Directed at the problem of occlusion in target tracking,a new improved algorithm based on the Meanshift algorithm and Kalman filter is proposed.The algorithm effectively combines the Meanshift algorithm with the Kalma...Directed at the problem of occlusion in target tracking,a new improved algorithm based on the Meanshift algorithm and Kalman filter is proposed.The algorithm effectively combines the Meanshift algorithm with the Kalman filtering algorithm to determine the position of the target centroid and subsequently adjust the current search window adaptively according to the target centroid position and the previous frame search window boundary.The derived search window is more closely matched to the location of the target,which improves the accuracy and reliability of tracking.The environmental influence and other influencing factors on the algorithm are also reduced.Through comparison and analysis of the experiments,the modified algorithm demonstrates good stability and adaptability,and can effectively solve the problem of large area occlusion and similar interference.展开更多
Extended Kalman Filter(EKF)algorithm is widely used in parameter estimation for nonlinear systems.The estimation precision is sensitively dependent on EKF’s initial state covariance matrix and state noise matrix.The ...Extended Kalman Filter(EKF)algorithm is widely used in parameter estimation for nonlinear systems.The estimation precision is sensitively dependent on EKF’s initial state covariance matrix and state noise matrix.The grid optimization method is always used to find proper initial matrix for off-line estimation.However,the grid method has the draw back being time consuming hence,coarse grid followed by a fine grid method is adopted.To further improve efficiency without the loss of estimation accuracy,we propose a genetic algorithm for the coarse grid optimization in this paper.It is recognized that the crossover rate and mutation rate are the main influencing factors for the performance of the genetic algorithm,so sensitivity experiments for these two factors are carried out and a set of genetic algorithm parameters with good adaptability were selected by testing with several gyros’experimental data.Experimental results show that the proposed algorithm has higher efficiency and better estimation accuracy than the traversing grid algorithm.展开更多
The multi-robot coverage motion planning(MCMP) problem in which every reachable area must be covered is common in multi-robot systems. To deal with the MCMP problem, we propose an efficient, complete, and off-line alg...The multi-robot coverage motion planning(MCMP) problem in which every reachable area must be covered is common in multi-robot systems. To deal with the MCMP problem, we propose an efficient, complete, and off-line algorithm, named the 'auction-based spanning tree coverage(A-STC)' algorithm. First, the configuration space is divided into mega cells whose size is twice the minimum coverage range of a robot. Based on connection relationships among mega cells, a graph structure can be obtained. A robot that circumnavigates a spanning tree of the graph can generate a coverage trajectory. Then, the proposed algorithm adopts an auction mechanism to construct one spanning tree for each robot. In this mechanism, an auctioneer robot chooses a suitable vertex of the graph as an auction item from neighboring vertexes of its spanning tree by heuristic rules. A bidder robot submits a proper bid to the auctioneer according to the auction vertexes’ relationships with the spanning tree of the robot and the estimated length of its trajectory. The estimated length is calculated based on vertexes and edges in the spanning tree. The bidder with the highest bid is selected as a winner to reduce the makespan of the coverage task. After auction processes, acceptable coverage trajectories can be planned rapidly. Computational experiments validate the effectiveness of the proposed MCMP algorithm and the method for estimating trajectory lengths. The proposed algorithm is also compared with the state-of-the-art algorithms. The comparative results show that the A-STC algorithm has apparent advantages in terms of the running time and the makespan for large crowded configuration spaces.展开更多
In conventional technical trajectory correction schemes, continuous attitude adjusting mechanisms, such as canards, are inferior in terms of response time and efficiency of executing instructions. Discontinuous attitu...In conventional technical trajectory correction schemes, continuous attitude adjusting mechanisms, such as canards, are inferior in terms of response time and efficiency of executing instructions. Discontinuous attitude adjusting mechanisms, such as the lateral pulse jet, have complex impact on the airflow layer of the projectile surface caused by the thrust vector jet flow. An improved two-dimensional trajectory correction mechanism is designed based on the principle of firing mass blocks by a tailor-made propellant. The mechanical properties of the thrust force (namely the correction force) is analyzed. The trajectory correction model is established to analyze the effects of correction starting moment and correction phase angle of a thrust force on the projectile's trajectory. According to the trajectory correction scheme, an improved genetic algorithm is employed to this work. The scheme is tested in the simulation. The results show that the correction scheme is effective to reduce target dispersion and increase the precision of the impact point.展开更多
Bird swarm algorithm(BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimi...Bird swarm algorithm(BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimization problems. To enhance the performance of BSA, handling boundary constraints are applied to fix the candidate solutions that are out of boundary or on the boundary in iterations, which can boost the diversity of the swarm to avoid the premature problem. On the other hand, we accelerate the foraging behavior by adjusting the cognitive and social components the sin cosine coefficients. Simulation results and comparison based on sixty benchmark functions demonstrate that the improved BSA has superior performance over the BSA in terms of almost all functions.展开更多
Given a set of radio broadcast programs, the radio broadcast scheduling problem is to allocate a set of devices to transmit the programs to achieve the optimal sound quality. In this article, we propose a complete alg...Given a set of radio broadcast programs, the radio broadcast scheduling problem is to allocate a set of devices to transmit the programs to achieve the optimal sound quality. In this article, we propose a complete algorithm to solve the problem, which is based on a branch-and-bound(BnB) algorithm. We formulate the problem with a new model, called constrained maximum weighted bipartite matching(CMBM),i.e., the maximum matching problem on a weighted bipartite graph with constraints. For the reduced matching problem, we propose a novel BnB algorithm by introducing three new strategies, including the highest quality first, the least conflict first and the more edge first. We also establish an upper bound estimating function for pruning the search space of the algorithm. The experimental results show that our new algorithm can quickly find the optimal solution for the radio broadcast scheduling problem at small scales, and has higher scalability for the problems at large scales than the existing complete algorithm.展开更多
A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time–height clustering me...A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time–height clustering method and the k-means clustering method. The cloud classification algorithm, developed using a fuzzy logic method, uses nine physical parameters to classify clouds into nine types: cirrostratus, cirrocumulus, altocumulus, altostratus, stratus, stratocumulus, nimbostratus,cumulus or cumulonimbus. The performance of the clustering and classification algorithm is presented by comparison with all-sky images taken from January to June 2014. Overall, 92% of the cloud profiles are clustered successfully and the agreement in classification between the radar system and the all-sky imager is 87%. The distribution of cloud types in Beijing from January 2014 to December 2017 is studied based on the clustering and classification algorithm. The statistics show that cirrostratus clouds have the highest occurrence frequency(24%) among the nine cloud types. High-level clouds have the maximum occurrence frequency and low-level clouds the minimum occurrence frequency.展开更多
Submerged arc welding(SAW),owing to its high deposition rate and high welding quality,is widely used in the fabrication of pressure vessel,marine vessel,pipelines and offshore structures.However,selection of an optimu...Submerged arc welding(SAW),owing to its high deposition rate and high welding quality,is widely used in the fabrication of pressure vessel,marine vessel,pipelines and offshore structures.However,selection of an optimum combination of welding parameters is critical in achieving high weld quality and productivity.In this work,initially,the SAWexperiments were performed using fractional factorial design to analyze the effect of direct and indirect input parameters,namely,welding voltage,wire feed rate,welding speed,nozzle to plate distance,flux condition,and plate thickness on weld bead geometrical responses viz.bead width,reinforcement,and penetration.The bead on plate technique was used to deposit weld metal on AISI 1023 steel plates.The effect of SAW input parameters on response variables were analyzed using main and interaction effects.The linear regression was used to develop the mathematical models for the response variable.Then,the multi-objective optimization of input parameters was carried out using desirability approach,genetic algorithm and Jaya algorithm.The Jaya algorithm offered better optimization results as compared to desirability approach,genetic algorithm.展开更多
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
Nowadays,several stern devices are attracting a great deal of attention.The control surface is an effective apparatus for improving the hydrodynamic performance of planing hulls and is considered an important element ...Nowadays,several stern devices are attracting a great deal of attention.The control surface is an effective apparatus for improving the hydrodynamic performance of planing hulls and is considered an important element in the design of planing hulls.Control surfaces produce forces and a pitching moment due to the pressure distribution that they cause,which can be used to change the running state of high-speed marine boats.This work elaborates a new study to evaluate the hydrodynamic performance of a planing boat with a trim tab and an interceptor,and optimizes them by using an optimization algorithm.The trim tab and the interceptor have been used to optimize the running trim and motion control of semi-planing and planing boats at various speeds and sea conditions for many years.In this paper,the usage of trim tab is mathematically verified and experimental equations are utilized to optimize the performance of a planing boat at a specificd trim angle by using an optimization algorithm.The genetic algorithm(GA)is one of the most useful optimizing methods and is used in this study.The planing boat equations were programmed according to Savitsky’s equations and then analyzed in the framework of the GA-based optimization for performance improvement of theplaning hull.The optimal design of trim tab and interceptor for planing boat can be considered a multiobjective problem.The input data of GA include different parameters,such as speed,longitudinal center of gravity,and deadrise angle.We can extract the best range of forecasting the planing boat longitudinal center of gravity,the angle of the trim,and the least drag force at the best trim angle of the boat.展开更多
This study focuses on the problem of handoff minimization for a set of users moving in a wireless network.This problem is analyzed by considering two cases for the user's movement under access point capacity const...This study focuses on the problem of handoff minimization for a set of users moving in a wireless network.This problem is analyzed by considering two cases for the user's movement under access point capacity constraints:1)all users move together,and 2)each user can have their chosen path within the network.In the first case,we propose an optimal competitive ratio algorithm for the problem.However,in the second case,having the connectivity assumption,that is,"if a user is connected to an access point so long that the received signal strength of the access point is not less than a specified threshold,the user should continue his/her connection",we prove that no approach can reduce the number of unnecessary handoffs in an offline setting.However,without connectivity assumption,we present an optimal deterministic algorithm with the competitive ratio of n?for this problem under online setting,where n is the number of users and?is the maximum number of access points which cover any single point in the environment.Also,we prove that the randomized version of the algorithm achieves an expected competitive ratio of O(log△).展开更多
Railway marshalling transportation is a crucial part of enterprise production supply chain, with the development of national economy;enterprises face more and more pressure on station railway marshalling operation. Re...Railway marshalling transportation is a crucial part of enterprise production supply chain, with the development of national economy;enterprises face more and more pressure on station railway marshalling operation. Realizing enterprise railway dispatching plan automatically by computer, which can improve the level of the station scheduling and transport efficiency, at the same time can reduce the scheduling cost. Based on the basic rules of marshalling and dispatching of railway freight trains at enterprise stations, this paper investigates the site of special railway line at enterprise stations and establishes the space of dispatching state and regulation base according to the actual situation. The information feedback model is designed according to the train information, carriage information and real-time information of the track of the station. Based on the analysis of the railway regulation and the demand of the station, establish the scheduling rule method library. Based on the state space and feedback model of the station, using the scheduling rule method library, this paper designs an enterprise railway automatic marshalling algorithm with a certain universality, and realizes automatic train marshalling and scheduling operation. Considering the economic benefit of the station and the efficiency of the marshalling model, this paper introduces the time cost function and applies the improved greedy algorithm to optimize the automatic marshalling model, realizing the optimal marshalling of railway station in a short time.展开更多
Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system ...Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.展开更多
This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-object...This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.展开更多
Aiming at the problems of premature convergence and easily falling into local optimums of the antlion optimization algorithm, a chaos antlion optimization algorithm based on the chaos search is proposed. Firstly, in t...Aiming at the problems of premature convergence and easily falling into local optimums of the antlion optimization algorithm, a chaos antlion optimization algorithm based on the chaos search is proposed. Firstly, in the algorithm, the population is initialized by using the tent chaotic mapping, and the self-adaptive dynamic adjustment of chaotic search scopes is proposed in order to improve the overall fitness and the optimization efficiency of the population. Then, the tournament strategy is used to select antlions. Finally, the logistic chaos operator is used to optimize the random walk of ants, which forms a global and local parallel search mode with the antlion’s foraging behavior. The performance algorithm is tested through 13 complex high-dimensional benchmark functions and three dimensional path planning problems. The experimental results of six complex high-dimensional benchmark functions show that the presented algorithm has a better convergence speed and precision than the standard antlion algorithm and other optimization algorithms, and is suitable for the optimization of complex high dimensional functions. The trajectory planning experimental results show that compared with the antlion optimizer(ALO) algorithm, grey wolf optimizer(GWO), particle swarm optimization(PSO) and artificial bee colony(ABC) algorithm, it has advantages in speed and accuracy to obtain a specific path, and it is of great value in actual problems.展开更多
Social networks are important media for spreading information, ideas, and influence among individuals.Most existing research focuses on understanding the characteristics of social networks, investigating how informati...Social networks are important media for spreading information, ideas, and influence among individuals.Most existing research focuses on understanding the characteristics of social networks, investigating how information is spread through the 'word-of-mouth' effect of social networks, or exploring social influences among individuals and groups. However, most studies ignore negative influences among individuals and groups. Motivated by the goal of alleviating social problems, such as drinking, smoking, and gambling, and influence-spreading problems, such as promoting new products, we consider positive and negative influences, and propose a new optimization problem called the Minimum-sized Positive Influential Node Set(MPINS) selection problem to identify the minimum set of influential nodes such that every node in the network can be positively influenced by these selected nodes with no less than a threshold of ?. Our contributions are threefold. First, we prove that, under the independent cascade model considering positive and negative influences, MPINS is APX-hard. Subsequently, we present a greedy approximation algorithm to address the MPINS selection problem. Finally, to validate the proposed greedy algorithm, we conduct extensive simulations and experiments on random graphs and seven different realworld data sets that represent small-, medium-, and large-scale networks.展开更多
基金the Scholarship of China Scholarship Council(CSC)(201606935043).
文摘Directed at the problem of occlusion in target tracking,a new improved algorithm based on the Meanshift algorithm and Kalman filter is proposed.The algorithm effectively combines the Meanshift algorithm with the Kalman filtering algorithm to determine the position of the target centroid and subsequently adjust the current search window adaptively according to the target centroid position and the previous frame search window boundary.The derived search window is more closely matched to the location of the target,which improves the accuracy and reliability of tracking.The environmental influence and other influencing factors on the algorithm are also reduced.Through comparison and analysis of the experiments,the modified algorithm demonstrates good stability and adaptability,and can effectively solve the problem of large area occlusion and similar interference.
文摘Extended Kalman Filter(EKF)algorithm is widely used in parameter estimation for nonlinear systems.The estimation precision is sensitively dependent on EKF’s initial state covariance matrix and state noise matrix.The grid optimization method is always used to find proper initial matrix for off-line estimation.However,the grid method has the draw back being time consuming hence,coarse grid followed by a fine grid method is adopted.To further improve efficiency without the loss of estimation accuracy,we propose a genetic algorithm for the coarse grid optimization in this paper.It is recognized that the crossover rate and mutation rate are the main influencing factors for the performance of the genetic algorithm,so sensitivity experiments for these two factors are carried out and a set of genetic algorithm parameters with good adaptability were selected by testing with several gyros’experimental data.Experimental results show that the proposed algorithm has higher efficiency and better estimation accuracy than the traversing grid algorithm.
基金the National Natural Science Foundation of China(Nos.61822304,61673058,and 61621063)the Project of Major International(Regional)Joint Research Program NSFC(No.61720106011)the NSFC–Zhejiang Joint Fund for the Integration of Industrialization and Informationization(No.U1609214).
文摘The multi-robot coverage motion planning(MCMP) problem in which every reachable area must be covered is common in multi-robot systems. To deal with the MCMP problem, we propose an efficient, complete, and off-line algorithm, named the 'auction-based spanning tree coverage(A-STC)' algorithm. First, the configuration space is divided into mega cells whose size is twice the minimum coverage range of a robot. Based on connection relationships among mega cells, a graph structure can be obtained. A robot that circumnavigates a spanning tree of the graph can generate a coverage trajectory. Then, the proposed algorithm adopts an auction mechanism to construct one spanning tree for each robot. In this mechanism, an auctioneer robot chooses a suitable vertex of the graph as an auction item from neighboring vertexes of its spanning tree by heuristic rules. A bidder robot submits a proper bid to the auctioneer according to the auction vertexes’ relationships with the spanning tree of the robot and the estimated length of its trajectory. The estimated length is calculated based on vertexes and edges in the spanning tree. The bidder with the highest bid is selected as a winner to reduce the makespan of the coverage task. After auction processes, acceptable coverage trajectories can be planned rapidly. Computational experiments validate the effectiveness of the proposed MCMP algorithm and the method for estimating trajectory lengths. The proposed algorithm is also compared with the state-of-the-art algorithms. The comparative results show that the A-STC algorithm has apparent advantages in terms of the running time and the makespan for large crowded configuration spaces.
基金National Natural Science Foundation of China (11372142).
文摘In conventional technical trajectory correction schemes, continuous attitude adjusting mechanisms, such as canards, are inferior in terms of response time and efficiency of executing instructions. Discontinuous attitude adjusting mechanisms, such as the lateral pulse jet, have complex impact on the airflow layer of the projectile surface caused by the thrust vector jet flow. An improved two-dimensional trajectory correction mechanism is designed based on the principle of firing mass blocks by a tailor-made propellant. The mechanical properties of the thrust force (namely the correction force) is analyzed. The trajectory correction model is established to analyze the effects of correction starting moment and correction phase angle of a thrust force on the projectile's trajectory. According to the trajectory correction scheme, an improved genetic algorithm is employed to this work. The scheme is tested in the simulation. The results show that the correction scheme is effective to reduce target dispersion and increase the precision of the impact point.
基金the National Natural Science Foundation of China(11871383,71471140 and 11771058).
文摘Bird swarm algorithm(BSA), a novel bio-inspired algorithm, has good performance in solving numerical optimization problems. In this paper, a new improved bird swarm algorithm is conducted to solve unconstrained optimization problems. To enhance the performance of BSA, handling boundary constraints are applied to fix the candidate solutions that are out of boundary or on the boundary in iterations, which can boost the diversity of the swarm to avoid the premature problem. On the other hand, we accelerate the foraging behavior by adjusting the cognitive and social components the sin cosine coefficients. Simulation results and comparison based on sixty benchmark functions demonstrate that the improved BSA has superior performance over the BSA in terms of almost all functions.
基金National Natural Science Foundation of China(Grant No.61772503)National Basic Research Program of China(Grant No.2014CB340302).
文摘Given a set of radio broadcast programs, the radio broadcast scheduling problem is to allocate a set of devices to transmit the programs to achieve the optimal sound quality. In this article, we propose a complete algorithm to solve the problem, which is based on a branch-and-bound(BnB) algorithm. We formulate the problem with a new model, called constrained maximum weighted bipartite matching(CMBM),i.e., the maximum matching problem on a weighted bipartite graph with constraints. For the reduced matching problem, we propose a novel BnB algorithm by introducing three new strategies, including the highest quality first, the least conflict first and the more edge first. We also establish an upper bound estimating function for pruning the search space of the algorithm. The experimental results show that our new algorithm can quickly find the optimal solution for the radio broadcast scheduling problem at small scales, and has higher scalability for the problems at large scales than the existing complete algorithm.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41775032 and 41275040).
文摘A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time–height clustering method and the k-means clustering method. The cloud classification algorithm, developed using a fuzzy logic method, uses nine physical parameters to classify clouds into nine types: cirrostratus, cirrocumulus, altocumulus, altostratus, stratus, stratocumulus, nimbostratus,cumulus or cumulonimbus. The performance of the clustering and classification algorithm is presented by comparison with all-sky images taken from January to June 2014. Overall, 92% of the cloud profiles are clustered successfully and the agreement in classification between the radar system and the all-sky imager is 87%. The distribution of cloud types in Beijing from January 2014 to December 2017 is studied based on the clustering and classification algorithm. The statistics show that cirrostratus clouds have the highest occurrence frequency(24%) among the nine cloud types. High-level clouds have the maximum occurrence frequency and low-level clouds the minimum occurrence frequency.
文摘Submerged arc welding(SAW),owing to its high deposition rate and high welding quality,is widely used in the fabrication of pressure vessel,marine vessel,pipelines and offshore structures.However,selection of an optimum combination of welding parameters is critical in achieving high weld quality and productivity.In this work,initially,the SAWexperiments were performed using fractional factorial design to analyze the effect of direct and indirect input parameters,namely,welding voltage,wire feed rate,welding speed,nozzle to plate distance,flux condition,and plate thickness on weld bead geometrical responses viz.bead width,reinforcement,and penetration.The bead on plate technique was used to deposit weld metal on AISI 1023 steel plates.The effect of SAW input parameters on response variables were analyzed using main and interaction effects.The linear regression was used to develop the mathematical models for the response variable.Then,the multi-objective optimization of input parameters was carried out using desirability approach,genetic algorithm and Jaya algorithm.The Jaya algorithm offered better optimization results as compared to desirability approach,genetic algorithm.
基金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Nowadays,several stern devices are attracting a great deal of attention.The control surface is an effective apparatus for improving the hydrodynamic performance of planing hulls and is considered an important element in the design of planing hulls.Control surfaces produce forces and a pitching moment due to the pressure distribution that they cause,which can be used to change the running state of high-speed marine boats.This work elaborates a new study to evaluate the hydrodynamic performance of a planing boat with a trim tab and an interceptor,and optimizes them by using an optimization algorithm.The trim tab and the interceptor have been used to optimize the running trim and motion control of semi-planing and planing boats at various speeds and sea conditions for many years.In this paper,the usage of trim tab is mathematically verified and experimental equations are utilized to optimize the performance of a planing boat at a specificd trim angle by using an optimization algorithm.The genetic algorithm(GA)is one of the most useful optimizing methods and is used in this study.The planing boat equations were programmed according to Savitsky’s equations and then analyzed in the framework of the GA-based optimization for performance improvement of theplaning hull.The optimal design of trim tab and interceptor for planing boat can be considered a multiobjective problem.The input data of GA include different parameters,such as speed,longitudinal center of gravity,and deadrise angle.We can extract the best range of forecasting the planing boat longitudinal center of gravity,the angle of the trim,and the least drag force at the best trim angle of the boat.
文摘This study focuses on the problem of handoff minimization for a set of users moving in a wireless network.This problem is analyzed by considering two cases for the user's movement under access point capacity constraints:1)all users move together,and 2)each user can have their chosen path within the network.In the first case,we propose an optimal competitive ratio algorithm for the problem.However,in the second case,having the connectivity assumption,that is,"if a user is connected to an access point so long that the received signal strength of the access point is not less than a specified threshold,the user should continue his/her connection",we prove that no approach can reduce the number of unnecessary handoffs in an offline setting.However,without connectivity assumption,we present an optimal deterministic algorithm with the competitive ratio of n?for this problem under online setting,where n is the number of users and?is the maximum number of access points which cover any single point in the environment.Also,we prove that the randomized version of the algorithm achieves an expected competitive ratio of O(log△).
文摘Railway marshalling transportation is a crucial part of enterprise production supply chain, with the development of national economy;enterprises face more and more pressure on station railway marshalling operation. Realizing enterprise railway dispatching plan automatically by computer, which can improve the level of the station scheduling and transport efficiency, at the same time can reduce the scheduling cost. Based on the basic rules of marshalling and dispatching of railway freight trains at enterprise stations, this paper investigates the site of special railway line at enterprise stations and establishes the space of dispatching state and regulation base according to the actual situation. The information feedback model is designed according to the train information, carriage information and real-time information of the track of the station. Based on the analysis of the railway regulation and the demand of the station, establish the scheduling rule method library. Based on the state space and feedback model of the station, using the scheduling rule method library, this paper designs an enterprise railway automatic marshalling algorithm with a certain universality, and realizes automatic train marshalling and scheduling operation. Considering the economic benefit of the station and the efficiency of the marshalling model, this paper introduces the time cost function and applies the improved greedy algorithm to optimize the automatic marshalling model, realizing the optimal marshalling of railway station in a short time.
文摘Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.
基金the National Key Research and Development Program of China (2016YFD0700605)the Fundamental Research Funds for the Central Universities (JZ2016HGBZ1035)the Anhui University Natural Science Research Project (KJ2017A891).
文摘This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.
基金the National Natural Science Foundation of China (61503409)the Aeronautical Science Foundation of China (20155196022)the Natural Science Foundation of Shanxi Province (2016JQ6050).
文摘Aiming at the problems of premature convergence and easily falling into local optimums of the antlion optimization algorithm, a chaos antlion optimization algorithm based on the chaos search is proposed. Firstly, in the algorithm, the population is initialized by using the tent chaotic mapping, and the self-adaptive dynamic adjustment of chaotic search scopes is proposed in order to improve the overall fitness and the optimization efficiency of the population. Then, the tournament strategy is used to select antlions. Finally, the logistic chaos operator is used to optimize the random walk of ants, which forms a global and local parallel search mode with the antlion’s foraging behavior. The performance algorithm is tested through 13 complex high-dimensional benchmark functions and three dimensional path planning problems. The experimental results of six complex high-dimensional benchmark functions show that the presented algorithm has a better convergence speed and precision than the standard antlion algorithm and other optimization algorithms, and is suitable for the optimization of complex high dimensional functions. The trajectory planning experimental results show that compared with the antlion optimizer(ALO) algorithm, grey wolf optimizer(GWO), particle swarm optimization(PSO) and artificial bee colony(ABC) algorithm, it has advantages in speed and accuracy to obtain a specific path, and it is of great value in actual problems.
基金funded in part by the Kennesaw State University College of Science and Mathematics Interdisciplinary Research Opportunities(IDROP)Programthe Provincial Key Research and Development Program of Zhejiang,China(No.2016C01G2010916)+1 种基金the Fundamental Research Funds for the Central Universities,the Alibaba-Zhejiang University Joint Research Institute for Frontier Technologies(A.Z.F.T.)(No.XT622017000118)the CCF-Tencent Open Research Fund(No.AGR20160109).
文摘Social networks are important media for spreading information, ideas, and influence among individuals.Most existing research focuses on understanding the characteristics of social networks, investigating how information is spread through the 'word-of-mouth' effect of social networks, or exploring social influences among individuals and groups. However, most studies ignore negative influences among individuals and groups. Motivated by the goal of alleviating social problems, such as drinking, smoking, and gambling, and influence-spreading problems, such as promoting new products, we consider positive and negative influences, and propose a new optimization problem called the Minimum-sized Positive Influential Node Set(MPINS) selection problem to identify the minimum set of influential nodes such that every node in the network can be positively influenced by these selected nodes with no less than a threshold of ?. Our contributions are threefold. First, we prove that, under the independent cascade model considering positive and negative influences, MPINS is APX-hard. Subsequently, we present a greedy approximation algorithm to address the MPINS selection problem. Finally, to validate the proposed greedy algorithm, we conduct extensive simulations and experiments on random graphs and seven different realworld data sets that represent small-, medium-, and large-scale networks.