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Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system

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ENERGY 2018年 148卷 59-78页

作者： Wang, Jianzhou Yang, Wendong Du, Pei Li, Yifan Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Liaoning Peoples R China Univ Calif Los Angeles Coll Letters & Sci Los Angeles CA 90024 USA

Electrical power system (EPS) forecasting plays a significant role in economic and social development but it remains an extremely challenging task. Because of its significance, relevant studies on EPS are especially needed. More specifically, only a few of the previous studies in this area conducted in-depth investigations of the entire EPS forecasting and merely focused on modeling individual signals centered on wind speed or electrical load. Moreover, most of these past studies concentrated on accuracy improvements and usually ignore the significance of forecasting stability. Therefore, to simultaneously achieve high accuracy and dependable stability, a hybrid forecasting framework based on the multi objective dragonfly algorithm (MODA) was successfully developed in this study. The framework consists of four modules data preprocessing, optimization, forecasting, and evaluation modules. In this framework, MODA is employed to optimize the Elman neural network (ENN) model as a part of the optimization module to overcome the drawbacks of single-objective optimization algorithms. In addition, data preprocessing and evaluation modules are incorporated to improve forecasting performance and conduct a comprehensive evaluation for this framework, respectively. Empirical results reveal that the developed forecasting framework can be an effective tool for the planning and management of power grids. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Electrical power system Hybrid forecasting framework multi-objective optimization algorithm Forecasting accuracy and stability

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Development of support vector machine-based model and comparative analysis with artificial neural network for modeling the plant tissue culture procedures: effect of plant growth regulators on somatic embryogenesis of chrysanthemum, as a case study

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PLANT METHODS 2020年第1期16卷 112-112页

作者： Hesami, Mohsen Naderi, Roohangiz Tohidfar, Masoud Yoosefzadeh-Najafabadi, Mohsen Univ Guelph Dept Plant Agr Guelph ON Canada Univ Tehran Fac Agr Dept Hort Sci Karaj Iran Shahid Beheshti Univ Fac Sci & Biotechnol Dept Plant Biotechnol GC Tehran Iran

Background Optimizing the somatic embryogenesis protocol can be considered as the first and foremost step in successful gene transformation studies. However, it is usually difficult to achieve an optimized embryogenesis protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach, such as machine learning algorithms for this aim. In the present study, two machine learning algorithms, including multilayer Perceptron (MLP) as an artificial neural network (ANN) and support vector regression (SVR), were employed to model somatic embryogenesis of chrysanthemum, as a case study, and compare their prediction accuracy. Results The results showed that SVR (R-2 > 0.92) had better performance accuracy than MLP (R-2 > 0.82). Moreover, the Non-dominated Sorting Genetic algorithm-II (NSGA-II) was also applied for the optimization of the somatic embryogenesis and the results showed that the highest embryogenesis rate (99.09%) and the maximum number of somatic embryos per explant (56.24) can be obtained from a medium containing 9.10 mu M 2,4-dichlorophenoxyacetic acid (2,4-D), 4.70 mu M kinetin (KIN), and 18.73 mu M sodium nitroprusside (SNP). According to our results, SVR-NSGA-II was able to optimize the chrysanthemum's somatic embryogenesis accurately. Conclusions SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.

关键词： Artificial intelligence Support vector regression multi-objective optimization algorithm Machine learning algorithms multilayer perceptron Somatic embryogenesis Chrysanthemum Nitric oxide

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Research on multi-objective Optimized Target Speed Curve of Subway Operation Based on ATO System

International Core Journal of Engineering

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International Core Journal of Engineering 2020年第2期6卷 133-137页

作者： Duoduo Gong Guilin Li

To optimize the target speed curve of urban rail trains, first analyze the train operation control strategy and optimization principles, and combine the performance index evaluation system of the train automatic operation curve to establish a model of the evaluation index from subway energy saving, train running on time, and From the perspective of passenger comfort, fully utilizing the control structure of the ATO system, based on the PSO-CS multi-objective optimization algorithm, researching the generation of the target speed curve of the subway train operation, and performing simulation experiment analysis. Simulation results show that the PSO-CS algorithm can provide the optimal control target speed curve for urban rail train operation.

关键词： Performance index multi-objective optimization algorithm target speed curve

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Design and Implementation of Fuzzy-PI Controllers for PMSM Based on multi-objective optimization algorithms 5

Design and Implementation of Fuzzy-PI Controllers for PMSM B...

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5th International Conference on Mechanical, Automotive and Materials Engineering (CMAME)

作者： Kao, I-Hsi Lu, Kuna-Chung Perng, Jau-Woei Natl Sun Yat Sen Univ Dept Mech & Electromech Engn Kaohsiung Taiwan

ISBN: (纸本)9781538604328

Various intelligent algorithms have been applied to our daily lives, such as fuzzy theory, neural networks, and machine learning. These methods are widely used for solving many real-world problems;however, these algorithms also exhibit deficiencies and limitations. This paper introduces the recently improved algorithm, known as multi-objective particle swarm optimization, based on decomposition and dominance (D<^>2 MOPSO) in order to design the permanent magnet synchronous motor (PMSM) fuzzy controller for different objects. This means that the user can easily change the customized controller, according to their requirements. Furthermore, this paper compares the final decision of the controller parameter with other algorithms: the multi-objective particle swarm optimization with crowding distance (MOPSO-CD), and nondominated sorting genetic algorithm II (NSGA-II). The simulation results of the three algorithms indicate the optimum PMSM controller parameter in the computing software MATLAB. Finally, we implement the fuzzy controller in an embedded system (DSP28069) to demonstrate that our design matches the reality system response and meets the user's demands with ease.

关键词： fuzzy control multi-objective optimization algorithm multi-objective particle swarm optimization MOPSO Nondominated sorting genetic algorithm NSGA-II

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Comparative Study of Ant Colony algorithms for multi-objective optimization

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INFORMATION 2019年第1期10卷 11页

作者： Ning, Jiaxu Zhang, Changsheng Sun, Peng Feng, Yunfei Shenyang Ligong Univ Sch Informat Sci & Engn Shenyang 110159 Liaoning Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China Iowa State Univ Dept Comp Sci Ames IA 50010 USA Sams Club Technol Wal Mart Inc Bentonville AR 72712 USA

In recent years, when solving MOPs, especially discrete path optimization problems, MOACOs concerning other meta-heuristic algorithms have been used and improved often, and they have become a hot research topic. This article will start from the basic process of ant colony algorithms for solving MOPs to illustrate the differences between each step. Secondly, we provide a relatively complete classification of algorithms from different aspects, in order to more clearly reflect the characteristics of different algorithms. After that, considering the classification result, we have carried out a comparison of some typical algorithms which are from different categories on different sizes TSP (traveling salesman problem) instances and analyzed the results from the perspective of solution quality and convergence rate. Finally, we give some guidance about the selection of these MOACOs to solve problem and some research works for the future.

关键词： multi-objective optimization problem multi-objective optimization algorithm meta-heuristic algorithm multi-objective ant colony optimization

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A multi-objective mathematical model for the optimization of the transittability of complex biomolecular networks 22

A multi-objective mathematical model for the optimization of...

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22nd International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES)

作者： Ayadi, Ali Zanni-Merk, Cecilia de Beuvron, Francois de Bertrand Krichen, Saoussen CNRS UMR 7357 ICUBE Team Pole API BP 10413 F-67412 Illkirch Graffenstaden France Normandie Univ INSA Rouen LITIS F-76000 Rouen France Univ Tunis LARODEC Lab Inst Super Gest Tunis Rue Liberte Bardo 2000 Tunisia

The fundamental goal of systems biology is to understand the dynamic aspects of cells and their behaviour. This organism is represented by a network so-called complex biomolecular network in which the nodes represent the different cellular components and the edges represent the interactions occurring among them. Through this network, it is easy to study the transition states and the dynamic behaviour of cells. Indeed, perturbing some nodes of the biomolecular network induce the transition of all the network. This process, known as the "transittability", expresses the idea of steering the complex biomolecular network from an unexpected state to a desired state. In this context, we are thus interested in how to use the transittability of biomolecular networks to increase the efficiency of translational medicine for improving human health and disease, including genetic and environmental factors of of patient's wellbeing. This is a great opportunity to understand diseases, and find new diagnoses and treatments. Due to its complexity, the transittability of complex biomolecular networks can be considered as an optimization problem. Up to a recent date only few studies have been carried out in this problem. Most of them focused only on the minimization of the required nodes to steer the entire network, and others considered the minimization of the number of stimuli to be applied on the network. However, this assumption is not always realistic, because steering complex biomolecular networks is in general a multi-objective optimization problem. It requires finding appropriate trade-offs among various objectives, for example between the appropriate nodes to be stimulated and the number of external stimuli to be used and their cost, and the impact on patient's well-being. In this paper, the optimization of the transittability of complex biomolecular networks is investigated from the multi-objective perspective. In the mathematical model four criteria are considered simultaneo

关键词： Complex biomolecular network transittability multi-objective optimization algorithm optimization systems biology.

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Performance optimization for the CMOS voltage reference circuit based on NSGA-II 14

Performance optimization for the CMOS voltage reference circ...

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14th IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)

作者： Huang, Huajie Zeng, Yanhan Liao, Jinrui Chen, Rongjun Tan, Hong-zhou Guangzhou Univ Sch Phys & Elect Engn Guangzhou Guangdong Peoples R China SYSU CMU Shunde Int Joint Res Inst Guangzhou Guangdong Peoples R China Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538682401

An application of NSGA-II for parameter optimization in the design of a CMOS voltage reference circuit is presented in this paper. It is difficult by manual work to design the optimal parameters and get the best performances of TC, LS and PSRR with power and time constrains. In this paper, NSGA-II, a multi-objective optimization algorithm, is used to optimize the parameters. Considering the characteristics of CMOS voltage reference circuit, the initial population generation scheme based on manual optimal solution and the special fitness function are proposed to increase the convergence speed and optimization efficiency. Simulation results show that LS, TC and power are improved by 32.18%, 12.5% and 19.53%, respectively, compared with the manual solution.

关键词： CMOS voltage reference NSGA-II multi-objective optimization algorithm Circuit optimization

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Wind Speed Forecasting Using a Two-Stage Forecasting System With an Error Correcting and Nonlinear Ensemble Strategy

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IEEE ACCESS 2019年 7卷 176000-176023页

作者： Zhang, Lifang Dong, Yao Wang, Jianzhou Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Jiangxi Univ Finance & Econ Sch Stat Nanchang 330013 Jiangxi Peoples R China Jiangxi Univ Finance & Econ Appl Stat Res Ctr Nanchang 330013 Jiangxi Peoples R China

Precise wind speed prediction is increasingly practical for sustained and stable wind energy utilization considering the growing portion of wind energy in the global electric grid. Although plenty of wind speed forecasting approaches have been devoted to improving forecasting performance, the majority have neglected the utilization of error information, integrated the forecasting value of every component with simple ensemble approaches, and ignored forecasting stability, which may make the forecasting results poor. Considering the above drawbacks, a two-stage forecasting system is performed in our study based on the data preprocessing approach, improved multi-objective optimization algorithm, error correction and nonlinear ensemble strategy. The developed system effectively overcomes the shortcomings of previous models and observably prompts wind speed forecasting capacity. For investigating the prediction capacity of the developed system, six wind speed series obtained from China and Spain are applied as case sites. The forecasting consequences indicate that the developed system is more conducive to enhancing forecasting precision and stability than other involved models, which can provide beneficial assistance for wind speed forecasting.

关键词： Wind speed forecasting multi-objective optimization algorithm ensemble strategy forecasting accuracy

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Constrained multi-objective Weapon Target Assignment for Area Targets by Efficient Evolutionary algorithm

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IEEE ACCESS 2019年 7卷 176339-176360页

作者： Zhang, Kai Zhou, Deyun Yang, Zhen Pan, Qian Kong, Weiren Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China

The weapon target assignment (WTA) problem is the crucial decision support in Command & Control (C2). In the classic WTA model, the point-to-point saturation salvo has a low efficiency-cost ratio when the swarming targets, which have the advantage of low casualty, low cost and recyclable, become the major operational units. The constraint is less studied for the operational intention of the decision-maker. In this paper, a constrained multi-objective weapon target assignment (CMWTA) model is formulated for area targets. The optimization objectives are minimizing collateral damage and resource consumption. The multi-constraint is derived from the actual operational requirements of security evasion, damage threshold, and preference assignment. To solve CMWTA efficiently, a novel multi-objective optimization evolutionary algorithm (MOEA) is proposed to obtain the non-dominated solutions as the alternative plans for the decision-maker. A self-adaptive sorting algorithm is proposed to guarantee the completeness of the Pareto-optimal set, and a cooperative evolutionary mechanism is adopted to strengthen the convergence. For handling multi-constraint, a repair mechanism is proposed to improve the quality of infeasible solutions, and the measurement of constraint violation is designed to evaluate the infeasible solutions. A variant of the convergence metric is introduced to evaluate the algorithms solving multi-objective weapon target assignment (MWTA) problem. The experimental results show the effectiveness and superiority of the proposed approaches.

关键词： Constrained weapon target assignment collateral damage multi-objective optimization algorithm decision support system

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Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia

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ENERGIES 2019年第13期12卷 2467页

作者： Ni, Kailai Wang, Jianzhou Tang, Guangyu Wei, Danxiang Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Electricity load forecasting plays an essential role in improving the management efficiency of power generation systems. A large number of load forecasting models aiming at promoting the forecasting effectiveness have been put forward in the past. However, many traditional models have no consideration for the significance of data preprocessing and the constraints of individual forecasting models. Moreover, most of them only focus on the forecasting accuracy but ignore the forecasting stability, resulting in nonoptimal performance in practical applications. This paper presents a novel hybrid model that combines an advanced data preprocessing strategy, a deep neural network, and an avant-garde multi-objective optimization algorithm, overcoming the defects of traditional models and thus improving the forecasting performance effectively. In order to evaluate the validity of the proposed hybrid model, the electricity load data sampled in 30-min intervals from Queensland, Australia are used as a case to study. The experiments show that the new proposed model is obviously superior to all other traditional models. Furthermore, it provides an effective technical forecasting means for smart grid management.

关键词： electricity load forecasting hybrid model data preprocessing strategy multi-objective optimization algorithm deep neural network

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