized minimization problem, which, however, generally requires proper selection of regularization parameter for desired reconstruction quality. The key contribution of this paper is to develop a novel data-driven scheme to optimize regularization parameter, such that the resultant restored image achieves minimum prediction error (p-error). First, we develop Stein's unbiased risk estimate (SURE), an unbiased estimate of p-error, for image degradation model. Then, we propose a recursive evaluation of SURE for the basic iterative shrinkage/thresholding (IST), which enables us to find the optimal value of regularization parameter by exhaustive search. The numerical experiments show that the proposed SURE-based optimization leads to nearly optimal deconvolution performance in terms of peak signal-to-noise ratio (PSNR).

  7. A controller based on Optimal Type-2 Fuzzy Logic: systematic design, optimization and real-time implementation.

    Science.gov (United States)

    Fayek, H M; Elamvazuthi, I; Perumal, N; Venkatesh, B

    2014-09-01

    A computationally-efficient systematic procedure to design an Optimal Type-2 Fuzzy Logic Controller (OT2FLC) is proposed. The main scheme is to optimize the gains of the controller using Particle Swarm Optimization (PSO), then optimize only two parameters per type-2 membership function using Genetic Algorithm (GA). The proposed OT2FLC was implemented in real-time to control the position of a DC servomotor, which is part of a robotic arm. The performance judgments were carried out based on the Integral Absolute Error (IAE), as well as the computational cost. Various type-2 defuzzification methods were investigated in real-time. A comparative analysis with an Optimal Type-1 Fuzzy Logic Controller (OT1FLC) and a PI controller, demonstrated OT2FLC׳s superiority; which is evident in handling uncertainty and imprecision induced in the system by means of noise and disturbances. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Topology optimization using bi-directional evolutionary structural optimization based on the element-free Galerkin method

    Science.gov (United States)

    Shobeiri, Vahid

    2016-03-01

    In this article, the bi-directional evolutionary structural optimization (BESO) method based on the element-free Galerkin (EFG) method is presented for topology optimization of continuum structures. The mathematical formulation of the topology optimization is developed considering the nodal strain energy as the design variable and the minimization of compliance as the objective function. The EFG method is used to derive the shape functions using the moving least squares approximation. The essential boundary conditions are enforced by the method of Lagrange multipliers. Several topology optimization problems are presented to show the effectiveness of the proposed method. Many issues related to topology optimization of continuum structures, such as chequerboard patterns and mesh dependency, are studied in the examples.

  9. Swarm Optimization-Based Magnetometer Calibration for Personal Handheld Devices

    Directory of Open Access Journals (Sweden)

    Naser El-Sheimy

    2012-09-01

    Full Text Available Inertial Navigation Systems (INS consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on Earth’s magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are usually corrupted by several errors, including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO-based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometers. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. Furthermore, the proposed algorithm can help in the development of Pedestrian Navigation Devices (PNDs when combined with inertial sensors and GPS/Wi-Fi for indoor navigation and Location Based Services (LBS applications.

  10. Sampling-based Algorithms for Optimal Motion Planning

    CERN Document Server

    Karaman, Sertac

    2011-01-01

    During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically opti...

  11. Task-Based Optimization of Computed Tomography Imaging Systems

    CERN Document Server

    Sanchez, Adrian A

    2015-01-01

    The goal of this thesis is to provide a framework for the use of task-based metrics of image quality to aid in the design, implementation, and evaluation of CT image reconstruction algorithms and CT systems in general. We support the view that task-based metrics of image quality can be useful in guiding the algorithm design and implementation process in order to yield images of objectively superior quality and higher utility for a given task. Further, we believe that metrics such as the Hotelling observer (HO) SNR can be used as summary scalar metrics of image quality for the evaluation of images produced by novel reconstruction algorithms. In this work, we aim to construct a concise and versatile formalism for image reconstruction algorithm design, implementation, and assessment. The bulk of the work focuses on linear analytical algorithms, specifically the ubiquitous filtered back-projection (FBP) algorithm. However, due to the demonstrated importance of optimization-based algorithms in a wide variety of CT...

  12. Sensor Calibration Design Based on D-Optimality Criterion

    Directory of Open Access Journals (Sweden)

    Hajiyev Chingiz

    2016-09-01

    Full Text Available In this study, a procedure for optimal selection of measurement points using the D-optimality criterion to find the best calibration curves of measurement sensors is proposed. The coefficients of calibration curve are evaluated by applying the classical Least Squares Method (LSM. As an example, the problem of optimal selection for standard pressure setters when calibrating a differential pressure sensor is solved. The values obtained from the D-optimum measurement points for calibration of the differential pressure sensor are compared with those from actual experiments. Comparison of the calibration errors corresponding to the D-optimal, A-optimal and Equidistant calibration curves is done.

  13. NOISY OBSERVATION BASED STABILIZATION AND OPTIMIZATION FOR UNKNOWN SYSTEMS

    Institute of Scientific and Technical Information of China (English)

    CHEN Hanfu(Han-Fu Chen)

    2003-01-01

    The paper addresses optimization of a performance function which either is optimized via stabilizing and controlling the underlying unknown system or is directly optimized on the basis of its noise-corrupted observations. For the first case the unknown system is identified and then the indirect adaptive control approach is applied to optimize the performance function. For the second case the stochastic approximation method is used to optimize the objective function, and it appears that a number of problems arising from applications may be reduced to the one solvable by this approach. The paper demonstrates some basic results in the area, but with no intention to give a complete survey.

  14. Topology Optimization in Electric Car Body Frame Based on Optistruct

    Directory of Open Access Journals (Sweden)

    Ge Dongdong

    2017-01-01

    Full Text Available In order to optimize the structure of the electric car body frame, the static analysis of the car frame were carried on. For the goal of the frame’s weight minimum, OptiStruct software was used to topology optimization design. And the optimal material distribution program of the frame structure was got. Static strength before and after optimization was comprehensive compared through the stress, deformation. The results showed that the weight of frame after optimization was reduced by 18.96%, and the requirements of the strength and stiffness were ensured.

  15. Reliability-based design optimization for flexible mechanism with particle swarm optimization and advanced extremum response surface method

    Institute of Scientific and Technical Information of China (English)

    张春宜; 宋鲁凯; 费成巍; 郝广平; 刘令君

    2016-01-01

    To improve the computational efficiency of the reliability-based design optimization (RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method (PSO-AERSM) was proposed by integrating particle swarm optimization (PSO) algorithm and advanced extremum response surface method (AERSM). Firstly, the AERSM was developed and its mathematical model was established based on artificial neural network, and the PSO algorithm was investigated. And then the RBDO model of flexible mechanism was presented based on AERSM and PSO. Finally, regarding cross-sectional area as design variable, the reliability optimization of flexible mechanism was implemented subject to reliability degree and uncertainties based on the proposed approach. The optimization results show that the cross-section sizes obviously reduce by 22.96 mm2 while keeping reliability degree. Through the comparison of methods, it is demonstrated that the AERSM holds high computational efficiency while keeping computational precision for the RBDO of flexible mechanism, and PSO algorithm minimizes the response of the objective function. The efforts of this work provide a useful sight for the reliability optimization of flexible mechanism, and enrich and develop the reliability theory as well.

  16. Study on optimization control method based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    FU Hua; SUN Shao-guang; XU Zhen-Iiang

    2005-01-01

    In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limitations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advantages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With optimization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.

  17. Optimization of Natural Lipstick Formulation Based on Pitaya (Hylocereus polyrhizus) Seed Oil Using D-Optimal Mixture Experimental Design

    OpenAIRE

    2014-01-01

    The D-optimal mixture experimental design was employed to optimize the melting point of natural lipstick based on pitaya (Hylocereus polyrhizus) seed oil. The influence of the main lipstick components—pitaya seed oil (10%–25% w/w), virgin coconut oil (25%–45% w/w), beeswax (5%–25% w/w), candelilla wax (1%–5% w/w) and carnauba wax (1%–5% w/w)—were investigated with respect to the melting point properties of the lipstick formulation. The D-optimal mixture experimental design was applied to opti...

  18. OPTIMIZATION METHOD FOR VIRTUAL PRODUCT DEVELOPMENT BASED ON SIMULATION METAMODEL AND ITS APPLICATION

    Institute of Scientific and Technical Information of China (English)

    Pan Jun; Fan Xiumin; Ma Dengzhe; Jin Ye

    2003-01-01

    Virtual product development (VPD) is essentially based on simulation. Due to computational inefficiency, traditional engineering simulation software and optimization methods are inadequate to analyze optimization problems in VPD. Optimization method based on simulation metamodel for virtual product development is proposed to satisfy the needs of complex optimal designs driven by VPD. This method extends the current design of experiments (DOE) by various metamodeling technologies. Simulation metamodels are built to approximate detailed simulation codes, so as to provide link between optimization and simulation, or serve as a bridge for simulation software integration among different domains. An example of optimal design for composite material structure is used to demonstrate the newly introduced method.

  19. Optimal ship imaging for shore-based ISAR using DCF estimation

    Institute of Scientific and Technical Information of China (English)

    Ling Wang; Zhenxiao Cao; Ning Li; Teng Jing; Daiyin Zhu

    2015-01-01

    The optimal imaging time selection of ship targets for shore-based inverse synthetic aperture radar (ISAR) in high sea conditions is investigated. The optimal imaging time includes opti-mal imaging instants and optimal imaging duration. A novel method for optimal imaging instants selection based on the estimation of the Doppler centroid frequencies (DCFs) of a series of images obtained over continuous short durations is proposed. Combined with the optimal imaging duration selection scheme using the image contrast maximization criteria, this method can provide the ship images with the highest focus. Simulated and real data pro-cessing results verify the effectiveness of the proposed imaging method.

  20. An Optimization-Based Approach to Injector Element Design

    Science.gov (United States)

    Tucker, P. Kevin; Shyy, Wei; Vaidyanathan, Rajkumar; Turner, Jim (Technical Monitor)

    2000-01-01

    An injector optimization methodology, method i, is used to investigate optimal design points for gaseous oxygen/gaseous hydrogen (GO2/GH2) injector elements. A swirl coaxial element and an unlike impinging element (a fuel-oxidizer-fuel triplet) are used to facilitate the study. The elements are optimized in terms of design variables such as fuel pressure drop, APf, oxidizer pressure drop, deltaP(sub f), combustor length, L(sub comb), and full cone swirl angle, theta, (for the swirl element) or impingement half-angle, alpha, (for the impinging element) at a given mixture ratio and chamber pressure. Dependent variables such as energy release efficiency, ERE, wall heat flux, Q(sub w), injector heat flux, Q(sub inj), relative combustor weight, W(sub rel), and relative injector cost, C(sub rel), are calculated and then correlated with the design variables. An empirical design methodology is used to generate these responses for both element types. Method i is then used to generate response surfaces for each dependent variable for both types of elements. Desirability functions based on dependent variable constraints are created and used to facilitate development of composite response surfaces representing the five dependent variables in terms of the input variables. Three examples illustrating the utility and flexibility of method i are discussed in detail for each element type. First, joint response surfaces are constructed by sequentially adding dependent variables. Optimum designs are identified after addition of each variable and the effect each variable has on the element design is illustrated. This stepwise demonstration also highlights the importance of including variables such as weight and cost early in the design process. Secondly, using the composite response surface that includes all five dependent variables, unequal weights are assigned to emphasize certain variables relative to others. Here, method i is used to enable objective trade studies on design issues