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A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network

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APPLIED ENERGY 2014年 134卷 102-113页

作者： Yu, Feng Xu, Xiaozhong Shanghai Normal Univ Coll Informat & Elect Engn Shanghai Peoples R China

This paper proposes an appropriate combinational approach which is based on improved BP neural network for short-term gas load forecasting, and the network is optimized by the real-coded genetic algorithm. Firstly, several kinds of modifications are carried out on the standard neural network to accelerate the convergence speed of network, including improved additional momentum factor, improved self-adaptive learning rate and improved momentum and self-adaptive learning rate. Then, it is available to use the global search capability of optimized genetic algorithm to determine the initial weights and thresholds of BP neural network to avoid being trapped in local minima. The ability of GA is enhanced by cat chaotic mapping. In light of the characteristic of natural gas load for Shanghai, a series of data preprocessing methods are adopted and more comprehensive load factors are taken into account to improve the prediction accuracy. Such improvements facilitate forecasting efficiency and exert maximum performance of the model. As a result, the integration model improved by modified additional momentum factor gets more ideal solutions for short-term gas load forecasting, through analyses and comparisons of the above several different combinational algorithms. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Natural gas load forecasting model Data pre-processing Modified BP neural network real-coded genetic algorithm Chaos characteristics

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A ROBUST METHOD FOR LOCATING THE POLES OF GREEN'S FUNCTION IN LOSSLESS OR LOSSY MULTILAYERED MEDIA

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2009年第8期51卷 1929-1934页

作者： Chen, R. S. Zhuang, W. Wang, D. X. Ding, D. Z. Nanjing Univ Sci & Technol Dept Commun Engn Nanjing 210094 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

This article describes a robust method for locating the poles of the spectral-domain Green's functions in a lossless or lossy multilayered medium. real-coded genetic algorithm (RGA) is first introduced to approximate the locations of the surface- and/or leaky-wave poles and then followed by a Cauchy's integral theorem to rapidly refine the poles and remove the unwanted ones. Numerical examples demonstrate the accuracy efficiency of the method. (C) 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 1929-1934, 2009;Published online in Wiley InterScience (***). DOI 10.1002/mop.24472

关键词： surface wave pole leaky wave pole real-coded genetic algorithm Cauchy's integral theorem

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A model of motor control of the nematode C. elegans with neuronal circuits

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2005年第1-2期35卷 75-86页

作者： Suzuki, M Tsuji, T Ohtake, H Hiroshima Univ Dept Artificial Complex Syst Engn Hiroshima 7398527 Japan Osaka Univ Dept Biotechnol Suita Osaka Japan

Objective: Living organisms have mechanisms to adapt to various conditions of external environments. If we can realize these mechanisms on the computer, it may be possible to apply methods of biological and biomimetic adaptation to the engineering of artificial machines. This paper focuses on the nematode Caenorhabditis elegans (C. elegans), which has a relatively simple structure and is one of the most studied multicellular organisms. We aim to develop its computer model, artificial C. elegans, to analyze control mechanisms with respect to motion. Although C. elegans processes many kinds of external stimuli, we focused on gentle touch stimulation. Methods: The proposed model consists of a neuronal circuit model for motor control that responds to gentle touch stimuli and a kinematic model of the body for movement. All parameters included in the neuronal circuit model are adjusted by using the real-coded genetic algorithm. Also, the neuronal oscillator model is employed in the body model to generate the sinusoidal movement. The motion velocity of the body model is controlled by the neuronal circuit model so as to correspond to the touch stimuli that are received in sensory neurons. Conclusion: The computer simulations confirmed that the proposed model is capable of realizing motor control similar to that of the actual organism qualitatively. By using the artificial organism it may be possible to clarify or predict some characteristics that cannot be measured in actual experiments. With the recent development of computer technology, such a computational analysis becomes a real possibility. The artificial C. elegans will contribute for studies in experimental biology in future, although it is stilt developing at present. (c) 2005 Elsevier B.V. All rights reserved.

关键词： C. elegans neuronal circuit model body model neuronal oscillator real-coded genetic algorithm computer simulation

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Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2008年第2期30卷 140-149页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

This paper presents nondominated sorting genetic algorithm-II for dynamic economic emission dispatch problem. This problem is formulated as a nonlinear constrained multi-objective optimization problem. Nondominated sorting genetic algorithm-II is proposed to handle dynamic economic emission dispatch problem as a true multi-objective optimization problem with competing and noncommensurable objectives. The proposed approach has a good performance in finding a diverse set of solutions and in converging near the true pareto-optimal set. Numerical results for a sample test system have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic economic emission dispatch problem in one single run. The comparison with the classical technique demonstrates the superiority of the proposed algorithm. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： dynamic economic dispatch dynamic economic emission dispatch multi-objective optimization nondominated sorting genetic algorithm-II real-coded genetic algorithm

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Particle Swarm Assisted genetic algorithm for the Optimal Design of Flexbeam Sections

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2013年第4期14卷 341-349页

作者： Dhadwal, Manoj Kumar Lim, Kyu Baek Jung, Sung Nam Kim, Tae Joo Konkuk Univ Dept Aerosp Informat Engn Seoul South Korea Korea Aerosp Res Inst Rotorcraft Program Off Rotor Dept Taejon South Korea

This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved genetic algorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the genetic algorithm, of crossover and mutation, are revisited, and a new rank-based multi-parent crossover operator is utilized. The rank-based crossover operator simultaneously enhances both the local, and the global exploration. The benchmark results demonstrate remarkable improvements, in terms of efficiency and robustness, as compared to other state-of-the-art algorithms. The developed algorithm is adopted for two baseline flexbeam section designs, and optimum cross-section configurations are obtained with less function evaluations, and less computation time.

关键词： Beam Section Optimization real-coded genetic algorithm Particle Swarm Optimization Rank-based Multi-Parent Crossover Flexbeam

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Particle swarm optimization based feedforward controller for a XY PZT positioning stage

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MECHATRONICS 2012年第5期22卷 614-628页

作者： Lin, Chih-Jer Lin, Po-Ting Natl Taipei Univ Technol Grad Inst Automat Technol Taipei 10608 Taiwan

Compensations for cross-axis coupling effect and hysteretic nonlinearity of a novel XY piezo-actuated positioning stage are presented in this study. The piezo-actuated stage utilizes a monolithic flexure-based mechanism (FBM) to achieve translations in X- and Y-axes instead of using stacked mechanisms. A hysteresis model with crossover term is proposed to alleviate the cross coupling effect between X- and Y-stages during precision positioning tasks. System identifications using real-coded genetic algorithm (RCA) and clonal selection algorithm (CSA) are compared with particle swarm optimization (PSO). The results show that PSO provides better performance than the others. Therefore, a feedforward controller with cross-axis coupling compensation is studied and the used for the piezo-actuated FBM to enhance the precision of the coarse positioning stage. The experimental results confirm that the proposed controller can achieve precision tracking tasks with submicron precision. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Piezoelectric actuator Flexure-based mechanism real-coded genetic algorithm Clonal selection algorithm Particle swarm optimization System identification

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An approach for design optimization of helical gear pair with balanced specific sliding and modified tooth profile

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2019年第1期60卷 331-341页

作者： Rai, Paridhi Barman, Asim Gopal Natl Inst Technol Patna Dept Mech Engn Patna Bihar India

A unique perspective in design optimization of helical gear pair has been emerged and presented in this article. Specific sliding needs to be balanced for enhancing wear and scuffing resistance of helical gears. Optimized modification in tooth profile has immense benefits in gear operations. Effect of profile shift and specific sliding on design optimization of helical gear pair have been studied and found to be beneficial of great importance. Preventing undercutting, balancing of wear and bending fatigue strength and centre distance adjustment are some of the advantages of profile tooth modifications. real-coded genetic algorithm (RCGA) has been used to attain minimum volume of helical gear pair. Profile shift coefficients for gear and pinion have been included as design variables along with mostly used generic design variables, module, face width and number of teeth on pinion. Specific sliding, transverse contact ratio and face width constraint along with other strength requirements are the design constraints. The optimal design solutions obtained with and without profile shift are recorded and compared with commercially used software for validation. 3D computer-aided design (CAD) models have been developed by using the optimized results obtained from RCGA and commercially used software. These CAD models are used for performing finite element analysis (FEA) on the helical gear set for analyzing the stress developed in the gear pair. The developed stress in the helical gear pair is found to be well within the allowable stress limits for the gear pair.

关键词： Helical gear Profile shift Specific sliding Volume optimization real-coded genetic algorithm FEM analysis

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Multiobjective expected value model for portfolio selection in fuzzy environment

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OPTIMIZATION LETTERS 2013年第8期7卷 1765-1791页

作者： Gupta, Pankaj Mittal, Garima Mehlawat, Mukesh Kumar Univ Delhi Dept Operat Res Delhi 110007 India Apeejay Sch Management Dept Decis Sci New Delhi India

In this paper, we propose a credibilistic framework for portfolio selection problem using an expected value multiobjective model with fuzzy parameters. We consider short term return, long term return, risk and liquidity as key financial criteria. A solution procedure comprising fuzzy goal programming and fuzzy simulation based real-coded genetic algorithm is developed to solve the model. The proposed solution approach is considered advantageous particularly for the cases where the fuzzy parameters of the problem may assume any general functional form. An empirical study is included to illustrate the usefulness of the proposed model and solution approach in real-world applications of portfolio selection.

关键词： Fuzzy portfolio selection Expected value model Multiobjective programming real-coded genetic algorithm Fuzzy simulation

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System identification of a Scott-Russell amplifying mechanism with offset driven by a piezoelectric actuator

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APPLIED MATHEMATICAL MODELLING 2012年第6期36卷 2788-2802页

作者： Chen, Ching-Ming Hsu, Yen-Chun Fung, Rong-Fong Natl Kaohsiung First Univ Sci Technol Dept Mech & Automat Engn Kaohsiung 824 Taiwan Natl Kaohsiung First Univ Sci Technol Grad Inst Electroopt Engn Kaohsiung 824 Taiwan Natl Kaohsiung First Univ Sci & Technol Inst Engn Sci & Technol Kaohsiung 824 Taiwan Natl Kaohsiung First Univ Sci & Technol Grad Inst Elector Opt Engn Kaohsiung 824 Taiwan

The micro-positioning Scott-Russell (SR) mechanism driven by a piezoelectric actual (PA) is designed to magnify the displacement of the PA. The main feature of the SR mechanism is its straight-line output for a given input displacement. In this paper, the r objective is to propose a complete mathematical model, including the driving cir Bouc-Wen hysteresis and mechanical equation, to describe the system. In system identification, the real-coded genetic algorithm (RGA) is adopted to find the parameters of the mechanism and the PA. From the comparisons between numerically identified dynamic responses and experimental results, it is found that the error percentages are wi -1.4% similar to 1.7% for the system without offset and -3.85% similar to 3.33% for the system with of It is concluded that the numerically identified parameters of the complete model almost the same as those of the real system, and the RGA method is feasible for the identification of the SR mechanism driven by the PA. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Bouc-Wen hysteresis Piezoelectric actuator real-coded genetic algorithm System identification Scott-Russell mechanism

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Coefficient estimation of IIR filter by a multiple crossover genetic algorithm

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2006年第9-10期51卷 1437-1444页

作者： Chang, Wei-Der Shu Te Univ Dept Comp & Commun Kaohsiung 824 Taiwan

This paper proposes an improved genetic algorithm (GA) with multiple crossovers to estimate the system coefficients for the infinite impulse response (IIR) digital filter. In the traditional crossover operation, it needs two parent chromosomes to achieve the crossover work, whereas in this paper the proposed algorithm selects three chromosomes for crossover in order to generate more promising offspring toward the problem solution. Each of unknown IIR coefficients is called a gene and the collection of genes forms a chromosome. A population of chromosomes is evolved by the genetic operations of reproduction, multiple crossover, and mutation. Finally, two illustrative examples including the band pass and band stop IIR filters are demonstrated to verify the proposed method. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： IIR filters coefficients estimation multiple crossover real-coded genetic algorithm optimization

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