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Multi-input-multi-output optimization of reactivity-controlled compression-ignition combustion in a heavy-duty diesel engine running on natural gas/diesel fuel

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INTERNATIONAL JOURNAL OF ENGINE RESEARCH 2020年第3期21卷 470-483页

作者： Ebrahimi, Mojtaba Najafi, Mohammad Jazayeri, Seyed Ali Islamic Azad Univ Dept Mech Engn Sci & Res Branch Tehran *** Iran KN Toosi Univ Technol Fac Mech Engn Tehran Iran

The aim of this study is to implement the multi-input-multi-output optimization of reactivity-controlled compression-ignition combustion in a heavy-duty diesel engine running on natural gas and diesel fuel. A single-cylinder heavy-duty diesel engine with a modified bathtub piston bowl profile is set on operation at 9.4 bar indicated mean effective pressure and running at a fixed engine speed of 1300 r/min. A certain amount of diesel fuel mass per cycle is fed into the engine at a fixed equivalence ratio without any exhaust gas recirculation. The optimization targets include reduction in engine emissions as much as possible, avoiding diesel knock occurrence, and achieving low temperature combustion concept with the least or no engine power losses. To implement the optimization, the effects of three control factors on the engine performance are assessed by the design of experiment concept-fractional factorial method. These selected control factors are intake temperature and intake pressure (both at intake valve closing) and the diesel fuel start of injection timing. Some randomized treatment combinations of chosen levels from the three selected control factors are employed to simulate reactivity-controlled compression-ignition combustion. Based on the engine's responses derived from the simulation, reactivity-controlled compression-ignition combustion's mathematical model is identified directly using an artificial neural network. Next, an optimization process is conducted using two different optimization algorithms, namely, genetic algorithm and particle swarm optimization algorithm. For assessing and validating the obtained optimal results, the obtained data are used to simulate reactivity-controlled compression-ignition combustion as the engine input factors. The results show that the proposed artificial neural network design is effectively capable of identifying reactivity-controlled compression-ignition combustion's mathematical model. Also, by optimizing reactivi

关键词： Reactivity-controlled compression ignition combustion diesel engine natural gas emission design of experiment factorial method artificial neural network multi-input-multi-output optimization genetic algorithm particle swarm optimization algorithm

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Shear strength prediction of short circular reinforced-concrete columns using soft computing methods

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ADVANCES IN STRUCTURAL ENGINEERING 2020年第14期23卷 3048-3061页

作者： Ketabdari, Hesam Karimi, Farzad Rasouli, Mahsa Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran *** Iran Qom Univ Fac Civil Engn Qom Iran

In this article, it has been aimed to predict the shear strength of short circular reinforced-concrete columns using the meta-heuristic algorithms. Based on the studies conducted so far, the parameters dominantly affecting the shear strength include axial force, longitudinal and transverse reinforcement, column dimension ratio, concrete compressive strength and ductility. In this respect, first, 200 numerical models of the short circular reinforced-concrete column incorporating various effective parameters so that a sufficient number of outputs could be provided, are analyzed by ABAQUS software to compute their shear strengths. Then, the gene expression programming and particle swarm optimization algorithms are employed to predict the shear strengths and by means of each algorithm, a relation was proposed accordingly. Then, using the experimental data, these relations are evaluated by comparing with those specified in ACI 318 and ASCE-ACI 426. The results indicate that the percentage of relative error between the experimental data and the values obtained from ACI 318 and ASCE-ACI 426 is respectively equal to 25% and 30%, which have been reduced to 13% and 9% through the gene expression programming and particle swarm optimization algorithms implying the satisfactory performance of these two algorithms. Finally, a comparison of the gene expression programming and particle swarm optimization is investigated in terms of convergence rate, degree of accuracy, and performance mechanism.

关键词： ACI 318 ASCE-ACI 426 finite element model gene expression programming particle swarm optimization algorithm shear strength short circular reinforced-concrete columns

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Modeling and simulation to optimize direct power control of DFIG in variable-speed pumped-storage power plant using teaching-learning-based optimization technique

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SOFT COMPUTING 2020年第22期24卷 16895-16915页

作者： Hosseini, Seyed Mohammad Hassan Rezvani, Alireza Islamic Azad Univ South Tehran Branch Dept Elect Engn Tehran Iran Islamic Azad Univ Saveh Branch Dept Elect Engn Tehran Iran

The issues related to the optimal control of large-scale storage systems in electric power systems such as pumped storage (PS) plant have turned into vital challenges in the way of integrating renewable energy sources into power systems to provide reliable and economical electric energy. In this regard, this paper uses the direct power control strategy to model and simulate a variable-speed PS plant, which includes a doubly fed induction generator (DFIG). The active and the reactive power of the stator would be able to be controlled, separately. This approach has a better dynamic performance compared to other methods, while it would be quite simple to implement. But there are some shortfalls with this method, such as high ripple relating to the active power as well as reactive power together with the current harmonics. In this respect, the space vector modulation (SVM) is applied to eliminate these shortfalls. In the proposed control technique, including SVM, the dynamic performance of the studied DFIG unit is controlled using the proportional-integral (PI) controller. It should be noted that the teaching-learning-based optimization (TLBO) method is employed to tune the PI controller for controlling the DFIG system in the PS plant. Finally, in order to validate the performance of the suggested framework, a comparison is made between the results obtained by the TLBO and the ones reported by other optimization methods. The obtained results using the TLBO algorithm indicate better performance of the PI controller to reduce the ripples of the active and reactive power of the stator as well as the harmonic power.

关键词： Pumped-storage power plant Doubly fed induction generator Teaching-learning-based optimization Imperialist competitive algorithm particle swarm optimization algorithm

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Switchable Polarization Beam Splitter Based on GST-on-Silicon Waveguides

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2020年第2期12卷

作者： Zhang, Bohao Chen, Weiwei Wang, Pengjun Dai, Shixun Jiang, Weifeng Liang, Wei Lu, Hao Ding, Jian Li, Jun Li, Yan Fu, Qiang Dai, Tingge Yu, Hui Yang, Jianyi Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Cyrus Tang Ctr Sensor Mat & Applicat Hangzhou 310027 Peoples R China Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Nanjing 210023 Peoples R China Ningbo Univ Lab Infrared Mat & Devices Res Inst Adv Technol Ningbo 315211 Peoples R China

A switchable polarization beam splitter utilizing GST-on-silicon waveguides is introduced and comprehensively investigated in this paper. Based on the phase matching and mode hybridization, structural parameters of the presented device are optimized by using the particle swarm optimization algorithm and finite difference time domain method, so that the output fundamental transverse-electric (TE) and transverse-magnetic (TM) modes can be switched by controlling the phase state of GST and the device exhibits broad bandwidth, low crosstalk, and high polarization extinction ratio. For the designed device with the amorphous GST, the crosstalk is lower than dB and the polarization extinction ratio is larger than 21.35dB over the C-band. In the case of the crystalline GST, within the C-band, the crosstalk of less than 27.37dB and the polarization extinction ratio of greater than 21.37dB are achieved.

关键词： Switchable polarization beam splitter GST-on-silicon waveguide particle swarm optimization algorithm

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V-REP-based navigation of automated wheeled robot between obstacles using PSO-tuned feedforward neural network

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2020年第4期7卷 427-434页

作者： Pandey, Anish Panwar, Vikas Singh Hasan, Md Ehtesham Parhi, Dayal R. KIIT Deemed Univ Bhubaneswar India NIT Rourkela Rourkela Odisha India

This paper describes the navigation of an automated Pioneer P3-DX wheeled robot between obstacles using particle swarm optimization (PSO) algorithm tuned feedforward neural network (FNN). This PSO algorithm minimizes the mean square error between the actual and predicted values of the FNN. In this work, 2 separate DC motors and 16 ultrasonic sensors have been used for making differential drive steering angle and for collecting the distance from obstacles, respectively. The proposed without tuned FNN and PSO-tuned FNN receives obstacle's distance as inputs form ultrasonic sensors and control the steering angle of a differential drive of automated Pioneer P3-DX wheeled robot as output. We have compared the results between without tuned FNN and PSO-tuned FNN, and it has been found that PSO-tuned FNN gives a better trajectory and takes less distance to reach the target. Virtual Robot Experimentation Platform software has been used to design the real-time simulation results. A comparative study between without tuned FNN and PSO-tuned FNN verifies the effectiveness of PSO-tuned FNN for automated Pioneer P3-DX wheeled robot navigation. Also, we have compared this winner PSO-tuned FNN to the previously developed PSO-optimized Fuzzy Logic Controller navigational technique to show the authenticity and real-time implementation of PSO-tuned FNN.

关键词： navigation automated Pioneer P3-DX wheeled robot feedforward neural network particle swarm optimization algorithm Virtual Robot Experimentation Platform

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Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions

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RENEWABLE ENERGY 2020年 145卷 2034-2045页

作者： Fan, Junliang Wu, Lifeng Ma, Xin Zhou, Hanmi Zhang, Fucang Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Henan Univ Sci & Technol Coll Agr Engn Luoyang 471003 Peoples R China

Increasing air pollutants significantly affect the proportion of diffuse (R-d) to global (R-s) solar radiation. This study proposed three new hybrid support vector machines (SVM) with particle swarm optimization algorithm (SVM-PSO), bat algorithm (SVM-BAT) and whale optimization algorithm (SVM-WOA) for predicting daily R-d in air-polluted regions. These models were further compared to standalone SVM, multivariate adaptive regression spline (MARS) and extreme gradient boosting (XGBoost) models. The results showed that models with suspended particulate matter with aerodynamic diameter smaller than 2.5 mu m and 10 mu m (PM2.5 and PM10) and ozone (O-3) produced more accurate daily R-d estimates than those without air pollution parameters, with average relative decreases in root mean square deviation (RMSD) of 11.1%, 10.0% and 10.4% for sunshine duration-based, R-s-based and combined models, respectively. SVM showed better accuracy than XGBoost and MARS. However, compared to SVM, SVM-BAT further enhanced the prediction accuracy and convergence rate in daily R-d modeling, followed by SVM-WOA and SVM-PSO, with relative decreases in RMSD of 2.9%-5.6%, 1.9%-4.9% and 1.1%-3.3%, respectively. The results highlighted the significance of incorporating air pollutants for more accurate estimation of daily R-d in air-polluted regions. Heuristic algorithms, especially BAT, are highly recommended for improving performance of standalone machine learning models. (c) 2019 Elsevier Ltd. All rights reserved.

关键词： Air pollution Support vector machines Extreme gradient boosting particle swarm optimization algorithm Bat algorithm Whale optimization algorithm

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Time series prediction for output of multi-region solar power plants

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APPLIED ENERGY 2020年 257卷

作者： Zheng, Jianqin Zhang, Haoran Dai, Yuanhao Wang, Bohong Zheng, Taicheng Liao, Qi Liang, Yongtu Zhang, Fengwei Song, Xuan China Univ Petr Natl Engn Lab Pipeline Safety MOE Key Lab Petr Engn Beijing Key Lab Urban Oil & Gas Distribut Technol Fuxue Rd 18 Beijing 102249 Peoples R China Univ Tokyo Ctr Spatial Informat Sci 5-1-5 Kashiwanoha Kashiwa Chiba 2778568 Japan Wayne State Univ Dept Comp Sci Comp & Syst Secur Lab Detroit MI 48202 USA

Solar energy, as a renewable and clean energy source, has developed rapidly and has attracted considerable attention. The integration of solar energy into a power grid requires precise prediction of the power output of solar plants. Accurate solar power output prediction can promote power dispatch, maintaining the normal operation of power systems. However, research on multi-region solar power is still rare. In this study, long shortterm memory and a particle swarm optimization algorithm contribute to solar power prediction considering time series. In order to improve the prediction accuracy, particle swarm optimization is used to optimize the parameters of the long short-term memory model. In addition, different long short-term memory structures are illustrated to determine the final prediction model with sensitivity analysis. Experiments are carried out to verify the effectiveness of the proposed method. The mean absolute error and root mean square error of the proposed method is the smallest among the prediction methods in four cases containing different seasons. In terms of prediction accuracy, results indicate that the proposed prediction model outperforms basic long short-term memory, artificial neural network, and extreme gradient boosting.

关键词： Solar power output prediction Time series Multi-region Long short-term memory particle swarm optimization algorithm Sensitivity analysis

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Bi-level programming model for resource-shared parking lots allocation

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2020年第7期12卷 501-511页

作者： Duan, Manzhen Wu, Dayong Liu, Hongchao North China Univ Sci & Technol Dept Civil & Architectural Engn Tangshan Peoples R China Texas Tech Univ Dept Civil Environm & Construct Engn Lubbock TX 79409 USA

The paper proposed a Personalize Parking Guidance Service(PPGS), in which a bi-level programming model was built to describe the relationship between the Personalized Parking Guidance Information System and drivers. The upper-level of the model aimed to achieve the efficetive and balanced utilization of parking resonurces during peak time, while the lower-level model was to minimize the driver's walking distance after parking.A nested Particel swarm optimization algorithm was used to solve the proposed model. The simulation results of the model show that the peak congestion time has been reduced remarkably under the guidance of the proposed model. The Mean value of Unocuupied Parking Difference Index(MUPDI) curves trend to decline during the overall process. It means that within the acceptable walking distance, the proposed parking lots allocation model can effectively balance the utilization of parking resources shared in the service area and minimize walking distance as well.

关键词： Intelligent transportation system personalized parking guidance information system bi-level design particle swarm optimization algorithm parking lots allocation model

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Bi-level programming problem in the supply chain and its solution algorithm

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SOFT COMPUTING 2020年第4期24卷 2703-2714页

作者： Luo, Haiyan Liu, Linzhong Yang, Xun Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China Lanzhou Jiaotong Univ Sch Econ & Management Lanzhou 730070 Peoples R China

Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where the upper level of a hierarchy may have his objective function and decision space partly determined by other levels. In addition, each planner's control instruments may allow him to influence the policies at other levels and thereby to improve his own objective function. As a tool, bi-level programming is applied for modeling decentralized decisions in which two decision makers make decisions successively. In this paper, we specifically address bi-level decision-making problems with budget constraint as an attractive feature in the context of enterprise-wide supply chain. We first describe the typical bi-level linear programming problem (BLLPP) and its optimal solution to the penalty function problem, and then, a cooperative decision-making problem in supply chain is modeled as BLLPP. A particle swarm optimization-based computational algorithm is designed to solve the problem, and the numerical example is presented to illustrate the proposed framework.

关键词： Decentralized supply chain Bi-level linear programming Budget constraint particle swarm optimization algorithm

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particle swarm optimization and Cuckoo Search Paralleled algorithm 3

Particle Swarm Optimization and Cuckoo Search Paralleled Alg...

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3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Yang Xiaodong Cai Zefan Shunde Polytech Elect & Informat Engn Deptartment Shunde Peoples R China

ISBN: (纸本)9781509063529

particle swarm optimization algorithm and cuckoo search algorithm both are bionic swarm optimization algorithms, which are simple and convenient. They have been applied to many fields. However, the algorithms have obvious disadvantages. When they are applied to complex optimization problems, they cannot obtain the optimal solutions, so some measures must be adopted in order to improve their global search ability. In this paper, particle swarm optimization algorithm and cuckoo search algorithm evolve in parallel. At the end of each generation, the better solution of the two algorithms is selected as the global optimal solution. The simulation results show that the paralleled algorithm absorbs the advantages of the two algorithms, improves the global search ability and the average convergence speed, and enhances the robustness of the algorithm. The new algorithm is able to solve complex optimization problems more efficiently.

关键词： cuckoo search algorithm particle swarm optimization algorithm paralleled algorithm

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