The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become i...In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become indistinguishable as the curse of dimensionality increases in the objective space and the accumulation of surrogate approximated errors.Therefore,in this paper,each objective function is modeled using a radial basis function approach,and the optimal solution set of the surrogate model is located by the multi⁃objective evolutionary algorithm of strengthened dominance relation.The original objective function values of the true evaluations are converted to two indicator values,and then the surrogate models are set up for the two performance indicators.Finally,an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set.The algorithm is contrasted against several advanced surrogate⁃assisted evolutionary algorithms on two suites of test cases,and the experimental findings prove that the approach is competitive in solving expensive many⁃objective optimization problems.展开更多
A class of interactive multi objective decision making method by means of evaluation criterion is proposed for problems with linear value function,in which case,the decision maker(DM) usually has only unwhole infor...A class of interactive multi objective decision making method by means of evaluation criterion is proposed for problems with linear value function,in which case,the decision maker(DM) usually has only unwhole information of weights for objectives. The concept of fault measure of the evaluation criterion is proposed to measure the deviation of the evaluation criterion from the DMs preference structure.The approach to obtain an upper boundary of fault measure of an evaluation criterion,and the approach to modify the evaluation criterion to be one with smaller fault measure,and the approach to obtain a pre optimized objective set by evaluation criterion with certain fault measure are also proposed.展开更多
To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as...To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as an object. A changing weight value method is put forward and a selection formula is modified. Some experiments were implemented on an AUV, TwinBurger. The results shows that this method is effective and feasible.展开更多
In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （S...In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.展开更多
To performance efficient searching for an operator-supervised mobile robot, a multiple objectives route planning approach is proposed considering timeliness and path cost. An improved fitness function for route planni...To performance efficient searching for an operator-supervised mobile robot, a multiple objectives route planning approach is proposed considering timeliness and path cost. An improved fitness function for route planning is proposed based on the multi-objective genetic algorithm （MOGA） for multiple objectives traveling salesman problem （MOTSP）. Then, the path between two route nodes is generated based on the heuristic path planning method A ＊. A simplified timeliness function for route nodes is proposed to represent the timeliness of each node. Based on the proposed timeliness function, experiments are conducted using the proposed two-stage planning method. The experimental results show that the proposed MOGA with improved fitness function can perform the searching function well when the timeliness of the searching task needs to be taken into consideration.展开更多
Besides economics and controllability, waste minimization has now become an objective in designing chemical processes, and usually leads to high costs of investment and operation. An attempt was made to minimize waste...Besides economics and controllability, waste minimization has now become an objective in designing chemical processes, and usually leads to high costs of investment and operation. An attempt was made to minimize waste discharged from chemical reaction processes during the design and modification process while the operation conditions were also optimized to meet the requirements of technology and economics. Multiobjectives decision nonlinear programming (NLP) was employed to optimize the operation conditions of a chemical reaction process and reduce waste. A modeling language package-SPEEDUP was used to simulate the process. This paper presents a case study of the benzene production process. The flowsheet factors affecting the economics and waste generation were examined. Constraints were imposed to reduce the number of objectives and carry out optimal calculations easily. After comparisons of all possible solutions, best-compromise approach was applied to meet technological requirements and minimize waste.展开更多
Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty per...Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world population.The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains.This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques.Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy.There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy cultivation.The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)Algorithm with Genetic Algorithm(GA)and Multi Objective Particle Swarm Optimization Algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database.The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database.Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm.The reason for improving the growth of paddy was identified using the output of the Neural Network.Performance metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed algorithm.Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.展开更多
This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Auto...This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Automata （MDFA） is defined. MDFA allows the representation of the feasible solutions space of combinatorial problems. Second, it is defined and implemented a metaheuritic based on MDFA theory. It is named Metaheuristic of Deterministic Swapping （MODS）. MODS is a local search strategy that works using a MDFA. Due to this, MODS never take into account unfeasible solutions. Hence, it is not necessary to verify the problem constraints for a new solution found. Lastly, MODS is tested using well know instances of the Bi-Objective Traveling Salesman Problem （TSP） from TSPLIB. Its results were compared with eight Ant Colony inspired algorithms and two Genetic algorithms taken from the specialized literature. The comparison was made using metrics such as Spacing, Generational Distance, Inverse Generational Distance and No-Dominated Generation Vectors. In every case, the MODS results on the metrics were always better and in some of those cases, the superiority was 100%.展开更多
To solve the emerging complex optimization problems, multi objectiveoptimization algorithms are needed. By introducing the surrogate model forapproximate fitness calculation, the multi objective firefly algorithm with...To solve the emerging complex optimization problems, multi objectiveoptimization algorithms are needed. By introducing the surrogate model forapproximate fitness calculation, the multi objective firefly algorithm with surrogatemodel (MOFA-SM) is proposed in this paper. Firstly, the population wasinitialized according to the chaotic mapping. Secondly, the external archive wasconstructed based on the preference sorting, with the lightweight clustering pruningstrategy. In the process of evolution, the elite solutions selected from archivewere used to guide the movement to search optimal solutions. Simulation resultsshow that the proposed algorithm can achieve better performance in terms ofconvergence iteration and stability.展开更多
This paper uses the bibliometric analysis software CiteSpace to examine the current status and evolution of cold-chain logistics vehicle routing problems(CCVRP).7381 relevant articles published in the Web of Science c...This paper uses the bibliometric analysis software CiteSpace to examine the current status and evolution of cold-chain logistics vehicle routing problems(CCVRP).7381 relevant articles published in the Web of Science core collection from 2008 to 2024 were analyzed,an in-depth understanding of the publication trends and category distribution were gained.Subsequently,CiteSpace was used to create a scientific knowledge graph and perform visualization analysis.The analysis includes collaboration among authors,countries,and institutions;co-citation analysis of authors,journals,and references;citation burst detection of keywords;and co-citation cluster analysis of references.Based on a deep understanding of current research hotspots,an in-depth discussion of existing research was conducted from three perspectives:optimization objectives,distribution scenarios,and solution algorithms.The results show that CCVRP involves complex factors such as temperature requirements,time window constraints,and multi-objective optimization.These intricate constraints are causing research to become increasingly interdisciplinary and comprehensive.The evolution of hot topics shows that the research directions span multiple fields,from algorithm design to logistics management.This review helps researchers better understand the history,current status,and future development directions of CCVRP research,and provides valuable references and inspiration for academia and practice.展开更多
Helical axial-flow multiphase pumps are core devices for developing deep-sea oil and gas,but the complex two-phase flow inside such pumps affects their transport stability.As reported here,for enhanced flow characteri...Helical axial-flow multiphase pumps are core devices for developing deep-sea oil and gas,but the complex two-phase flow inside such pumps affects their transport stability.As reported here,for enhanced flow characteristics,splitter blades were added at the noncoincidence area of the impeller tail by using a multi-objective optimization method,and their impact on the pump performance was explored in comparison with the original model.The results indicate that Gaussian process regression combined with particle swarm optimization accurately predicts the pump external characteristics.Under an inlet gas volume fraction of 30%,the optimized model increases the head coefficient by 22.2%without reducing efficiency.Although splitter blades enhance the impeller tail,they intensify gas-phase accumulation at the tail.The optimized model promotes more-uniform two-phase flow in the channels,with the axial velocity uniformity of the gas phase and liquid phase improving by 15.1%and 9.5%and the average velocity angle increasing by 12.3°and 8.3°under an inlet gas volume fraction of 30%.展开更多
The presently existing decision making method for problem of goal type, i.e. the goal programming, is popular to some extent. In this paper we analyzed the features of the problem and the method,based on which we foun...The presently existing decision making method for problem of goal type, i.e. the goal programming, is popular to some extent. In this paper we analyzed the features of the problem and the method,based on which we found some defects of the method and pointed out these defects. To overcome these defects we absorbed the spirit and exploited concepts of evaluation criterion and the fault measure of evaluation criterion. We proposed and applied a method with an evaluation criterion, after which we also p...展开更多
Current researches mainly focus on the investigations of the valve plate utilizing pressure relief grooves. However,air?release and cavitation can occur near the grooves. The valve plate utilizing damping holes show e...Current researches mainly focus on the investigations of the valve plate utilizing pressure relief grooves. However,air?release and cavitation can occur near the grooves. The valve plate utilizing damping holes show excellent perfor?mance in avoiding air?release and cavitation. This study aims to reduce the noise emitted from an axial piston pump using a novel valve plate utilizing damping holes. A dynamic pump model is developed,in which the fluid properties are carefully modeled to capture the phenomena of air release and cavitation. The causes of di erent noise sources are investigated using the model. A comprehensive parametric analysis is conducted to enhance the understanding of the e ects of the valve plate parameters on the noise sources. A multi?objective genetic algorithm optimization method is proposed to optimize the parameters of valve plate. The amplitudes of the swash plate moment and flow rates in the inlet and outlet ports are defined as the objective functions. The pressure overshoot and undershoot in the piston chamber are limited by properly constraining the highest and lowest pressure values. A comparison of the various noise sources between the original and optimized designs over a wide range of pressure levels shows that the noise sources are reduced at high pressures. The results of the sound pressure level measurements show that the optimized valve plate reduces the noise level by 1.6 d B(A) at the rated working condition. The proposed method is e ective in reducing the noise of axial piston pumps and contributes to the development of quieter axial piston machines.展开更多
Use of hot-stamped high strength steels （HSHSS） not only reduces the vehicle weight, but also improves the crash safety, therefore more and more mentioned steels are used to produce automobile parts. However, there ...Use of hot-stamped high strength steels （HSHSS） not only reduces the vehicle weight, but also improves the crash safety, therefore more and more mentioned steels are used to produce automobile parts. However, there are several problems especially the low ductility and toughness, which have restricted the application of HSHSS in automobile body. Suitable process parameters are very crucial to improve strength and toughness. In order to study the effect of austenization temperature, soaking time and start deformation temperature on strength and toughness of boron steel 22MnB5, an L9 （34） orthogonal experiment which was analyzed by means of comprehensive evaluation was carried out based on Kahn tear method to obtain the value of fracture toughness. The results indicate that the ex- cellent formability, high strength and toughness of boron steel 22MnB5 with 1.6 mm in thickness are obtained when the austenization temperature is in the range of 920- 950 ℃, the soaking time is 1 min and the start deformation temperature is in the range of 650- 700 ℃. The optimal parameters were used for typical hot stamping structural parts tests. Properties of samples such as tear strength, unit initiation energy and ratio of strength to toughness （RST） were improved by 10.91%, 20.32% and 22.17%, respectively. Toughness was increased substantially on the basis of a small decrease of strength.展开更多
High-speed locomotives are prone to carbody or bogie hunting when the wheel-rail contact conicity is excessively low or high.This can cause negative impacts on vehicle dynamics performance.This study presents four typ...High-speed locomotives are prone to carbody or bogie hunting when the wheel-rail contact conicity is excessively low or high.This can cause negative impacts on vehicle dynamics performance.This study presents four types of typical yaw damper layouts for a high-speed locomotive(Bo-Bo)and compares,by using the multi-objective optimization method,the influences of those layouts on the lateral dynamics performance of the locomotive;the linear stability indexes under lowconicity and high-conicity conditions are selected as optimization objectives.Furthermore,the radial basis function-based highdimensional model representation(RBF-HDMR)method is used to conduct a global sensitivity analysis(GSA)between key suspension parameters and the lateral dynamics performance of the locomotive,including the lateral ride comfort on straight tracks under the low-conicity condition,and also the operational safety on curved tracks.It is concluded that the layout of yaw dampers has a considerable impact on low-conicity stability and lateral ride comfort but has little influence on curving performance.There is also an important finding that only when the locomotive adopts the layout with opening outward,the difference in lateral ride comfort between the front and rear ends of the carbody can be eliminated by adjusting the lateral installation angle of the yaw dampers.Finally,force analysis and modal analysis methods are adopted to explain the influence mechanism of yaw damper layouts on the lateral stability and differences in lateral ride comfort between the front and rear ends of the carbody.展开更多
A mathematical model of friction coefficient was proposed for the roll force calculation of hot-rolled strips. The online numerical solving method of the roll force calculation formula based on the proposed friction m...A mathematical model of friction coefficient was proposed for the roll force calculation of hot-rolled strips. The online numerical solving method of the roll force calculation formula based on the proposed friction model was developed and illustrated by the practical calculation case. Then, the friction coefficient during hot strip rolling was estimated from the measured roll force by force model inversion. And then, the expression of friction model was pro posed by analyzing the calculation process of stress state coefficient, and the model parameters were determined by the shared parameter multi-model nonlinear optimization method. Finally, the industrial experiments demonstrated the feasibility and effectiveness of the related models. The accuracy of the new roll force model based on the built friction model was much higher than that of the traditional Sims model, and it could be applied in the online hot rolling process control.展开更多
Perfect combination of structural size parameters of the hydroforming billets is essential to obtain even wall thicknesses of the car beam. Finite element （ FE ） analysis on hydroforming car beam was carried out, a...Perfect combination of structural size parameters of the hydroforming billets is essential to obtain even wall thicknesses of the car beam. Finite element （ FE ） analysis on hydroforming car beam was carried out, and the results were optimized according to multiple quality objectives by the grey system theory. With bending angle, bending radius and hight difference along the axis direction as variables, orthogonal FE analyses were conducted and the minimum and maximum wall thicknes ses of the billets with different sizes were obtained. Taking the minimum and maximum wall thick nesses as two references, the correlation coefficient between the data for reference and those for comparison by the grey system theory reduced multi objectives to a single quality objective, and the average correlation level of every billet facilitated the optimization of size parameters for hydroform ing car beam. The trial production showed that the optimization approach satisfied the need of hy droforming car beams.展开更多
Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical images.The deep learning models show mor...Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical images.The deep learning models show more remarkable performance than the traditional methods for medical image processing tasks,such as skin cancer,colorectal cancer,brain tumour,cardiac disease,Breast cancer(BrC),and a few more.The manual diagnosis of medical issues always requires an expert and is also expensive.Therefore,developing some computer diagnosis techniques based on deep learning is essential.Breast cancer is the most frequently diagnosed cancer in females with a rapidly growing percentage.It is estimated that patients with BrC will rise to 70%in the next 20 years.If diagnosed at a later stage,the survival rate of patients with BrC is shallow.Hence,early detection is essential,increasing the survival rate to 50%.A new framework for BrC classification is presented that utilises deep learning and feature optimization.The significant steps of the presented framework include(i)hybrid contrast enhancement of acquired images,(ii)data augmentation to facilitate better learning of the Convolutional Neural Network(CNN)model,(iii)a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes,(iv)deep transfer learning based model training for feature extraction,(v)the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach,and(vi)optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning classifiers.The experiments of the proposed framework have been carried out using the most critical and publicly available dataset,such as CBISDDSM,and obtained the best accuracy of 94.5%along with improved computation time.The comparison depicts that the presented method surpasses the current state‐ofthe‐art approaches.展开更多
In view of the problem of power quality degradation of port distribution network after the large-scale application of shore power load,a method of power quality management of port distribution network is proposed.Base...In view of the problem of power quality degradation of port distribution network after the large-scale application of shore power load,a method of power quality management of port distribution network is proposed.Based on the objective function of the best power quality management effect and the smallest investment cost of the management device,the optimization model of power quality management in the distribution network after the large-scale application of large-capacity shore power is constructed.Based on the balance between the economic demand of distribution network resources optimization and power quality management capability,the power quality of distribution network is considered comprehensively.The proposed optimization algorithm for power quality management based on Matlab and OpenDSS is proposed and analyzed for port distribution networks.The simulation results show that the proposed optimizationmethod can maximize the power qualitymanagement capability of the port distribution network,and the proposed optimization algorithm has good convergence and global optimization finding capability.展开更多
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金Sponsored by Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(Grant No.2022L294)Taiyuan University of Science and Technology Scientific Research Initial Funding(Grant Nos.W2022018,W20242012)Foundamental Research Program of Shanxi Province(Grant No.202403021212170).
文摘In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become indistinguishable as the curse of dimensionality increases in the objective space and the accumulation of surrogate approximated errors.Therefore,in this paper,each objective function is modeled using a radial basis function approach,and the optimal solution set of the surrogate model is located by the multi⁃objective evolutionary algorithm of strengthened dominance relation.The original objective function values of the true evaluations are converted to two indicator values,and then the surrogate models are set up for the two performance indicators.Finally,an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set.The algorithm is contrasted against several advanced surrogate⁃assisted evolutionary algorithms on two suites of test cases,and the experimental findings prove that the approach is competitive in solving expensive many⁃objective optimization problems.
文摘A class of interactive multi objective decision making method by means of evaluation criterion is proposed for problems with linear value function,in which case,the decision maker(DM) usually has only unwhole information of weights for objectives. The concept of fault measure of the evaluation criterion is proposed to measure the deviation of the evaluation criterion from the DMs preference structure.The approach to obtain an upper boundary of fault measure of an evaluation criterion,and the approach to modify the evaluation criterion to be one with smaller fault measure,and the approach to obtain a pre optimized objective set by evaluation criterion with certain fault measure are also proposed.
文摘To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as an object. A changing weight value method is put forward and a selection formula is modified. Some experiments were implemented on an AUV, TwinBurger. The results shows that this method is effective and feasible.
文摘In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.
基金Supported by the National Natural Science Foundation of China(9112001591120010)
文摘To performance efficient searching for an operator-supervised mobile robot, a multiple objectives route planning approach is proposed considering timeliness and path cost. An improved fitness function for route planning is proposed based on the multi-objective genetic algorithm （MOGA） for multiple objectives traveling salesman problem （MOTSP）. Then, the path between two route nodes is generated based on the heuristic path planning method A ＊. A simplified timeliness function for route nodes is proposed to represent the timeliness of each node. Based on the proposed timeliness function, experiments are conducted using the proposed two-stage planning method. The experimental results show that the proposed MOGA with improved fitness function can perform the searching function well when the timeliness of the searching task needs to be taken into consideration.
文摘Besides economics and controllability, waste minimization has now become an objective in designing chemical processes, and usually leads to high costs of investment and operation. An attempt was made to minimize waste discharged from chemical reaction processes during the design and modification process while the operation conditions were also optimized to meet the requirements of technology and economics. Multiobjectives decision nonlinear programming (NLP) was employed to optimize the operation conditions of a chemical reaction process and reduce waste. A modeling language package-SPEEDUP was used to simulate the process. This paper presents a case study of the benzene production process. The flowsheet factors affecting the economics and waste generation were examined. Constraints were imposed to reduce the number of objectives and carry out optimal calculations easily. After comparisons of all possible solutions, best-compromise approach was applied to meet technological requirements and minimize waste.
基金support of RUSA-Phase 2.0 grant sanctioned vide Letter No.F.24-51/2014-U,Policy(TNMulti-Gen),Dep.of Edn.Govt.of India,Dt.09.10.2018.
文摘Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world population.The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains.This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques.Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy.There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy cultivation.The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)Algorithm with Genetic Algorithm(GA)and Multi Objective Particle Swarm Optimization Algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database.The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database.Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm.The reason for improving the growth of paddy was identified using the output of the Neural Network.Performance metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed algorithm.Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.
文摘This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Automata （MDFA） is defined. MDFA allows the representation of the feasible solutions space of combinatorial problems. Second, it is defined and implemented a metaheuritic based on MDFA theory. It is named Metaheuristic of Deterministic Swapping （MODS）. MODS is a local search strategy that works using a MDFA. Due to this, MODS never take into account unfeasible solutions. Hence, it is not necessary to verify the problem constraints for a new solution found. Lastly, MODS is tested using well know instances of the Bi-Objective Traveling Salesman Problem （TSP） from TSPLIB. Its results were compared with eight Ant Colony inspired algorithms and two Genetic algorithms taken from the specialized literature. The comparison was made using metrics such as Spacing, Generational Distance, Inverse Generational Distance and No-Dominated Generation Vectors. In every case, the MODS results on the metrics were always better and in some of those cases, the superiority was 100%.
文摘To solve the emerging complex optimization problems, multi objectiveoptimization algorithms are needed. By introducing the surrogate model forapproximate fitness calculation, the multi objective firefly algorithm with surrogatemodel (MOFA-SM) is proposed in this paper. Firstly, the population wasinitialized according to the chaotic mapping. Secondly, the external archive wasconstructed based on the preference sorting, with the lightweight clustering pruningstrategy. In the process of evolution, the elite solutions selected from archivewere used to guide the movement to search optimal solutions. Simulation resultsshow that the proposed algorithm can achieve better performance in terms ofconvergence iteration and stability.
基金supported by the Natural Science Foundation of China(No.52062027)the'Double-First Class'Major Research Programs,the Educational Department of Gansu Province(GSSYLXM-04)+5 种基金Soft Science Special Project of Gansu Basic Research PIan under Grant No.22JR4ZA035Gansu Provincial Science and Technology Major Special Project-Enterprise Innovation Consortium Project(No.22ZD6GA010)Natural Science Foundation of Gansu Province(22JR5RA343)Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University,China,and Open Fund of National Engineering Research Center of Highway Maintenance Technology,Changsha University of Science&Technology(No.kfj220108)Key Research and Development Project of Gansu Province(No.22YF7GA142)Industry Support Plan Project from the Department of Education of Gansu Province(No.2024CYZC-28).
文摘This paper uses the bibliometric analysis software CiteSpace to examine the current status and evolution of cold-chain logistics vehicle routing problems(CCVRP).7381 relevant articles published in the Web of Science core collection from 2008 to 2024 were analyzed,an in-depth understanding of the publication trends and category distribution were gained.Subsequently,CiteSpace was used to create a scientific knowledge graph and perform visualization analysis.The analysis includes collaboration among authors,countries,and institutions;co-citation analysis of authors,journals,and references;citation burst detection of keywords;and co-citation cluster analysis of references.Based on a deep understanding of current research hotspots,an in-depth discussion of existing research was conducted from three perspectives:optimization objectives,distribution scenarios,and solution algorithms.The results show that CCVRP involves complex factors such as temperature requirements,time window constraints,and multi-objective optimization.These intricate constraints are causing research to become increasingly interdisciplinary and comprehensive.The evolution of hot topics shows that the research directions span multiple fields,from algorithm design to logistics management.This review helps researchers better understand the history,current status,and future development directions of CCVRP research,and provides valuable references and inspiration for academia and practice.
基金supported by the National Natural Science Foundation of China(Grant Nos.52179086 and 52269022)the Central Government Guides’Local Science and Technology Development Funding Project(Grant No.23ZYQA0320).
文摘Helical axial-flow multiphase pumps are core devices for developing deep-sea oil and gas,but the complex two-phase flow inside such pumps affects their transport stability.As reported here,for enhanced flow characteristics,splitter blades were added at the noncoincidence area of the impeller tail by using a multi-objective optimization method,and their impact on the pump performance was explored in comparison with the original model.The results indicate that Gaussian process regression combined with particle swarm optimization accurately predicts the pump external characteristics.Under an inlet gas volume fraction of 30%,the optimized model increases the head coefficient by 22.2%without reducing efficiency.Although splitter blades enhance the impeller tail,they intensify gas-phase accumulation at the tail.The optimized model promotes more-uniform two-phase flow in the channels,with the axial velocity uniformity of the gas phase and liquid phase improving by 15.1%and 9.5%and the average velocity angle increasing by 12.3°and 8.3°under an inlet gas volume fraction of 30%.
文摘The presently existing decision making method for problem of goal type, i.e. the goal programming, is popular to some extent. In this paper we analyzed the features of the problem and the method,based on which we found some defects of the method and pointed out these defects. To overcome these defects we absorbed the spirit and exploited concepts of evaluation criterion and the fault measure of evaluation criterion. We proposed and applied a method with an evaluation criterion, after which we also p...
基金Supported by National Basic Research Program of China(Grant No.2014CB046403)Zhejiang Provincial Natural Science Foundation of China(Grant No.LQ14E050005)
文摘Current researches mainly focus on the investigations of the valve plate utilizing pressure relief grooves. However,air?release and cavitation can occur near the grooves. The valve plate utilizing damping holes show excellent perfor?mance in avoiding air?release and cavitation. This study aims to reduce the noise emitted from an axial piston pump using a novel valve plate utilizing damping holes. A dynamic pump model is developed,in which the fluid properties are carefully modeled to capture the phenomena of air release and cavitation. The causes of di erent noise sources are investigated using the model. A comprehensive parametric analysis is conducted to enhance the understanding of the e ects of the valve plate parameters on the noise sources. A multi?objective genetic algorithm optimization method is proposed to optimize the parameters of valve plate. The amplitudes of the swash plate moment and flow rates in the inlet and outlet ports are defined as the objective functions. The pressure overshoot and undershoot in the piston chamber are limited by properly constraining the highest and lowest pressure values. A comparison of the various noise sources between the original and optimized designs over a wide range of pressure levels shows that the noise sources are reduced at high pressures. The results of the sound pressure level measurements show that the optimized valve plate reduces the noise level by 1.6 d B(A) at the rated working condition. The proposed method is e ective in reducing the noise of axial piston pumps and contributes to the development of quieter axial piston machines.
基金Sponsored by National Natural Science Foundation of China(50901011,10932003)National Basic Research Project of China(2010CB832700)+1 种基金"04"Great Project of Ministry of Industrialization and Information of China(2011ZX04001-21)Fundamental Research Funds for Central Universities of China(DUT12LK03)
文摘Use of hot-stamped high strength steels （HSHSS） not only reduces the vehicle weight, but also improves the crash safety, therefore more and more mentioned steels are used to produce automobile parts. However, there are several problems especially the low ductility and toughness, which have restricted the application of HSHSS in automobile body. Suitable process parameters are very crucial to improve strength and toughness. In order to study the effect of austenization temperature, soaking time and start deformation temperature on strength and toughness of boron steel 22MnB5, an L9 （34） orthogonal experiment which was analyzed by means of comprehensive evaluation was carried out based on Kahn tear method to obtain the value of fracture toughness. The results indicate that the ex- cellent formability, high strength and toughness of boron steel 22MnB5 with 1.6 mm in thickness are obtained when the austenization temperature is in the range of 920- 950 ℃, the soaking time is 1 min and the start deformation temperature is in the range of 650- 700 ℃. The optimal parameters were used for typical hot stamping structural parts tests. Properties of samples such as tear strength, unit initiation energy and ratio of strength to toughness （RST） were improved by 10.91%, 20.32% and 22.17%, respectively. Toughness was increased substantially on the basis of a small decrease of strength.
基金supported by the National Railway Group Science and Technology Program(Nos.N2020J026 and N2021J028)the Independent Research and Development Project of State Key Laboratory of Traction Power,China(No.2022TPL_Q02)。
文摘High-speed locomotives are prone to carbody or bogie hunting when the wheel-rail contact conicity is excessively low or high.This can cause negative impacts on vehicle dynamics performance.This study presents four types of typical yaw damper layouts for a high-speed locomotive(Bo-Bo)and compares,by using the multi-objective optimization method,the influences of those layouts on the lateral dynamics performance of the locomotive;the linear stability indexes under lowconicity and high-conicity conditions are selected as optimization objectives.Furthermore,the radial basis function-based highdimensional model representation(RBF-HDMR)method is used to conduct a global sensitivity analysis(GSA)between key suspension parameters and the lateral dynamics performance of the locomotive,including the lateral ride comfort on straight tracks under the low-conicity condition,and also the operational safety on curved tracks.It is concluded that the layout of yaw dampers has a considerable impact on low-conicity stability and lateral ride comfort but has little influence on curving performance.There is also an important finding that only when the locomotive adopts the layout with opening outward,the difference in lateral ride comfort between the front and rear ends of the carbody can be eliminated by adjusting the lateral installation angle of the yaw dampers.Finally,force analysis and modal analysis methods are adopted to explain the influence mechanism of yaw damper layouts on the lateral stability and differences in lateral ride comfort between the front and rear ends of the carbody.
基金Item Sponsored by Science and Technology Research Program of Hubei Ministry of Education of China(D20161103)Youth Science and Technology Program of Wuhan of China(2016070204010099)
文摘A mathematical model of friction coefficient was proposed for the roll force calculation of hot-rolled strips. The online numerical solving method of the roll force calculation formula based on the proposed friction model was developed and illustrated by the practical calculation case. Then, the friction coefficient during hot strip rolling was estimated from the measured roll force by force model inversion. And then, the expression of friction model was pro posed by analyzing the calculation process of stress state coefficient, and the model parameters were determined by the shared parameter multi-model nonlinear optimization method. Finally, the industrial experiments demonstrated the feasibility and effectiveness of the related models. The accuracy of the new roll force model based on the built friction model was much higher than that of the traditional Sims model, and it could be applied in the online hot rolling process control.
基金Supported by the National Key Technology R&D Program of the 11th Five-Year Plan of China(2006BAF04B05)the Natural Science Foundation of Shanxi Province(2010021024-2)
文摘Perfect combination of structural size parameters of the hydroforming billets is essential to obtain even wall thicknesses of the car beam. Finite element （ FE ） analysis on hydroforming car beam was carried out, and the results were optimized according to multiple quality objectives by the grey system theory. With bending angle, bending radius and hight difference along the axis direction as variables, orthogonal FE analyses were conducted and the minimum and maximum wall thicknes ses of the billets with different sizes were obtained. Taking the minimum and maximum wall thick nesses as two references, the correlation coefficient between the data for reference and those for comparison by the grey system theory reduced multi objectives to a single quality objective, and the average correlation level of every billet facilitated the optimization of size parameters for hydroform ing car beam. The trial production showed that the optimization approach satisfied the need of hy droforming car beams.
基金Supporting Project number(PNURSP2023R410)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.supported by MRC,UK(MC_PC_17171)+9 种基金Royal Society,UK(RP202G0230)BHF,UK(AA/18/3/34220)Hope Foundation for Cancer Research,UK(RM60G0680)GCRF,UK(P202PF11)Sino‐UK Industrial Fund,UK(RP202G0289)LIAS,UK(P202ED10,P202RE969)Data Science Enhancement Fund,UK(P202RE237)Fight for Sight,UK(24NN201)Sino‐UK Education Fund,UK(OP202006)BBSRC,UK(RM32G0178B8).The funding of this work was provided by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical images.The deep learning models show more remarkable performance than the traditional methods for medical image processing tasks,such as skin cancer,colorectal cancer,brain tumour,cardiac disease,Breast cancer(BrC),and a few more.The manual diagnosis of medical issues always requires an expert and is also expensive.Therefore,developing some computer diagnosis techniques based on deep learning is essential.Breast cancer is the most frequently diagnosed cancer in females with a rapidly growing percentage.It is estimated that patients with BrC will rise to 70%in the next 20 years.If diagnosed at a later stage,the survival rate of patients with BrC is shallow.Hence,early detection is essential,increasing the survival rate to 50%.A new framework for BrC classification is presented that utilises deep learning and feature optimization.The significant steps of the presented framework include(i)hybrid contrast enhancement of acquired images,(ii)data augmentation to facilitate better learning of the Convolutional Neural Network(CNN)model,(iii)a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes,(iv)deep transfer learning based model training for feature extraction,(v)the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach,and(vi)optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning classifiers.The experiments of the proposed framework have been carried out using the most critical and publicly available dataset,such as CBISDDSM,and obtained the best accuracy of 94.5%along with improved computation time.The comparison depicts that the presented method surpasses the current state‐ofthe‐art approaches.
文摘In view of the problem of power quality degradation of port distribution network after the large-scale application of shore power load,a method of power quality management of port distribution network is proposed.Based on the objective function of the best power quality management effect and the smallest investment cost of the management device,the optimization model of power quality management in the distribution network after the large-scale application of large-capacity shore power is constructed.Based on the balance between the economic demand of distribution network resources optimization and power quality management capability,the power quality of distribution network is considered comprehensively.The proposed optimization algorithm for power quality management based on Matlab and OpenDSS is proposed and analyzed for port distribution networks.The simulation results show that the proposed optimizationmethod can maximize the power qualitymanagement capability of the port distribution network,and the proposed optimization algorithm has good convergence and global optimization finding capability.
