Since it first appeared in 2022,the phenomenon referred to as Colony Collapse Disorder(CCD)has affected several regions of Morocco to varying degrees.In order to assess the possible impact of pesticides on the appeara...Since it first appeared in 2022,the phenomenon referred to as Colony Collapse Disorder(CCD)has affected several regions of Morocco to varying degrees.In order to assess the possible impact of pesticides on the appearance of this syndrome,we conducted a study aimed at evaluating the impact of pesticide use on the emergence of this syndrome through a year-long survey involving 160 beekeepers in the Beni Mellal–Khenifra Region(BKR)who also experienced an unprecedented desertion of hives during the same period.The majority of surveyed beekeepers practice mixed(45%)or migratory beekeeping(42%)and provide supplementary feeding(83.75%)to support their bees.Nearly 37.5%of the hives are located near crops treated with pesticides,exposing the bees to these chemicals.The results showed that the majority of beekeepers reported a cessation of queen laying(74.38%),high mortality rates among worker bees(81.25%),drones(65.63%),and queens(61.88%).Abnormal behaviors such as immobility with trembling(42.50%),reduced flights(47.50%),and disoriented navigation(28.75%)were also observed.Correlation analyses indicate that proximity to treated crops significantly increases the risk of queen laying cessation(Odds Ratio 6.0)and a reduction in waggle dances(Odds Ratio 2.41).Extended foraging flights show a borderline statistical significance(Odds Ratio 2.33),suggesting a disruption of natural food sources.These results highlight the potential impact of pesticides on colony health and bee behavior,pointing out the need to adapt beekeeping practices and implement protective measures against pesticides.展开更多
This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structu...This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structure,a double-layer Ag-Au metal film is combined with a blue phosphorene/transition metal dichalcogenide(BlueP/TMDC)hybrid structure and graphene.In the optimization function of the IABC method,the reflectivity at resonance angle is incorporated as a constraint to achieve high phase sensitivity.The performance of the Ag-Au-BlueP/TMDC-graphene heterostructure as optimized by the IABC method is compared with that of a similar structure optimized using the traditional ABC algorithm.The results indicate that optimization using the IABC method gives significantly more phase sensitivity,together with lower reflectivity,than can be achieved with the traditional ABC method.The highest phase sensitivity of 3.662×10^(6) °/RIU is achieved with a bilayer of BlueP/WS2 and three layers of graphene.Moreover,analysis of the electric field distribution demonstrates that the optimal arrangement can be utilized for enhanced detection of small biomolecules.Thus,given the exceptional sensitivity achieved,the proposed method based on the IABC algorithm has great promise for use in the design of high-performance SPR biosensors with a variety of multilayer structures.展开更多
Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of t...Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.展开更多
Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human p...Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human population and data trafficon the ground incurs unbalanced traffic load inLEO satellite networks. To this end, we proposea load-balancing routing algorithm for LEO satellitenetworks based on ant colony optimization and reinforcementlearning. In the ant colony algorithm,we improve the pheromone update rule by introducingload-aware heuristic information, e.g., the currentnode transmission overhead, delay and load status, andreinforcement learning-based link quality evaluation.It enables the routing algorithm to select the lightlyloaded node as the next hop to balance the networkload. We simulate and verify the proposed algorithmusing the NS2 simulation platform, and the resultsshow that our algorithm improves the data delivery ratioand throughput while ensuring lower latency andtransmission overhead.展开更多
Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a ...Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.展开更多
In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm base...In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.展开更多
Path planning is an important issue for autonomous underwater vehicles （AUVs） traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning usi...Path planning is an important issue for autonomous underwater vehicles （AUVs） traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization （ACO） were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.展开更多
[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored...[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.展开更多
Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencie...Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.展开更多
Granulocyte colony-stimulating factor(G-CSF)-producing tumor is one of the rare types of cancer clinically characterized by an elevated fever and white blood cell(WBC)increment.Although G-CSF producing tumors have bee...Granulocyte colony-stimulating factor(G-CSF)-producing tumor is one of the rare types of cancer clinically characterized by an elevated fever and white blood cell(WBC)increment.Although G-CSF producing tumors have been reported in several types of cancer including those of the lungs,cervix and bladder,G-CSF producing hepatocellular carcinoma is extremely rare.Here,we report the case of a rapidly growing and poorly differentiated hepatocellular carcinoma producing G-CSF.The patient showed symptoms of continuous high fever,stomach pain and cough,and high serum WBC counts,C-reactive protein(CRP)and G-CSF levels were found in laboratory tests.After a radical hepatectomy,the patient completely recovered from the above symptoms and inflammatory state.The serum levels of G-CSF were reduced to normal levels after radical surgery.An immunohistochemical analysis revealed the overexpression of G-CSF in the cytoplasm of certain hepatocellular carcinoma(HCC)cell.The patient's serum WBC,CRP and G-CSF levels remained within normal levels in the six months after surgery without recurrence.This is the 9^(th)case report of G-CSF producing hepatocellular carcinoma in English literature.We review the clinical characteristics of the G-CSF producing HCC and discuss a possible treatment strategy.展开更多
As one of the most widely used assays in biological research,an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive process.To speed up the colony counting,a machine learn...As one of the most widely used assays in biological research,an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive process.To speed up the colony counting,a machine learning method is presented for counting the colony forming units(CFUs),which is referred to as CFUCounter.This cellcounting program processes digital images and segments bacterial colonies.The algorithm combines unsupervised machine learning,iterative adaptive thresholding,and local-minima-based watershed segmentation to enable an accurate and robust cell counting.Compared to a manual counting method,CFUCounter supports color-based CFU classification,allows plates containing heterologous colonies to be counted individually,and demonstrates overall performance(slope 0.996,SD 0.013,95%CI:0.97–1.02,p value<1e-11,r=0.999)indistinguishable from the gold standard of point-and-click counting.This CFUCounter application is open-source and easy to use as a unique addition to the arsenal of colony-counting tools.展开更多
In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel referenc...In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel reference point based preference expression method is addressed.The fitness assignment function is defined based on the nondominated rank and the newly defined preference distance.An archive set is introduced for saving the nondominated solutions and an improved crowding-distance operator is addressed to remove the extra solutions in the archive.The experimental results of two benchmark test functions show that a preferred set of solutions and some other non-preference solutions are achieved simultaneously.The simulation results of the proportional-integral-derivative PID parameter optimization for superheated steam temperature verify that the PMABCA is efficient in aiding to making a reasonable decision.展开更多
To solve the traveling salesman problem with the characteristics of clustering,a novel hybrid algorithm,the ant colony algorithm combined with the C-means algorithm,is presented.In order to improve the speed of conver...To solve the traveling salesman problem with the characteristics of clustering,a novel hybrid algorithm,the ant colony algorithm combined with the C-means algorithm,is presented.In order to improve the speed of convergence,the traveling salesman problem(TSP)data is specially clustered by the C-means algorithm,then,the result is processed by the ant colony algorithm to solve the problem.The proposed algorithm treats the C-means algorithm as a new search operator and adopts a kind of local searching strategy—2-opt,so as to improve the searching performance.Given the cluster number,the algorithm can obtain the preferable solving result.Compared with the three other algorithms—the ant colony algorithm,the genetic algorithm and the simulated annealing algorithm,the proposed algorithm can make the results converge to the global optimum faster and it has higher accuracy.The algorithm can also be extended to solve other correlative clustering combination optimization problems.Experimental results indicate the validity of the proposed algorithm.展开更多
Flavobacterium columnare is the pathogenic agent of columnaris disease in aquaculture. Using a recently developed gene deletion strategy, two genes that encode the Glyco hydro_19 domain （GH19 domain） containing prot...Flavobacterium columnare is the pathogenic agent of columnaris disease in aquaculture. Using a recently developed gene deletion strategy, two genes that encode the Glyco hydro_19 domain （GH19 domain） containing proteins, ghd-1 and ghd-2, were deleted separately and together from the F. columnare G4 wild type strain. Surprisingly, the single-, Aghd-1 and Aghd-2, and double-gene mutants, Aghd-1 Aghd-2, all had rhizoid and non-rhizoid colony morphotypes, which we named Aghd-1, Aghd-2, Aghd-1 Aghd-2, and NAghd-1, NAghd-2, and NAghd-1 Aghd-2. However, chitin utilization was not detected in either these mutants or in the wild type. Instead, skimmed milk degradation was observed for the mutants and the wild type; the non-rhizoid strain NAghd-2 exhibited higher degradation activity as revealed by the larger transparent circle on the skimmed milk plate. Using zebrafish as the model organism, we found that non-rhizoid mutants had higher LDs0 values and were less virulent because zebrafish infected with these survived longer. Transcriptome analysis between the non-rhizoid and rhizoid colony morphotypes of each mutant, i.e., NAghd-1 versus （vs） Aghd-1, NAghd-2 vs Aghd-2, and NAghd-1 Aghd-2 vs Aghd-1 Aghd-2, revealed a large number of differentially expressed genes, among which 39 genes were common in three of the pairs compared. Although most of these genes encode hypothetical proteins, a few molecules such as phage tail protein, rhs element Vgr protein, thiol-activated cytolysin, and TonB-dependent outer membrane receptor precursor, expression of which was down-regulated in non-rhizoid mutants but up-regulated in rhizoid mutants, may play a role F. columnare virulence.展开更多
Aiming at the shortcomings of the existing automatic colony counter, a set of algorithms based on the principle of image chromatic aberration to achieve colony identification is proposed, and a colony identification d...Aiming at the shortcomings of the existing automatic colony counter, a set of algorithms based on the principle of image chromatic aberration to achieve colony identification is proposed, and a colony identification device is developed on this basis. The colony identification method is mainly based on the fact that different kinds of colonies and different concentrations of the same kind of colonies have different light-absorbing characteristics, and the judgement of colony types and concentrations is achieved through the method of image processing. The main features of the developed colony recognition equipment are high working efficiency, short recognition and detection time, and the potential of mixed recognition ability of multiple colonies. Therefore, the identification method and equipment have good application and promotion value in agriculture, food, medicine and other industries.展开更多
This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy featu...This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid rescheduling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances.展开更多
The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method...The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method for the path planning. In the paper the traditional ant colony algorithm is improved, and measures of keeping optimization, adaptively selecting and adaptively adjusting are applied, by which better path at higher convergence speed can be found. Finally the algorithm is implemented with computer simulation and preferable results are obtained.展开更多
Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is amo...Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.展开更多
Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic mode...Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach.展开更多
文摘Since it first appeared in 2022,the phenomenon referred to as Colony Collapse Disorder(CCD)has affected several regions of Morocco to varying degrees.In order to assess the possible impact of pesticides on the appearance of this syndrome,we conducted a study aimed at evaluating the impact of pesticide use on the emergence of this syndrome through a year-long survey involving 160 beekeepers in the Beni Mellal–Khenifra Region(BKR)who also experienced an unprecedented desertion of hives during the same period.The majority of surveyed beekeepers practice mixed(45%)or migratory beekeeping(42%)and provide supplementary feeding(83.75%)to support their bees.Nearly 37.5%of the hives are located near crops treated with pesticides,exposing the bees to these chemicals.The results showed that the majority of beekeepers reported a cessation of queen laying(74.38%),high mortality rates among worker bees(81.25%),drones(65.63%),and queens(61.88%).Abnormal behaviors such as immobility with trembling(42.50%),reduced flights(47.50%),and disoriented navigation(28.75%)were also observed.Correlation analyses indicate that proximity to treated crops significantly increases the risk of queen laying cessation(Odds Ratio 6.0)and a reduction in waggle dances(Odds Ratio 2.41).Extended foraging flights show a borderline statistical significance(Odds Ratio 2.33),suggesting a disruption of natural food sources.These results highlight the potential impact of pesticides on colony health and bee behavior,pointing out the need to adapt beekeeping practices and implement protective measures against pesticides.
基金funded by the National Natural Science Foundation of China(Grant No.52375547)the Natural Science Foundation of Chongqing,China(Grant Nos.CSTB2022NSCQ-BHX0736 and CSTB2022NSCQ-MSX1523)the Chongqing Scientific Institution Incentive Performance Guiding Special Projects(Grant No.CSTB2024JXJL-YFX0034).
文摘This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structure,a double-layer Ag-Au metal film is combined with a blue phosphorene/transition metal dichalcogenide(BlueP/TMDC)hybrid structure and graphene.In the optimization function of the IABC method,the reflectivity at resonance angle is incorporated as a constraint to achieve high phase sensitivity.The performance of the Ag-Au-BlueP/TMDC-graphene heterostructure as optimized by the IABC method is compared with that of a similar structure optimized using the traditional ABC algorithm.The results indicate that optimization using the IABC method gives significantly more phase sensitivity,together with lower reflectivity,than can be achieved with the traditional ABC method.The highest phase sensitivity of 3.662×10^(6) °/RIU is achieved with a bilayer of BlueP/WS2 and three layers of graphene.Moreover,analysis of the electric field distribution demonstrates that the optimal arrangement can be utilized for enhanced detection of small biomolecules.Thus,given the exceptional sensitivity achieved,the proposed method based on the IABC algorithm has great promise for use in the design of high-performance SPR biosensors with a variety of multilayer structures.
基金supported by the National Natural Science Foundation of China(62276055).
文摘Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.
基金supported in part by the National Natural Science Foundation of China(Grant No.62273107,61702127,62272113)Science and Technology Program of Guangzhou(Grant No.201804010461).
文摘Low earth orbit (LEO) satellite networkscan provide wider service coverage and lower latencythan traditional terrestrial networks, which haveattracted considerable attention. However, the unevendistribution of human population and data trafficon the ground incurs unbalanced traffic load inLEO satellite networks. To this end, we proposea load-balancing routing algorithm for LEO satellitenetworks based on ant colony optimization and reinforcementlearning. In the ant colony algorithm,we improve the pheromone update rule by introducingload-aware heuristic information, e.g., the currentnode transmission overhead, delay and load status, andreinforcement learning-based link quality evaluation.It enables the routing algorithm to select the lightlyloaded node as the next hop to balance the networkload. We simulate and verify the proposed algorithmusing the NS2 simulation platform, and the resultsshow that our algorithm improves the data delivery ratioand throughput while ensuring lower latency andtransmission overhead.
基金The National Natural Science Foundation of China(No.61074147)the Natural Science Foundation of Guangdong Province(No.S2011010005059)+2 种基金the Foundation of Enterprise-University-Research Institute Cooperation from Guangdong Province and Ministry of Education of China(No.2012B091000171,2011B090400460)the Science and Technology Program of Guangdong Province(No.2012B050600028)the Science and Technology Program of Huadu District,Guangzhou(No.HD14ZD001)
文摘Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.
基金The National Natural Science Foundation of China(No.61231002,61273266,61571106)the Foundation of the Department of Science and Technology of Guizhou Province(No.[2015]7637)
文摘In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.
基金Supported by State Key Laboratory of Robotics and System (HIT) under Grant No.SKLRS200706the Heilongjiang Scientific Research Foundation for Postdoctoral Financial Assistance under Grant No.323630221the Project of Harbin Technological Talent Research Foundation under Grant No.RC2006QN009015
文摘Path planning is an important issue for autonomous underwater vehicles （AUVs） traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization （ACO） were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.
基金Supported by the National Natural Science Foundation of China(31101085)the Program for Young Core Teachers of Colleges in Henan(2011GGJS-094)the Scientific Research Project for the High Level Talents,North China University of Water Conservancy and Hydroelectric Power~~
文摘[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.
基金supported by the National Natural Science Foundation of China(7177121671701209)
文摘Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.
文摘Granulocyte colony-stimulating factor(G-CSF)-producing tumor is one of the rare types of cancer clinically characterized by an elevated fever and white blood cell(WBC)increment.Although G-CSF producing tumors have been reported in several types of cancer including those of the lungs,cervix and bladder,G-CSF producing hepatocellular carcinoma is extremely rare.Here,we report the case of a rapidly growing and poorly differentiated hepatocellular carcinoma producing G-CSF.The patient showed symptoms of continuous high fever,stomach pain and cough,and high serum WBC counts,C-reactive protein(CRP)and G-CSF levels were found in laboratory tests.After a radical hepatectomy,the patient completely recovered from the above symptoms and inflammatory state.The serum levels of G-CSF were reduced to normal levels after radical surgery.An immunohistochemical analysis revealed the overexpression of G-CSF in the cytoplasm of certain hepatocellular carcinoma(HCC)cell.The patient's serum WBC,CRP and G-CSF levels remained within normal levels in the six months after surgery without recurrence.This is the 9^(th)case report of G-CSF producing hepatocellular carcinoma in English literature.We review the clinical characteristics of the G-CSF producing HCC and discuss a possible treatment strategy.
基金This research was funded by a VPR Special Research Grant entitled Potential of a Site-Specific DNA Interstrand Crosslink.
文摘As one of the most widely used assays in biological research,an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive process.To speed up the colony counting,a machine learning method is presented for counting the colony forming units(CFUs),which is referred to as CFUCounter.This cellcounting program processes digital images and segments bacterial colonies.The algorithm combines unsupervised machine learning,iterative adaptive thresholding,and local-minima-based watershed segmentation to enable an accurate and robust cell counting.Compared to a manual counting method,CFUCounter supports color-based CFU classification,allows plates containing heterologous colonies to be counted individually,and demonstrates overall performance(slope 0.996,SD 0.013,95%CI:0.97–1.02,p value<1e-11,r=0.999)indistinguishable from the gold standard of point-and-click counting.This CFUCounter application is open-source and easy to use as a unique addition to the arsenal of colony-counting tools.
基金The National Natural Science Foundation of China(No.51306082,51476027)
文摘In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel reference point based preference expression method is addressed.The fitness assignment function is defined based on the nondominated rank and the newly defined preference distance.An archive set is introduced for saving the nondominated solutions and an improved crowding-distance operator is addressed to remove the extra solutions in the archive.The experimental results of two benchmark test functions show that a preferred set of solutions and some other non-preference solutions are achieved simultaneously.The simulation results of the proportional-integral-derivative PID parameter optimization for superheated steam temperature verify that the PMABCA is efficient in aiding to making a reasonable decision.
基金The National Key Technology R&D Program of China during the 11th Five-Year Plan Period(No.2006BAH02A06)
文摘To solve the traveling salesman problem with the characteristics of clustering,a novel hybrid algorithm,the ant colony algorithm combined with the C-means algorithm,is presented.In order to improve the speed of convergence,the traveling salesman problem(TSP)data is specially clustered by the C-means algorithm,then,the result is processed by the ant colony algorithm to solve the problem.The proposed algorithm treats the C-means algorithm as a new search operator and adopts a kind of local searching strategy—2-opt,so as to improve the searching performance.Given the cluster number,the algorithm can obtain the preferable solving result.Compared with the three other algorithms—the ant colony algorithm,the genetic algorithm and the simulated annealing algorithm,the proposed algorithm can make the results converge to the global optimum faster and it has higher accuracy.The algorithm can also be extended to solve other correlative clustering combination optimization problems.Experimental results indicate the validity of the proposed algorithm.
基金Supported by the Chinese Academy of Sciences(No.XDA08010207)the National Key Technology R&D Program of China(No.2012BAD25B02)the State Key Laboratory of Freshwater Ecology and Biotechnology(No.2016FBZ04)
文摘Flavobacterium columnare is the pathogenic agent of columnaris disease in aquaculture. Using a recently developed gene deletion strategy, two genes that encode the Glyco hydro_19 domain （GH19 domain） containing proteins, ghd-1 and ghd-2, were deleted separately and together from the F. columnare G4 wild type strain. Surprisingly, the single-, Aghd-1 and Aghd-2, and double-gene mutants, Aghd-1 Aghd-2, all had rhizoid and non-rhizoid colony morphotypes, which we named Aghd-1, Aghd-2, Aghd-1 Aghd-2, and NAghd-1, NAghd-2, and NAghd-1 Aghd-2. However, chitin utilization was not detected in either these mutants or in the wild type. Instead, skimmed milk degradation was observed for the mutants and the wild type; the non-rhizoid strain NAghd-2 exhibited higher degradation activity as revealed by the larger transparent circle on the skimmed milk plate. Using zebrafish as the model organism, we found that non-rhizoid mutants had higher LDs0 values and were less virulent because zebrafish infected with these survived longer. Transcriptome analysis between the non-rhizoid and rhizoid colony morphotypes of each mutant, i.e., NAghd-1 versus （vs） Aghd-1, NAghd-2 vs Aghd-2, and NAghd-1 Aghd-2 vs Aghd-1 Aghd-2, revealed a large number of differentially expressed genes, among which 39 genes were common in three of the pairs compared. Although most of these genes encode hypothetical proteins, a few molecules such as phage tail protein, rhs element Vgr protein, thiol-activated cytolysin, and TonB-dependent outer membrane receptor precursor, expression of which was down-regulated in non-rhizoid mutants but up-regulated in rhizoid mutants, may play a role F. columnare virulence.
文摘Aiming at the shortcomings of the existing automatic colony counter, a set of algorithms based on the principle of image chromatic aberration to achieve colony identification is proposed, and a colony identification device is developed on this basis. The colony identification method is mainly based on the fact that different kinds of colonies and different concentrations of the same kind of colonies have different light-absorbing characteristics, and the judgement of colony types and concentrations is achieved through the method of image processing. The main features of the developed colony recognition equipment are high working efficiency, short recognition and detection time, and the potential of mixed recognition ability of multiple colonies. Therefore, the identification method and equipment have good application and promotion value in agriculture, food, medicine and other industries.
基金supported by the National Natural Science Foundation of China (No. 61203151)the National Basic Research Program of China (973 Program) (No. 2012CB720003)+2 种基金the Postdoctoral Science Foundation of China (20100471044)the Fundamental Research Funds for the Central Universities of China (No. HIT.NSRIF.2013038)the Key Laboratory Opening Funding of China (No. HIT.KLOF.2009071)
文摘This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid rescheduling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances.
文摘The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method for the path planning. In the paper the traditional ant colony algorithm is improved, and measures of keeping optimization, adaptively selecting and adaptively adjusting are applied, by which better path at higher convergence speed can be found. Finally the algorithm is implemented with computer simulation and preferable results are obtained.
基金supported by the National Natural Science Foundation of China(60573159)
文摘Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.
基金supported by the Natural Science Foundation of China (Grant no.60604009)Aeronautical Science Foundation of China (Grant no.2006ZC51039,Beijing NOVA Program Foundation of China (Grant no.2007A017)+1 种基金Open Fund of the Provincial Key Laboratory for Information Processing Technology,Suzhou University (Grant no KJS0821)"New Scientific Star in Blue Sky"Talent Program of Beihang University of China
文摘Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach.
