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algorithms for Smooth, Safe and Quick Routing on Sensor-Equipped Grid Networks

SENSORS 2021年第24期21卷 8188-8188页

作者： Andreatta, Giovanni De Francesco, Carla De Giovanni, Luigi Univ Padua Dipartimento Matemat Tullio Levi Civita I-35122 Padua Italy

Automation plays an important role in modern transportation and handling systems, e.g., to control the routes of aircraft and ground service equipment in airport aprons, automated guided vehicles in port terminals or in public transportation, handling robots in automated factories, drones in warehouse picking operations, etc. Information technology provides hardware and software (e.g., collision detection sensors, routing and collision avoidance logic) that contribute to safe and efficient operations, with relevant social benefits in terms of improved system performance and reduced accident rates. In this context, we address the design of efficient collision-free routes in a minimum-size routing network. We consider a grid and a set of vehicles, each moving from the bottom of the origin column to the top of the destination column. Smooth nonstop paths are required, without collisions nor deviations from shortest paths, and we investigate the minimum number of horizontal lanes allowing for such routing. The problem is known as fleet quickest routing problem on grids. We propose a mathematical formulation solved, for small instances, through standard solvers. For larger instances, we devise heuristics that, based on known combinatorial properties, define priorities, and design collision-free routes. Experiments on random instances show that our algorithms are able to quickly provide good quality solutions.

关键词： automated transportation network collision-free routing grid network optimization algorithm integer linear programming heuristics

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Parameters Identification of Proton Exchange Membrane Fuel Cell Model Based on the Lightning Search algorithm

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ENERGIES 2022年第21期15卷 7893页

作者： Mohanty, Banaja Elavarasan, Rajvikram Madurai Hasanien, Hany M. Devaraj, Elangovan Turky, Rania A. Pugazhendhi, Rishi Veer Surendra Sai Univ Technol VSSUT Dept Elect Engn Burla 768018 India Univ Queensland Sch Informat Technol & Elect Engn St Lucia Qld 4072 Australia Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Vellore Inst Technol VIT TIFAC CORE Vellore 632014 Tamil Nadu India Vellore Inst Technol VIT Sch Elect Engn Vellore 632014 Tamil Nadu India Future Univ Egypt Fac Engn & Technol Elect Engn Dept Cairo 11835 Egypt Nestl Private Ltd Res & Dev Div Power & Energy Chennai 600091 Tamil Nadu India

The fuel cell is vital in electrical distribution networks as a distributed generation in today's world. A precise model of a fuel cell is extensively required as it rigorously affects the simulation studies' transient and dynamic analyses of the fuel cell. This appears in several microgrids and smart grid systems. This paper introduces a novel attempt to optimally determine all unknown factors of the polymer exchange membrane (PEM) fuel cell model using a meta-heuristic algorithm termed the Lightning search algorithm (LSA). In this model, the current-voltage relationship is heavily nonlinear, including several unknown factors because of the shortage of fuel cell data from the manufacturer's side. This issue can be treated as an optimization problem, and LSA is applied to detect its ability to solve this problem accurately. The objective function is the sum of the squared error between the estimated output voltage and the measured output voltage of the fuel cell. The constraints of the optimization problem involve the factors range (lower and upper limit). The LSA is utilized in minimizing the objective function. The effectiveness of the LSA-PEM fuel cell model is extensively verified using the simulation results performed under different operating conditions. The simulation results of the proposed model are compared with the measured results of three commercial fuel cells, such as Ballard Mark V 5 kW, BCS 500 W and Nedstack PS6 6 kW, to obtain a realistic study. The results of the proposed algorithm are also compared with different optimized models to validate the model and, further, to determine where LSA stands in terms of precision. In this regard, the proposed model can yield a lower SSE by more than 5% in some cases and high performance of the LSA-PEMFC model. With the results obtained, it can be concluded that LSA prevails as a potential optimization algorithm to develop a precise PEM fuel cell model.

关键词： parameters identification lightning search algorithm (LSA) PEM fuel cell optimization algorithm fuel cell model

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Optimizing relative root-zone water depletion thresholds to maximize yield and water productivity of winter wheat using AquaCrop

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AGRICULTURAL WATER MANAGEMENT 2023年第1期286卷

作者： Zhang, Ting Zuo, Qiang Ma, Ning Shi, Jianchu Fan, Yuchuan Wu, Xun Wang, Lichun Xue, Xuzhang Ben -Gal, Alon Minist Educ Key Lab Plant Soil Interact Beijing 100193 Peoples R China Minist Agr Key Lab Arable Land Conservat North China Beijing 100193 Peoples R China China Agr Univ Coll Land Sci & Technol Minist Educ Beijing 100193 Peoples R China Pingliang Acad Agr Sci Pingliang Gansu Peoples R China Univ Florida Everglades Res & Educ Ctr 3200 Canal St S Belle Glade FL 33430 USA Natl Res Ctr Intelligent Equipment Agr Beijing 100097 Peoples R China Gilat Res Ctr Agr Res Org Volcani Inst Soil Water & Environm Sci Mobile Post Negev IL-85280 Ramat Gan Israel China Agr Univ Coll Land Sci & Technol Beijing 100193 Peoples R China

Determination of relative root-zone water depletion (RRWD) thresholds to trigger irrigation is crucial to create optimal irrigation schedules targeting maximum yield and/or water productivity with limited water supply for a crop. In this study, a numerical procedure to determine RRWD thresholds was developed through coupling AquaCrop software with genetic-simplex algorithms. Using a two-year field lysimetric experiment for winter wheat conducted in the North China Plain (NCP), AquaCrop adequately simulated canopy cover, final above-ground biomass, grain yield, seasonal evapotranspiration, and soil water storage, with the normalized root mean squared error (NRMSE) smaller than 15 % and determination coefficient (R2) larger than 0.84. The global op-timum range of RRWD thresholds was preliminarily determined using the genetic algorithm, and subsequently final RRWD thresholds were optimized by fine tuning using the simplex algorithm. The RRWD threshold com-binations (composed of the RRWD thresholds to trigger different sequential irrigation events) for varying number of irrigation events (i.e.1-4) were optimized based on 39 years of historical meteorological data, and the effects of climate change on the optimal crop yield (Ya, opt), water productivity (WPopt), and the combinations of optimized RRWD threshold (RRWDopt) were investigated. The results indicated that both Ya , opt and WPopt generally increased with time showing a tendency of gradually elevated annual CO2 concentration and seasonal average effective temperature. Irrespective of the number of irrigation events during the winter wheat growing season, the differences of RRWDopt for different combinations of irrigation sequence and event in the same kind of hydrological year were relatively small, with a coefficient of variation consistently less than 23 % and a mean of 8 %. When combinations of mean RRWDopt were applied into AquaCrop to trigger irrigation for winter wheat in various hydrological years, the sim

关键词： Crop model optimization algorithm Irrigation scheduling Evapotranspiration Climate change

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Prediction of stock market index based on ISSA-BP neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 204卷

作者： Liu, Xin Guo, Junhong Wang, Hua Zhang, Fan Shandong Technol & Business Univ Sch Stat Yantai 264005 Peoples R China Ludong Univ Sch Informat & Electe Engn Yantai 264005 Peoples R China Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China Shandong Future Intelligent Financial Engn Lab Yantai 264005 Peoples R China

Stock market index forecasting is a very tempting topic. Appropriate analysis of such a topic will provide valuable insights for investors, traders and policymakers in the appealing stock markets. In this article, an improved sparrow search algorithm is proposed to optimize the initial weight and threshold of BPNN, and is applied to the forecasting of stock market indices. The contributions of this article are mainly in the following aspects: (1) Use the sine chaos model to initialize the population;(2) In the position update formula of the discoverer, the global optimal solution of the previous generation is added, and at the same time, adaptive weights are added to coordinate the capabilities of local mining and global exploration. (3) Combining the Gaussian mutation operator and the reverse learning strategy, perturbation mutation is carried out at the optimal position to generate new solutions. (4) Use the improved sparrow algorithm (ISSA) to optimize the initial weight and threshold of BP neural network. We evaluate the performance of the proposed model on four datasets, which are SSE, SZSE, SP500 and DJI. Two types of models are used to compared with the proposed mode. One of them is the use of swarm intelligence optimization algorithms to optimize BPNN, including GA-BP, PSO-BP, ACO-BP, GWO-BP, CS-BP and SSA-BP. The others are some deep learning models. The experimental results show that the three strategies proposed in this paper greatly improve the optimization ability of the sparrow search algorithm. The ISSA-BP model achieve great success in the short-term prediction of stock prices, which can help investors predict market trends and find the right time to trade. In addition, for policymakers, the rationality of policies can be assessed through market trends, so as to better promote the development of the stock market.

关键词： Stock market Sparrow search algorithm BP neural network optimization algorithm

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Modeling and optimizing in vitro seed germination of industrial hemp (Cannabis sativa L.)

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INDUSTRIAL CROPS AND PRODUCTS 2021年 170卷 113753-113753页

作者： Hesami, Mohsen Pepe, Marco Monthony, Adrian Scott Baiton, Austin Jones, Andrew Maxwell Phineas Univ Guelph Dept Plant Agr Guelph ON N1G 2W1 Canada

In vitro seed germination of cannabis as the first physiological stage in the plant life cycle is not only important for studying factors affecting cultivation conditions but also crucial for obtaining juvenile tissue as a potential explant for different in vitro procedures. On the other hand, in vitro seed germination is a multi-variable biological process that can be influenced by genetic (genotype) and physical factors (medium composition and environ-mental conditions). Therefore, a powerful mathematical methodology such as artificial neural networks (ANNs) is well suited to analyze the data and optimize the conditions this complex system. The current study was aimed to evaluate the effect of different types and concentrations of carbohydrate sources (sucrose and glucose) as well as different strengths of DKW (Driver and Kuniyaki Walnut) and mMS (Murashige and Skoog Medium, Van der Salm modification) media on seed germination indices as well as morphological features of in vitro-grown cannabis seedlings by using three ANNs including multilayer perceptron (MLP), radial basis function (RBF), and generalized regression neural network (GRNN). The GRNN model displayed higher predictive accuracy (r(2)>0.70) in both training and testing sets for all germination indices and morphological traits in comparison to RBF or MLP. Moreover, non-dominated sorting genetic algorithm-II (NSGA-II) was subjected to the GRNN to find the optimal type and level of media and carbohydrate source for obtaining the best seed germination indices (germination rate and mean germination time). According to the optimization process, 0.43 strength mMS medium supplemented with 2.3 % sucrose would result in the best outcomes. This result showed that a moderate level of salts existing in culture media (0.43 strength of mMS medium) supplemented with a moderate level of sucrose (2.3 %) can improve in vitro seed germination of hemp. The results of a validation experiment revealed that there was a neglig

关键词： Artificial intelligence Carbohydrate sources Culture medium Machine learning algorithm optimization algorithm Plant tissue culture

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DSGWO: An improved grey wolf optimizer with diversity enhanced strategy based on group-stage competition and balance mechanisms

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KNOWLEDGE-BASED SYSTEMS 2022年第0期250卷

作者： Jiang, Jianhua Zhao, Ziying Liu, Yutong Li, Weihua Wang, Huan Jilin Univ Finance & Econ Jilin Prov Key Lab Fintech Changchun 130117 Peoples R China Jilin Agr Univ Res Ctr Corpus Applicat Changchun 130118 Peoples R China Auckland Univ Technol Sch Engn Comp & Math Sci 55 Wellesley St E Auckland 1010 New Zealand

Grey wolf optimizer (GWO) is a popular algorithm in the field of meta-heuristics with fast convergence speed and strong exploitation ability for solving different types of optimization problems. However, there are two problems presented in the optimization process of GWO. Problem (1): GWO algorithm uses three leading wolves to direct the population for search, resulting in poor population diversity. Problem (2): Individuals selected in the optimization process of GWO have the current optimal fitness value, resulting in poor global search capabilities of the algorithm, and the population may tend to converge prematurely and fall into local optimum. In order to solve the above problems, this paper proposes the Diversity enhanced Strategy based Grey Wolf Optimizer (DSGWO), which combines two mechanisms to improve the performance of the GWO algorithm, including the group-stage competition mechanism and the exploration-exploitation balance mechanism. The experimental results are evaluated by using IEEE CEC 2014 benchmark functions and two engineering problems. The Wilcoxon rank-sum test is also conducted to measure the performance of DSGWO. The results demonstrate that the DSGWO algorithm can efficiently obtain the optimal solution of the dataset used in this paper, preserve more population diversity, and enhance the global search capability. In addition, through the application of two engineering problems, it is verified that DSGWO is suitable for solving engineering design problems. (C) 2022 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Grey wolf optimizer GWO Continuous optimization problem

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Jaya-ICSM: A rapid inverse method driven by monitoring data for concrete-faced rockfill dams static displacement simulation

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ADVANCED ENGINEERING INFORMATICS 2023年第1期57卷

作者： Li, Yichuan Pang, Rui Xu, Bin Zhou, Yang Dalian Univ Technol Fac Infrastructure Engn Sch Hydraul Engn Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China Dalian Univ Technol DUT Sch Civil Engn 2 Linggong RdHigh Tech Zone Dalian 116024 Peoples R China Dalian Univ Technol DUT State Key Lab Coastal & Offshore Engn 2 Linggong RdHigh Tech Zone Dalian 116024 Peoples R China

This paper presents a rapid static parameter inverse method for concrete-faced rockfill dams based on displacement data using an improved cloud surrogate model and the Jaya optimization algorithm. The improved cloud surrogate model (ICSM), which evolved from the uncertainty cloud theory, is used rapidly to establish a nonlinear relationship between static parameters and displacement. The Jaya optimization algorithm, which is proficient in recognizing the extreme value of the objective function, is selected for parameter identification. To verify the performance of the proposed method, an inverse analysis of two numerical models and a real engineering project is considered, and a comparative study with the BP neural network as the surrogate model is conducted. Study results demonstrate that ICSM has an extremely high prediction accuracy, with a mean relative error that is always under 1 %. The prediction accuracy does not increase too much even under complex nonlinear conditions or when dealing with sparse training data. Moreover, Jaya-ICSM maintains high computational efficiency throughout the entire inverse analysis, saving more than 98 % of the time compared to another inverse method and proving to be much better than direct inversion based on the finite element method. This enables real-time inverse analysis and state feedback of complex engineering. The results also illustrate that the Jaya-ICSM is promising in parameter recognition and superior in supporting the displacement evaluation of earth-rock dams. Therefore, the proposed method can be an effective tool for engineers to evaluate the safety of earth-rockfill dams and ensure their long-term stability.

关键词： Inverse analysis Concrete -faced rockfill dam Cloud surrogate model optimization algorithm Displacement simulation

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Comparison between two different efficiency improvement approaches for an obsolete small-size multi-generation plant

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Energy Procedia 2018年 148卷 583-590页

作者： Piero Danti Sandro Magnani Giovanni Cauchi Yanmar R&D Europe Firenze 50125 Italy

In the latest years, consumer’s needs of reducing energy cost raised a general interest on plants upgrade and optimization; currently there are many approaches to deal with this tendency. Most intuitive way of proceeding is embodied by a revamping solution: often, above all in Italian landscape, small-medium size energy plants run outdated equipment whose performance have considerable room for improvement. Because of that, during last 15 years, many Energy Service Companies (ESCos) have been burgeoning providing a broad range of energy solutions. One of these solutions is represented by trigeneration systems installation, combining CHP and absorption chiller; by means of this a decrease of energy supply costs and CO 2 emissions is achieved. Another philosophy to face the problem is the optimization of generators functioning using ad hoc control logics. This analysis aims to draw a comparison, from an economical point of view, between two different optimization strategies applied to the same small size energy plant where a cogeneration system is installed: the former strategy will focus on plant upgrade, substituting obsolete devices with higher performance ones, the latter will solely lean on an EMS running an optimization algorithm. Results will be presented and analysed in terms of daily costs.

关键词： CHP EHP EMS optimization algorithm

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A Modified Newton-Raphson Search for the Model-adaptive Identification of Delays

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IFAC Proceedings Volumes 1979年第8期12卷 1279-1286页

作者： H. Burkhardt H. Moll Institut für Mess- und Regelungstechnik University of Karlsruhe D-7500 Karlsruhe F.R.G.

The paper presents a new, modified Newton-Raphson technique for the fast, model-adaptive identification of delays between two unknown stochastic or deterministic signals. The method combines properties of the gradient, as well as the Newton-Raphson technique. In contrast to the linear convergence of gradient techniques, it has in this special application the same cubic convergence rate as the Newton-Raphson method combined, however, with the larger range of stability of the gradient method. Further properties are a nearly uniform settling time, independent of the bandwidth and the amplitudes of the measured signals, and under certain assumptions a monotone decreasing error. The algorithm leads straightforwardly to a rather simple, discrete hardware implementation, adjusting a parametric model. Possible applications are in the field of velocity and distance measurement. Extensive simulations for the proposed scheme together with results of known methods are presented.

关键词： Identification Newton-Raphson correlation optimization algorithm stochastic algorithm speed measurement distance measurement tracking system

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Optimized inversion method for thermal parameters of concrete dam under the insulated condition

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期126卷

作者： Bo, Chen Qingyi, Wang Weinan, Chen Hao, Gu Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Water Conservancy & Hydroelect Power Co Ltd Zhejiang Design Inst Hangzhou 310000 Peoples R China

It is crucial to study how thermal characteristics of concrete dam change over time, especially in cold regions, in order to guarantee long-term safety of the engineering projects. In this manuscript, an inverse analysis method by coupling the numerical simulation model and dam safety monitoring data, is proposed to solve the problem that true thermal parameters of the whole structure cannot be obtained by laboratory experiments or field tests of single points. Moreover, a numerical equivalence method of the dam and its insulation layer is introduced, in order to overcome the problem that the thickness of the insulation layer is much smaller than that of the concrete dam, resulting in low accuracy of the simulation model. Thirdly, the particle swarm optimization is introduced, in order to solve the ill-posed problems occurring during the solution process of multi-parameter inverse analysis. Considering the problem that the original particle swarm algorithm is easy to fall into local optimum and stuck edge, this study improves particles velocity and inertia weights. Benchmark function tests are applied to show the performance compared to several typical optimized algorithms. Numerical simulation and engineering verifi-cation are used to demonstrate the rationality and feasibility of the proposed method. The cases show that the residual box diagram of two typical measuring points calculated by inversion value is smaller than the median line and mean point calculated by design value. Moreover, the change law of the temperature field of the finite element calculation using the inversion values is closer to the actual measured value than that of the design value. The results indicate that the multi-parameter inverse method is feasible in insulated dam engineering. In addition, the result of the improved PSO optimization converges faster and has a higher fitness value than that of the original algorithm. It suggests that the improved PSO algorithm for thermal parameters i

关键词： Cold region Concrete dam Dam insulation Equivalent heat release coefficient optimization algorithm

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