The probability-based covering algorithm(PBCA) is a new algorithm based on probability distribution. It decides, by voting, the class of the tested samples on the border of the coverage area, based on the probability ...The probability-based covering algorithm(PBCA) is a new algorithm based on probability distribution. It decides, by voting, the class of the tested samples on the border of the coverage area, based on the probability of training samples. When using the original covering algorithm(CA), many tested samples that are located on the border of the coverage cannot be classified by the spherical neighborhood gained. The network structure of PBCA is a mixed structure composed of both a feed-forward network and a feedback network. By using this method of adding some heterogeneous samples and enlarging the coverage radius,it is possible to decrease the number of rejected samples and improve the rate of recognition accuracy. Relevant computer experiments indicate that the algorithm improves the study precision and achieves reasonably good results in text classification.展开更多
Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status ...Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status of land covers in Hung Yen province of Vietnam using Landsat 8 OLI satellite images,a free data source with reasonable spatial and temporal resolution.The results of the study show that all three algorithms presented good classification for five basic types of land cover including Rice land,Water bodies,Perennial vegetation,Annual vegetation,Built-up areas as their overall accuracy and Kappa coefficient were greater than 80%and 0.8,respectively.Among the three algorithms,SVM achieved the highest accuracy as its overall accuracy was 86%and the Kappa coefficient was 0.88.Land cover classification based on the SVM algorithm shows that Built-up areas cover the largest area with nearly 31,495 ha,accounting for more than 33.8%of the total natural area,followed by Rice land and Perennial vegetation which cover an area of over 30,767 ha(33%)and 15,637 ha(16.8%),respectively.Water bodies and Annual vegetation cover the smallest areas with 8,820(9.5%)ha and 6,302 ha(6.8%),respectively.The results of this study can be used for land use management and planning as well as other natural resource and environmental management purposes in the province.展开更多
Currently,the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems.In this sense,metaheuristics have been a common trend in the field in order to design approaches to so...Currently,the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems.In this sense,metaheuristics have been a common trend in the field in order to design approaches to solve them successfully.Thus,a well-known strategy consists in the use of algorithms based on discrete swarms transformed to perform in binary environments.Following the No Free Lunch theorem,we are interested in testing the performance of the Fruit Fly Algorithm,this is a bio-inspired metaheuristic for deducing global optimization in continuous spaces,based on the foraging behavior of the fruit fly,which usually has much better sensory perception of smell and vision than any other species.On the other hand,the Set Coverage Problem is a well-known NP-hard problem with many practical applications,including production line balancing,utility installation,and crew scheduling in railroad and mass transit companies.In this paper,we propose different binarization methods for the Fruit Fly Algorithm,using Sshaped and V-shaped transfer functions and various discretization methods to make the algorithm work in a binary search space.We are motivated with this approach,because in this way we can deliver to future researchers interested in this area,a way to be able to work with continuous metaheuristics in binary domains.This new approach was tested on benchmark instances of the Set Coverage Problem and the computational results show that the proposed algorithm is robust enough to produce good results with low computational cost.展开更多
Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction m...Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.展开更多
In this paper, we investigate the/-preemptive scheduling on parallel machines to maximize the minimum machine completion time, i.e., machine covering problem with limited number of preemptions. It is aimed to obtain t...In this paper, we investigate the/-preemptive scheduling on parallel machines to maximize the minimum machine completion time, i.e., machine covering problem with limited number of preemptions. It is aimed to obtain the worst case ratio of the objective value of the optimal schedule with unlimited preemptions and that of the schedule allowed to be preempted at most i times. For the m identical machines case, we show the worst case ratio is 2m-i-1/m and we present a polynomial time algorithm which can guarantee the ratio for any 0 〈 i 〈2 m - 1. For the /-preemptive scheduling on two uniform machines case, we only need to consider the cases of i = 0 and i = 1. For both cases, we present two linear time algorithms and obtain the worst case ratios with respect to s, i.e., the ratio of the speeds of two machines.展开更多
This paper presents a new hybrid genetic algorithm for the vertex cover problems in which scan-repair and local improvement techniques are used for local optimization. With the hybrid approach, genetic algorithms are ...This paper presents a new hybrid genetic algorithm for the vertex cover problems in which scan-repair and local improvement techniques are used for local optimization. With the hybrid approach, genetic algorithms are used to perform global exploration in a population, while neighborhood search methods are used to perform local exploitation around the chromosomes. The experimental results indicate that hybrid genetic algorithms can obtain solutions of excellent quality to the problem instances with different sizes. The pure genetic algorithms are outperformed by the neighborhood search heuristics procedures combined with genetic algorithms.展开更多
This paper describes an extremely fast polynomial time algorithm, the NOVCA （Near Optimal Vertex Cover Algorithm） that produces an optimal or near optimal vertex cover for any known undirected graph G （V, E）. NOVC...This paper describes an extremely fast polynomial time algorithm, the NOVCA （Near Optimal Vertex Cover Algorithm） that produces an optimal or near optimal vertex cover for any known undirected graph G （V, E）. NOVCA is based on the idea of（l） including the vertex having maximum degree in the vertex cover and （2） rendering the degree of a vertex to zero by including all its adjacent vertices. The three versions of algorithm, NOVCA-I, NOVCA-II, and NOVCA-random, have been developed. The results identifying bounds on the size of the minimum vertex cover as well as polynomial complexity of algorithm are given with experimental verification. Future research efforts will be directed at tuning the algorithm and providing proof for better approximation ratio with NOVCA compared to any available vertex cover algorithms.展开更多
Construction of Covering Arrays （CA） with minimum possible number of rows is challenging. Often the available CA have redundant combinatorial interaction that could be removed to reduce the number of rows. This pape...Construction of Covering Arrays （CA） with minimum possible number of rows is challenging. Often the available CA have redundant combinatorial interaction that could be removed to reduce the number of rows. This paper addresses the problem of removing redundancy of CA using a metaheuristic post- optimization （MPO） approach. Our approach consists of three main components： a redundancy detector （RD）; a row reducer （RR）; and a missing-combinations reducer （MCR）. The MCR is a metaheuristic component implemented using a simulated annealing algorithm. MPO was instantiated with 21,964 CA taken from the National Institute of Standards and Technology （NIST） repository. It is a remarkable result that this instantiation of MPO has delivered 349 new upper bounds for these CA.展开更多
The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and,therefore,the areal extent of crop residue cover(CRC)could provide a rapid measure of the sustainability of agricul...The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and,therefore,the areal extent of crop residue cover(CRC)could provide a rapid measure of the sustainability of agricultural production systems in a region.Recognizing the limitations of traditional CRC methods,a new method is proposed for estimating the spatial and temporal distribution of maize residue cover(MRC)in the Jilin Province,NE China.The method used random forest(RF)algorithms,13 tillage indices and 9 textural feature indicators derived from Sentinel-2 data.The tillage indices with the best predictive performance were STI and NDTI(R^(2) of 0.85 and 0.84,respectively).Among the texture features,the bestfitting was Band8AMean-5*5(R^(2) of 0.56 and 0.54 for the line-transect and photographic methods,respectively).Based on MSE and InNodePurity,the optimal combination of RF algorithm for the linetransect method was STI,NDTI,NDI7,NDRI5,SRNDI,NDRI6,NDRI7 and Band3Mean-3*3.Likewise,the optimal combination of RF algorithm for the photographic method was STI,NDTI,NDI7,SRNDI,NDRI6,NDRI5,NDRI9 and Band3Mean-3*3.Regional distribution of MRC in the Jilin Province,estimated using the RF prediction model,was higher in the central and southeast sections than in the northwest.That distribution was in line with the spatial heterogeneity of maize yield in the region.These findings showed that the RF algorithm can be used to map regional MRC and,therefore,represents a useful tool for monitoring regional-scale adoption of conservation agricultural practices.展开更多
基金supported by the Fund for Philosophy and Social Science of Anhui Provincethe Fund for Human and Art Social Science of the Education Department of Anhui Province(Grant Nos.AHSKF0708D13 and 2009sk038)
文摘The probability-based covering algorithm(PBCA) is a new algorithm based on probability distribution. It decides, by voting, the class of the tested samples on the border of the coverage area, based on the probability of training samples. When using the original covering algorithm(CA), many tested samples that are located on the border of the coverage cannot be classified by the spherical neighborhood gained. The network structure of PBCA is a mixed structure composed of both a feed-forward network and a feedback network. By using this method of adding some heterogeneous samples and enlarging the coverage radius,it is possible to decrease the number of rejected samples and improve the rate of recognition accuracy. Relevant computer experiments indicate that the algorithm improves the study precision and achieves reasonably good results in text classification.
文摘Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status of land covers in Hung Yen province of Vietnam using Landsat 8 OLI satellite images,a free data source with reasonable spatial and temporal resolution.The results of the study show that all three algorithms presented good classification for five basic types of land cover including Rice land,Water bodies,Perennial vegetation,Annual vegetation,Built-up areas as their overall accuracy and Kappa coefficient were greater than 80%and 0.8,respectively.Among the three algorithms,SVM achieved the highest accuracy as its overall accuracy was 86%and the Kappa coefficient was 0.88.Land cover classification based on the SVM algorithm shows that Built-up areas cover the largest area with nearly 31,495 ha,accounting for more than 33.8%of the total natural area,followed by Rice land and Perennial vegetation which cover an area of over 30,767 ha(33%)and 15,637 ha(16.8%),respectively.Water bodies and Annual vegetation cover the smallest areas with 8,820(9.5%)ha and 6,302 ha(6.8%),respectively.The results of this study can be used for land use management and planning as well as other natural resource and environmental management purposes in the province.
文摘Currently,the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems.In this sense,metaheuristics have been a common trend in the field in order to design approaches to solve them successfully.Thus,a well-known strategy consists in the use of algorithms based on discrete swarms transformed to perform in binary environments.Following the No Free Lunch theorem,we are interested in testing the performance of the Fruit Fly Algorithm,this is a bio-inspired metaheuristic for deducing global optimization in continuous spaces,based on the foraging behavior of the fruit fly,which usually has much better sensory perception of smell and vision than any other species.On the other hand,the Set Coverage Problem is a well-known NP-hard problem with many practical applications,including production line balancing,utility installation,and crew scheduling in railroad and mass transit companies.In this paper,we propose different binarization methods for the Fruit Fly Algorithm,using Sshaped and V-shaped transfer functions and various discretization methods to make the algorithm work in a binary search space.We are motivated with this approach,because in this way we can deliver to future researchers interested in this area,a way to be able to work with continuous metaheuristics in binary domains.This new approach was tested on benchmark instances of the Set Coverage Problem and the computational results show that the proposed algorithm is robust enough to produce good results with low computational cost.
基金supported by the Beijing Natural Science Foundation,China(4202066)the Central Public-interest Scientific Institution Basal Research Fund,China(JBYWAII-2020-29 and JBYW-AII-2020-31)+1 种基金the Key Research and Development Program of Hebei Province,China(19227407D)the Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences(CAAS-ASTIP2020-All)。
文摘Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.
基金Supported by the National Natural Science Foundation of China(11001242,11071220)
文摘In this paper, we investigate the/-preemptive scheduling on parallel machines to maximize the minimum machine completion time, i.e., machine covering problem with limited number of preemptions. It is aimed to obtain the worst case ratio of the objective value of the optimal schedule with unlimited preemptions and that of the schedule allowed to be preempted at most i times. For the m identical machines case, we show the worst case ratio is 2m-i-1/m and we present a polynomial time algorithm which can guarantee the ratio for any 0 〈 i 〈2 m - 1. For the /-preemptive scheduling on two uniform machines case, we only need to consider the cases of i = 0 and i = 1. For both cases, we present two linear time algorithms and obtain the worst case ratios with respect to s, i.e., the ratio of the speeds of two machines.
基金This project was supported by the National Natural Science Foundation of China the Open Project Foundation of Comput-er Software New Technique National Key Laboratory of Nanjing University.
文摘This paper presents a new hybrid genetic algorithm for the vertex cover problems in which scan-repair and local improvement techniques are used for local optimization. With the hybrid approach, genetic algorithms are used to perform global exploration in a population, while neighborhood search methods are used to perform local exploitation around the chromosomes. The experimental results indicate that hybrid genetic algorithms can obtain solutions of excellent quality to the problem instances with different sizes. The pure genetic algorithms are outperformed by the neighborhood search heuristics procedures combined with genetic algorithms.
文摘This paper describes an extremely fast polynomial time algorithm, the NOVCA （Near Optimal Vertex Cover Algorithm） that produces an optimal or near optimal vertex cover for any known undirected graph G （V, E）. NOVCA is based on the idea of（l） including the vertex having maximum degree in the vertex cover and （2） rendering the degree of a vertex to zero by including all its adjacent vertices. The three versions of algorithm, NOVCA-I, NOVCA-II, and NOVCA-random, have been developed. The results identifying bounds on the size of the minimum vertex cover as well as polynomial complexity of algorithm are given with experimental verification. Future research efforts will be directed at tuning the algorithm and providing proof for better approximation ratio with NOVCA compared to any available vertex cover algorithms.
文摘Construction of Covering Arrays （CA） with minimum possible number of rows is challenging. Often the available CA have redundant combinatorial interaction that could be removed to reduce the number of rows. This paper addresses the problem of removing redundancy of CA using a metaheuristic post- optimization （MPO） approach. Our approach consists of three main components： a redundancy detector （RD）; a row reducer （RR）; and a missing-combinations reducer （MCR）. The MCR is a metaheuristic component implemented using a simulated annealing algorithm. MPO was instantiated with 21,964 CA taken from the National Institute of Standards and Technology （NIST） repository. It is a remarkable result that this instantiation of MPO has delivered 349 new upper bounds for these CA.
基金jointly supported by the National Key Research and Development Program of China(2021YFD1500103)the Science and Technology Project for Black Soil Granary(XDA28080500)the National Science&Technology Fundamental Resources Investigation Program of China(2018FY100300).
文摘The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and,therefore,the areal extent of crop residue cover(CRC)could provide a rapid measure of the sustainability of agricultural production systems in a region.Recognizing the limitations of traditional CRC methods,a new method is proposed for estimating the spatial and temporal distribution of maize residue cover(MRC)in the Jilin Province,NE China.The method used random forest(RF)algorithms,13 tillage indices and 9 textural feature indicators derived from Sentinel-2 data.The tillage indices with the best predictive performance were STI and NDTI(R^(2) of 0.85 and 0.84,respectively).Among the texture features,the bestfitting was Band8AMean-5*5(R^(2) of 0.56 and 0.54 for the line-transect and photographic methods,respectively).Based on MSE and InNodePurity,the optimal combination of RF algorithm for the linetransect method was STI,NDTI,NDI7,NDRI5,SRNDI,NDRI6,NDRI7 and Band3Mean-3*3.Likewise,the optimal combination of RF algorithm for the photographic method was STI,NDTI,NDI7,SRNDI,NDRI6,NDRI5,NDRI9 and Band3Mean-3*3.Regional distribution of MRC in the Jilin Province,estimated using the RF prediction model,was higher in the central and southeast sections than in the northwest.That distribution was in line with the spatial heterogeneity of maize yield in the region.These findings showed that the RF algorithm can be used to map regional MRC and,therefore,represents a useful tool for monitoring regional-scale adoption of conservation agricultural practices.
