DV-Hop localization algorithm has greater localization error which estimates distance from an unknown node to the different anchor nodes by using estimated average size of a hop to achieve the location of the unknown ...DV-Hop localization algorithm has greater localization error which estimates distance from an unknown node to the different anchor nodes by using estimated average size of a hop to achieve the location of the unknown node.So an improved DV-Hop localization algorithm based on correctional average size of a hop,HDCDV-Hop algorithm,is proposed.The improved algorithm corrects the estimated distance between the unknown node and different anchor nodes based on fractional hop count information and relatively accurate coordinates of the anchor nodes information,and it uses the improved Differential Evolution algorithm to get the estimate location of unknown nodes so as to further reduce the localization error.Simulation results show that our proposed algorithm have lower localization error and higher localization accuracy compared with the original DV-Hop algorithm and other classical improved algorithms.展开更多
The diversity, adaptation and memory of biological immune system attract much attention of researchers. Several optimal algorithms based on immune system have also been proposed up to now. The distance concentra- tion...The diversity, adaptation and memory of biological immune system attract much attention of researchers. Several optimal algorithms based on immune system have also been proposed up to now. The distance concentra- tion-based artificial immune algorithm (DCAIA) is proposed to overcome defects of the classical artificial immune al- gorithm (CAIA) in this paper. Compared with genetic algorithm (GA) and CAIA, DCAIA is good for solving the prob- lem of precocity,holding the diversity of antibody, and enhancing convergence rate.展开更多
An extension of 2-D assignment approach is proposed for measurement-to-target association for improving multiple targets vector miss distance measurement accuracy. When the multiple targets move so closely, the measur...An extension of 2-D assignment approach is proposed for measurement-to-target association for improving multiple targets vector miss distance measurement accuracy. When the multiple targets move so closely, the measurements can not be fully resolved due to finite resolution. The proposed method adopts an auction algorithm to compute the feasible measurement-to-target assignment with unresolved measurements for solving this 2-D assignment problem. Computer simulation results demonstrate the effectiveness and feasibility of this method.展开更多
Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a dis...Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.展开更多
Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address t...Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address this shortcoming,an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed.The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results.An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation,which significantly improves the efficiency of segmentation.Results from simulation of the liver tumor segmentation challenge(LiTS)dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention,and enable a fast,interactive liver image segmentation that is convenient for doctors.展开更多
With the improvement of automobile ownership in recent years,the incidence of traffic accidents constantly increases and requirements on the security of automobiles become increasingly higher.As science and technology...With the improvement of automobile ownership in recent years,the incidence of traffic accidents constantly increases and requirements on the security of automobiles become increasingly higher.As science and technology develops constantly,the development of automobile automatic obstacle avoidance and cruise system accelerates gradually,and the requirement on distance control becomes stricter.Automobile automatic obstacle avoidance and cruise system can determine the conditions of automobiles and roads using sensing technology,automatically adopt measures to control automobile after discovering road safety hazards,thus to reduce the incidence of traffic accidents.To prevent accidental collision of automobile which are installed with automatic obstacle avoidance and cruise system,active brake should be controlled during driving.This study put forward a neural network based proportional-integral-derivative(PID)control algorithm.The active brake of automobiles was effectively controlled using the system to keep the distance between automobiles.Moreover the algorithm was tested using professional automobile simulation platform.The results demonstrated that neural network based PID control algorithm can precisely and efficiently control the distance between two cars.This work provides a reference for the development of automobile automatic obstacle avoidance and cruise system.展开更多
We continue to consider one of the cybernetic methods in biology related to the study of DNA chains. Exactly, we are considering the problem of reconstructing the distance matrix for DNA chains. Such a matrix is forme...We continue to consider one of the cybernetic methods in biology related to the study of DNA chains. Exactly, we are considering the problem of reconstructing the distance matrix for DNA chains. Such a matrix is formed on the basis of any of the possible algorithms for determining the distances between DNA chains, as well as any specific object of study. At the same time, for example, the practical programming results show that on an average modern computer, it takes about a day to build such a 30 × 30 matrix for mnDNAs using the Needleman-Wunsch algorithm;therefore, for such a 300 × 300 matrix, about 3 months of continuous computer operation is expected. Thus, even for a relatively small number of species, calculating the distance matrix on conventional computers is hardly feasible and the supercomputers are usually not available. Therefore, we started publishing our variants of the algorithms for calculating the distance between two DNA chains, then we publish algorithms for restoring partially filled matrices, i.e., the inverse problem of matrix processing. Previously, we used the method of branches and boundaries, but in this paper we propose to use another new algorithm for restoring the distance matrix for DNA chains. Our recent work has shown that even greater improvement in the quality of the algorithm can often be achieved without improving the auxiliary heuristics of the branches and boundaries method. Thus, we are improving the algorithms that formulate the greedy function of this method only. .展开更多
Most clustering algorithms need to describe the similarity of objects by a predefined distance function. Three distance functions which are widely used in two traditional clustering algorithms k-means and hierarchical...Most clustering algorithms need to describe the similarity of objects by a predefined distance function. Three distance functions which are widely used in two traditional clustering algorithms k-means and hierarchical clustering were investigated. Both theoretical analysis and detailed experimental results were given. It is shown that a distance function greatly affects clustering results and can be used to detect the outlier of a cluster by the comparison of such different results and give the shape information of clusters. In practice situation, it is suggested to use different distance function separately, compare the clustering results and pick out the 搒wing points? And such points may leak out more information for data analysts.展开更多
This paper proposed the scheme of transmission lines distance protection based on differential equation algorithms (DEA) and Hilbert-Huang transform (HHT). The measured impedance based on EDA is affected by various fa...This paper proposed the scheme of transmission lines distance protection based on differential equation algorithms (DEA) and Hilbert-Huang transform (HHT). The measured impedance based on EDA is affected by various factors, such as the distributed capacitance, the transient response characteristics of current transformer and voltage transformer, etc. In order to overcome this problem, the proposed scheme applies HHT to improve the apparent impedance estimated by DEA. Empirical mode decomposition (EMD) is used to decompose the data set from DEA into the intrinsic mode functions (IMF) and the residue. This residue has monotonic trend and is used to evaluate the impedance of faulty line. Simulation results show that the proposed scheme improves significantly the accuracy of the estimated impedance.展开更多
Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algori...Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property,however have poor estimation property and some of them does not suit for target’s variable velocity model.The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model.In this paper distance weight is adopted to estimate the target’s position,which is different from the existing distance weight in other papers.On the analysis of principle of distance weight (DW),prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed.Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property.展开更多
The projection matrix model is used to describe the physical relationship between reconstructed object and projection.Such a model has a strong influence on projection and backprojection,two vital operations in iterat...The projection matrix model is used to describe the physical relationship between reconstructed object and projection.Such a model has a strong influence on projection and backprojection,two vital operations in iterative computed tomographic reconstruction.The distance-driven model（DDM） is a state-of-the-art technology that simulates forward and back projections.This model has a low computational complexity and a relatively high spatial resolution;however,it includes only a few methods in a parallel operation with a matched model scheme.This study introduces a fast and parallelizable algorithm to improve the traditional DDM for computing the parallel projection and backprojection operations.Our proposed model has been implemented on a GPU（graphic processing unit） platform and has achieved satisfactory computational efficiency with no approximation.The runtime for the projection and backprojection operations with our model is approximately 4.5 s and 10.5 s per loop,respectively,with an image size of 256×256×256 and 360 projections with a size of 512×512.We compare several general algorithms that have been proposed for maximizing GPU efficiency by using the unmatched projection/backprojection models in a parallel computation.The imaging resolution is not sacrificed and remains accurate during computed tomographic reconstruction.展开更多
Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflecti...Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflection point method”to analyze specific deviation trajectories,determine deviation thresholds,and identify commonly used deviation paths.By combining multiple similarity metrics,including Euclidean distance,Hausdorff distance,and sector edit distance,with the density-based spatial clustering of applications with noise(DBSCAN)algorithm,the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs.A case study of 23578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework.Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances,with higher silhouette coefficients and lower Davies⁃Bouldin indices,ensuring better intra-cluster compactness and inter-cluster separation.Based on clustering results,19 representative trajectory options are identified,covering both nominal and deviation paths,which significantly enhance route diversity and reflect actual flight practices.This provides a practical basis for optimizing flight paths and scheduling,enhancing the flexibility of route selection for flights between city pairs.展开更多
The function of dynamic track drawing realized in the ship monitoring system. Based on the function, we could draw the sailing tracks dynamically according to the ship＇s orientation. Two kernel algorithms are involve...The function of dynamic track drawing realized in the ship monitoring system. Based on the function, we could draw the sailing tracks dynamically according to the ship＇s orientation. Two kernel algorithms are involved during the system developing process, i.e. the algorithms of angular deflection and distance interval value. The practice of system development shows that the proper application of these two algorithms has good effect＇in the visualization of sailing track.展开更多
In this paper, we report research on how to design the tele-network. First of all, we defined the reliability of tele-network. According to the definition, we divide the whole reliability into two parts:the reliabilit...In this paper, we report research on how to design the tele-network. First of all, we defined the reliability of tele-network. According to the definition, we divide the whole reliability into two parts:the reliability of the mini-way and that of the whole system. Then we do algebra unintersection of the mini-way, deriving a function of reliability of tele-network. Also, we got a function of the cost of tele-network after analyzing the cost of arcs and points. Finally, we give a mathematical model to design a tele-network. For the algorithm, we define the distance of a network and adjacent area within certain boundaries . We present a new algorithm--Queue Competition Algorithm (QCA) based on the adjacent area . The QCA correlates sequence of fitnesses in their father-generations with hunting zone of mutation and the number of individuals generated by mutation, making the stronger fitness in a small zone converge at a local extreme value, but the weaker one takes the advantage of lots of individuals and a big zone to hunt a new local extreme value. In this way, we get the overall extreme value. Numerical simulation shows that we can get the efficient hunting and exact solution by using QCA. The QCA efficient hunting and exact solution.展开更多
基金supported by Fundamental Research Funds of Jilin University(No.SXGJQY2017-9,No.2017TD-19)the National Natural Science Foundation of China(No.61771219)
文摘DV-Hop localization algorithm has greater localization error which estimates distance from an unknown node to the different anchor nodes by using estimated average size of a hop to achieve the location of the unknown node.So an improved DV-Hop localization algorithm based on correctional average size of a hop,HDCDV-Hop algorithm,is proposed.The improved algorithm corrects the estimated distance between the unknown node and different anchor nodes based on fractional hop count information and relatively accurate coordinates of the anchor nodes information,and it uses the improved Differential Evolution algorithm to get the estimate location of unknown nodes so as to further reduce the localization error.Simulation results show that our proposed algorithm have lower localization error and higher localization accuracy compared with the original DV-Hop algorithm and other classical improved algorithms.
文摘The diversity, adaptation and memory of biological immune system attract much attention of researchers. Several optimal algorithms based on immune system have also been proposed up to now. The distance concentra- tion-based artificial immune algorithm (DCAIA) is proposed to overcome defects of the classical artificial immune al- gorithm (CAIA) in this paper. Compared with genetic algorithm (GA) and CAIA, DCAIA is good for solving the prob- lem of precocity,holding the diversity of antibody, and enhancing convergence rate.
文摘An extension of 2-D assignment approach is proposed for measurement-to-target association for improving multiple targets vector miss distance measurement accuracy. When the multiple targets move so closely, the measurements can not be fully resolved due to finite resolution. The proposed method adopts an auction algorithm to compute the feasible measurement-to-target assignment with unresolved measurements for solving this 2-D assignment problem. Computer simulation results demonstrate the effectiveness and feasibility of this method.
基金supported by the National Natural Science Foundation of China(61890930-5,61533002,61603012)the Major Science and Technology Program for Water Pollution Control and Treatment of China(2018ZX07111005)+1 种基金the National Key Research and Development Project(2018YFC1900800-5)Beijing Municipal Education Commission Foundation(KM201710005025)
文摘Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.
基金the Project of China Scholarship Council(No.201708615011)the Xi’an Science and Technology Plan Project(No.GXYD1.7)。
文摘Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address this shortcoming,an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed.The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results.An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation,which significantly improves the efficiency of segmentation.Results from simulation of the liver tumor segmentation challenge(LiTS)dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention,and enable a fast,interactive liver image segmentation that is convenient for doctors.
文摘With the improvement of automobile ownership in recent years,the incidence of traffic accidents constantly increases and requirements on the security of automobiles become increasingly higher.As science and technology develops constantly,the development of automobile automatic obstacle avoidance and cruise system accelerates gradually,and the requirement on distance control becomes stricter.Automobile automatic obstacle avoidance and cruise system can determine the conditions of automobiles and roads using sensing technology,automatically adopt measures to control automobile after discovering road safety hazards,thus to reduce the incidence of traffic accidents.To prevent accidental collision of automobile which are installed with automatic obstacle avoidance and cruise system,active brake should be controlled during driving.This study put forward a neural network based proportional-integral-derivative(PID)control algorithm.The active brake of automobiles was effectively controlled using the system to keep the distance between automobiles.Moreover the algorithm was tested using professional automobile simulation platform.The results demonstrated that neural network based PID control algorithm can precisely and efficiently control the distance between two cars.This work provides a reference for the development of automobile automatic obstacle avoidance and cruise system.
文摘We continue to consider one of the cybernetic methods in biology related to the study of DNA chains. Exactly, we are considering the problem of reconstructing the distance matrix for DNA chains. Such a matrix is formed on the basis of any of the possible algorithms for determining the distances between DNA chains, as well as any specific object of study. At the same time, for example, the practical programming results show that on an average modern computer, it takes about a day to build such a 30 × 30 matrix for mnDNAs using the Needleman-Wunsch algorithm;therefore, for such a 300 × 300 matrix, about 3 months of continuous computer operation is expected. Thus, even for a relatively small number of species, calculating the distance matrix on conventional computers is hardly feasible and the supercomputers are usually not available. Therefore, we started publishing our variants of the algorithms for calculating the distance between two DNA chains, then we publish algorithms for restoring partially filled matrices, i.e., the inverse problem of matrix processing. Previously, we used the method of branches and boundaries, but in this paper we propose to use another new algorithm for restoring the distance matrix for DNA chains. Our recent work has shown that even greater improvement in the quality of the algorithm can often be achieved without improving the auxiliary heuristics of the branches and boundaries method. Thus, we are improving the algorithms that formulate the greedy function of this method only. .
文摘Most clustering algorithms need to describe the similarity of objects by a predefined distance function. Three distance functions which are widely used in two traditional clustering algorithms k-means and hierarchical clustering were investigated. Both theoretical analysis and detailed experimental results were given. It is shown that a distance function greatly affects clustering results and can be used to detect the outlier of a cluster by the comparison of such different results and give the shape information of clusters. In practice situation, it is suggested to use different distance function separately, compare the clustering results and pick out the 搒wing points? And such points may leak out more information for data analysts.
文摘This paper proposed the scheme of transmission lines distance protection based on differential equation algorithms (DEA) and Hilbert-Huang transform (HHT). The measured impedance based on EDA is affected by various factors, such as the distributed capacitance, the transient response characteristics of current transformer and voltage transformer, etc. In order to overcome this problem, the proposed scheme applies HHT to improve the apparent impedance estimated by DEA. Empirical mode decomposition (EMD) is used to decompose the data set from DEA into the intrinsic mode functions (IMF) and the residue. This residue has monotonic trend and is used to evaluate the impedance of faulty line. Simulation results show that the proposed scheme improves significantly the accuracy of the estimated impedance.
基金This work is supported by The National Science Fund for Distinguished Young Scholars （60725105） National Basic Research Program of China （973 Program） （2009CB320404）+5 种基金 Program for Changjiang Scholars and Innovative Research Team in University （IRT0852） The National Natural Science Foundation of China （60972048, 61072068） The Special Fund of State Key Laboratory （ISN01080301） The Major program of National Science and Technology （2009ZX03007- 004） Supported by the 111 Project （B08038） The Key Project of Chinese Ministry of Education （107103）.
文摘Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property,however have poor estimation property and some of them does not suit for target’s variable velocity model.The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model.In this paper distance weight is adopted to estimate the target’s position,which is different from the existing distance weight in other papers.On the analysis of principle of distance weight (DW),prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed.Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property.
基金supported by the National High Technology Research and Development Program of China(Grant No.2012AA011603)the National Natural Science Foundation of China(Grant No.61372172)
文摘The projection matrix model is used to describe the physical relationship between reconstructed object and projection.Such a model has a strong influence on projection and backprojection,two vital operations in iterative computed tomographic reconstruction.The distance-driven model（DDM） is a state-of-the-art technology that simulates forward and back projections.This model has a low computational complexity and a relatively high spatial resolution;however,it includes only a few methods in a parallel operation with a matched model scheme.This study introduces a fast and parallelizable algorithm to improve the traditional DDM for computing the parallel projection and backprojection operations.Our proposed model has been implemented on a GPU（graphic processing unit） platform and has achieved satisfactory computational efficiency with no approximation.The runtime for the projection and backprojection operations with our model is approximately 4.5 s and 10.5 s per loop,respectively,with an image size of 256×256×256 and 360 projections with a size of 512×512.We compare several general algorithms that have been proposed for maximizing GPU efficiency by using the unmatched projection/backprojection models in a parallel computation.The imaging resolution is not sacrificed and remains accurate during computed tomographic reconstruction.
基金supported in part by Boeing Company and Nanjing University of Aeronautics and Astronautics(NUAA)through the Research on Decision Support Technology of Air Traffic Operation Management in Convective Weather under Project 2022-GT-129in part by the Postgraduate Research and Practice Innovation Program of NUAA(No.xcxjh20240709)。
文摘Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflection point method”to analyze specific deviation trajectories,determine deviation thresholds,and identify commonly used deviation paths.By combining multiple similarity metrics,including Euclidean distance,Hausdorff distance,and sector edit distance,with the density-based spatial clustering of applications with noise(DBSCAN)algorithm,the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs.A case study of 23578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework.Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances,with higher silhouette coefficients and lower Davies⁃Bouldin indices,ensuring better intra-cluster compactness and inter-cluster separation.Based on clustering results,19 representative trajectory options are identified,covering both nominal and deviation paths,which significantly enhance route diversity and reflect actual flight practices.This provides a practical basis for optimizing flight paths and scheduling,enhancing the flexibility of route selection for flights between city pairs.
文摘The function of dynamic track drawing realized in the ship monitoring system. Based on the function, we could draw the sailing tracks dynamically according to the ship＇s orientation. Two kernel algorithms are involved during the system developing process, i.e. the algorithms of angular deflection and distance interval value. The practice of system development shows that the proper application of these two algorithms has good effect＇in the visualization of sailing track.
基金Supported by the National Natura1 Science Foundation of China(70071042,60073043,60133010)
文摘In this paper, we report research on how to design the tele-network. First of all, we defined the reliability of tele-network. According to the definition, we divide the whole reliability into two parts:the reliability of the mini-way and that of the whole system. Then we do algebra unintersection of the mini-way, deriving a function of reliability of tele-network. Also, we got a function of the cost of tele-network after analyzing the cost of arcs and points. Finally, we give a mathematical model to design a tele-network. For the algorithm, we define the distance of a network and adjacent area within certain boundaries . We present a new algorithm--Queue Competition Algorithm (QCA) based on the adjacent area . The QCA correlates sequence of fitnesses in their father-generations with hunting zone of mutation and the number of individuals generated by mutation, making the stronger fitness in a small zone converge at a local extreme value, but the weaker one takes the advantage of lots of individuals and a big zone to hunt a new local extreme value. In this way, we get the overall extreme value. Numerical simulation shows that we can get the efficient hunting and exact solution by using QCA. The QCA efficient hunting and exact solution.
