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Extraction of Multicrop Planning Rules in a Reservoir System: Application of Evolutionary algorithms

JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2013年第6期139卷 490-498页

作者： Fallah-Mehdipour, E. Bozorg-Haddad, Omid Marino, M. A. Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Tehran *** Iran Univ Calif Davis Dept Civil & Environm Engn Dept Land Air & Water Resources Davis CA 95616 USA Univ Calif Davis Dept Biol & Agr Engn Davis CA 95616 USA

Multicropping is the practice of growing two or more crops in the same space during a single growing season. Planning rules are mathematical equations that use previous experiences of a water resource system to balance the system's water supply and demand, and calculate multicrop areas in various periods. In this paper, linear and nonlinear planning rules are developed for optimal multicrop irrigation areas associated with reservoir operation policies in a reservoir-irrigation system. Reservoir operations are related to water allocations to each irrigated area by considering inflow and storage volume of the reservoir as the water supply in a monthly operation period. Evolutionary algorithms (EAs) can determine optimal multicropping patterns planning rules by considering various mathematical patterns. In this paper, three EAs, namely, (1) genetic algorithm (GA), (2) particle swarm optimization (PSO), and (3) shuffled frog leaping algorithm (SFLA) are employed and compared to maximize the total net benefit of the water resource system by supplying irrigation water for a proposed multicropping pattern over the planning horizon. Results show that the SFLA achieves the best solution, with the maximum value of the objective function in both linear and nonlinear planning rules compared to the GA and PSO. Moreover, the best yield of nonlinear rules is 45.52% better (higher) than that obtained by linear rules. (C) 2013 American Society of Civil Engineers.

关键词： Multicrop planning rule Evolutionary algorithms Genetic algorithm particle swarm optimization algorithm Shuffled frog leaping algorithm

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A New Method For Machinery Fault Diagnoses Based On an Optimal Multiscale Morphological Filter

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STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2013年第12期59卷 719-724页

作者： Tan, Wei Chen, Xiaoan Dong, Shaojiang Chongqing Univ State Key Lab Mech Transmiss Chongqing 630044 Peoples R China Chongqing Univ Technol Key Lab Adv Mfg Technol Automobile Parts Chongqing Peoples R China

In order to effectively eliminate the noise and extract the impulse components in the vibration signals, a new method based on an optimal multiscale morphological filter is proposed. In this method, firstly, the average of the closing and opening operator is used to construct the morphological filter, then the multiscale morphological filters' structure elements (SEs) are optimized and selected using a particle swarm optimization algorithm (PSO). The noise in the original signal is filtered by the multiscale morphological filter. The proposed method was evaluated by simulated signals and bearing fault signals. The results show that the method can effectively filter the noise and extract the impulse characteristics of the vibration signals, which demonstrate the effectiveness of the proposed method.

关键词： multiscale morphological filter structure element particle swarm optimization algorithm noise reduction

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Comparative analysis of the machine repair Problem with imperfect coverage and service pressure condition

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APPLIED MATHEMATICAL MODELLING 2013年第5期37卷 2870-2880页

作者： Wang, Kuo-Hsiung Liou, Cheng-Dar Lin, Yu-Hsueh Providence Univ Dept Comp Sci & Informat Management Taichung 43301 Taiwan Natl Formosa Univ Dept Business Adm Huwei 63201 Yunlin Taiwan Natl Chung Hsing Univ Dept Appl Math Taichung 402 Taiwan

In this paper we analyze the warm-standby M/M/R machine repair problem with multiple imperfect coverage which involving the service pressure condition. When an operating machine (or warm standby) fails, it may be immediately detected, located, and replaced with a coverage probability c by a standby if one is available. We use a recursive method to develop the steady-state analytic solutions which are used to calculate various system performance measures. The total expected profit function per unit time is derived to determine the joint optimal values at the maximum profit. We first utilize the direct search method to measure the various characteristics of the profit function followed by Quasi-Newton method to search the optimal solutions. Furthermore, the particle swarm optimization (PSO) algorithm is implemented to find the optimal combinations of parameters in the pursuit of maximum profit. Finally, a comparative analysis of the Quasi-Newton method with the PSO algorithm has demonstrated that the PSO algorithm provides a powerful tool to perform the optimization problem. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Comparative analysis Imperfect coverage particle swarm optimization algorithm Profit Service pressure

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Research on diversity of particle swarm optimization algorithm based on dynamic weight

Shiyou Huagong Gaodeng Xuexiao Xuebao/Journal of Petrochemic...

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Shiyou Huagong Gaodeng Xuexiao Xuebao/Journal of Petrochemical Universities 2008年第4期21卷 91-94页

作者： Zhu, Pei-Yi Zhang, Yu-Lin Department of Automation Changshu Institute of Technology Changshu 215500 China Department of Electric Information Engineering Huaiyin Institute of Technology Huaian 223003 China

Stocks diversity is the precondition for ensuring the convergence of PSO algorithm. The definition of stocks diversity is clear and the operand is small, moreover which was analyzed by particle evolution degree and aggregation degree. A changed algorithm was proposed based on adjusting weight adaptively. The algorithm ensures population diversity and avoids premature convergence effectively. Simulation results indicate that this algorithm not only speeds up the population the evolution speed, but also strengthens the algorithm the overall situation astringency, and convergence of probability also increases from 15% to 100%.

关键词： Diversity Dynamic weight particle swarm optimization algorithm Premature

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Supply Reliability and Generation Cost Analysis Due to Load Forecast Uncertainty in Unit Commitment Problems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2013年第3期28卷 2242-2252页

作者： Wang, Bo Li, You Watada, Junzo Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

The goal of a unit commitment optimization problem is to reduce the total generation cost as much as possible while satisfying future power demands. Thus, analysis must be performed based on correct predictions of future demands. However, various uncertain factors affect these loadsmaking an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory to evaluate the future uncertain loads. The objective of this research is to build a two-stage multi-objective fuzzy programming model based on 24-hour uncertain load forecasting. The first stage is a decision-making process on the interval data of the imprecise power loads, whereas the second stage pursues the optimization of the unit commitment scheduling, which can help find both optima simultaneously by maximizing power supply reliability and minimizing total generation cost. To define the supply reliability under uncertain forecasting, we propose a new concept of maximal blackout time during successful operation, which is based on the fuzzy credibility theory. Furthermore, as a solution approach to this model, an improved two-stage multi-objective particle swarm optimization algorithm is designed based on our previous studies. Finally, the performance of this algorithm is discussed in comparison with experimental results from several test systems.

关键词： Fuzzy set theory maximal blackout time during successful operation particle swarm optimization algorithm power loads two-stage multi-objective problem uncertainty

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PSO algorithm for fundamental frequency optimization of fiber metal laminated panels

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STRUCTURAL ENGINEERING AND MECHANICS 2013年第5期47卷 713-727页

作者： Ghashochi-Bargh, H. Sadr, M. H. Amirkabir Univ Technol Ctr Excellence Computat Aerosp Engn Dept Aerosp Engn Tehran Iran

In current study, natural frequency response of fiber metal laminated (FML) fibrous composite panels is optimized under different combination of the three classical boundary conditions using particle swarm optimization (PSO) algorithm and finite strip method (FSM). The ply angles, numbers of layers, panel length/width ratios, edge conditions and thickness of metal sheets are chosen as design variables. The formulation of the panel is based on the classical laminated plate theory (CLPT), and numerical results are obtained by the semi-analytical finite strip method. The superiority of the PSO algorithm is demonstrated by comparing with the simple genetic algorithm.

关键词： fiber metal laminated panel particle swarm optimization algorithm fundamental frequency

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A novel current injection model of GCSC for control and damping of power system oscillations

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IETE JOURNAL OF RESEARCH 2013年第6期59卷 768-773页

作者： Safari, Amin Rezaei, Navid Islamic Azad Univ Ahar Branch Dept Elect Engn Ahar Iran Iran Univ Sci & Technol Dept Elect Engn Tehran Iran

The paper proposes the theory and the modeling technique of the new invention of the flexible AC transmission system (FACTS) device, i.e., Gate controlled series capacitor (GCSC) for power flow control of power transmission lines and damping of low-frequency oscillations. In this study, a current injection model of the GCSC is used for studying the effect of the GCSC on the low-frequency oscillations, which is incorporated in the transmission system model. To be sure of optimal adjustment of the applied damping controller, the particle swarm optimization algorithm is employed as an efficient heuristic optimizer to find out the near global optimum set of the damping controller parameters. The proposed model is applied to a damping controller design of a multi-machine power system. Detailed simulations are carried out with MATLAB/SIMULINK environment and the effect of the GCSC-based damping controller and the current injection model over the system stability is studied.

关键词： Enhancing dynamic stability Gate controlled series capacitor ITAE performance index particle swarm optimization algorithm

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Soft sensor modeling based on particle swarm optimization algorithm and support vector machine

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Huadong Ligong Daxue Xuebao /Journal of East China University of Science and Technology 2008年第1期34卷 131-134页

作者： Bu, Yan-Ping Yu, Jinshou Institute of Automation East China University of Science and Technology Shanghai 200237 China School of Technology Shanghai Jiaotong University Shanghai 200231 China

On the basis of analyzing the particle swarm optimization (PSO) algorithm and support vector machine (SVM), this paper applies the PSO algorithm with last out mechanism to optimize the parameters of SVM. Then, the PSO-SVM model about a practical soft-sensor of gasoline endpoint of delayed coking plant is constructed. The method takes advantages of the minimum structure risk of SVM and the quickly globally optimizing ability of PSO for soft sensor modeling. The simulation results show that the model has effective generalization performance and higher precision.

关键词： Kernel function particle swarm optimization algorithm Soft sensor Support vector machine

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A novel fitting algorithm for Brillouin scattering spectrum of distributed sensing systems based on RBFN networks

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OPTIK 2013年第8期124卷 718-721页

作者： Zhang, Yanjun Fu, Guangwei Liu, Yin Bi, Weihong Li, Da Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China

In order to extract features for Brillouin scattering spectrum of distributed sensing systems with high accuracy, a novel fitting algorithm, using a hybrid algorithm based on particle swarm optimization algorithm and Levenberg-Marquardt algorithm to optimize the optimization process of radial basis function networks, is explanatorily proposed. Compared the proposed algorithm with traditional BP neural networks, the five times polynomial curve and piecewise cubic spline interpolation in fitting the simulative And experimental spectrum, respectively, the evaluation parameter is relatively better than other three algorithms under the same experiment with different pulse widths. The numerical and experimental results showed that modified RBFN networks have some referential roles, which can guarantee the accurate measurement of Brillouin scattering spectrum. (C) 2012 Elsevier GmbH. All rights reserved.

关键词： Distributed fiber sensing systems Brillouin scattering Neural networks Levenberg-Marquardt particle swarm optimization algorithm

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Aeroengine State Variable Modeling based on the PSO algorithm

Aeroengine State Variable Modeling based on the PSO Algorith...

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Chinese Automation Congress (CAC)

作者： Wang, Bin Shi, Yulin Wang, Xi Beihang Univ Dept Jet Prop Aerosp Control Lab Beijing 100191 Peoples R China

ISBN: (纸本)9781479903337

In this paper, a detailed research about aeroengine small perturbation State Variable Model (SVM) has been carried out. The small perturbation SVM was obtained by using partial derivative method and the particle swarm optimization (PSO) algorithm was selected to optimize parameter matrices. On the basis of comparison, the calculation results of the SVM have quite remarkable consistency with those results calculated by the nonlinear model. In order to better verify the accuracy and efficiency of this method, a real-time piecewise linear dynamic model (RPLDM) was constructed;and a transient simulation on sea-level condition was carried out. The results showed that the proposed approach to establishing the small perturbation SVM and the RPLDM was highly rated in validity and applicability.

关键词： aeroengine modeling state variable model particle swarm optimization algorithm real-time piecewise linear dynamic model

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