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
An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and s...An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. The results of function optimization show that the algorithm has good searching ability and high convergence speed. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due tσ multivariable inputs, a state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. The simulation results show that the designed controller can control the inverted pendulum successfully.展开更多
An ant colony optimization （ACO）-simulated annealing （SA）-based algorithm is developed for the target assignment problem （TAP） in the air defense （AD） command and control （C2） system of surface to air missi...An ant colony optimization （ACO）-simulated annealing （SA）-based algorithm is developed for the target assignment problem （TAP） in the air defense （AD） command and control （C2） system of surface to air missile （SAM） tactical unit. The accomplishment process of target assignment （TA） task is analyzed. A firing advantage degree （FAD） concept of fire unit （FU） intercepting targets is put forward and its evaluation model is established by using a linear weighted synthetic method. A TA optimization model is presented and its solving algorithms are designed respectively based on ACO and SA. A hybrid optimization strategy is presented and developed synthesizing the merits of ACO and SA. The simulation examples show that the model and algorithms can meet the solving requirement of TAP in AD combat.展开更多
In solving small- to medium-scale travelling salesman problems （TSPs） of both symmetric and asymmetric types, the traditional ant colony optimization （ACO） algorithm could work well, providing high accuracy and sa...In solving small- to medium-scale travelling salesman problems （TSPs） of both symmetric and asymmetric types, the traditional ant colony optimization （ACO） algorithm could work well, providing high accuracy and satisfactory efficiency. However, when the scale of the TSP increases, ACO, a heuristic algorithm, is greatly challenged with respect to accuracy and efficiency. A novel pheromone-trail updating strategy that moderately reduces the iteration time required in real optimization problem-solving is proposed. In comparison with the traditional strategy of the ACO in several experiments, the proposed strategy shows advantages in performance. Therefore, this strategy of pheromone-trail updating is proposed as a valuable approach that reduces the time-complexity and increases its efficiency with less iteration time in real optimization applications. Moreover, this strategy is especially applicable in solving the moderate large-scale TSPs based on ACO.展开更多
A new approach to extraction of affine invariant features of contour image and matching strategy is proposed for shape recognition.Firstly,the centroid distance and azimuth angle of each boundary point are computed.Th...A new approach to extraction of affine invariant features of contour image and matching strategy is proposed for shape recognition.Firstly,the centroid distance and azimuth angle of each boundary point are computed.Then,with a prior-defined angle interval,all the points in the neighbor region of the sample point are considered to calculate the average distance for eliminating noise.After that,the centroid distance ratios（CDRs） of any two opposite contour points to the barycenter are achieved as the representation of the shape,which will be invariant to affine transformation.Since the angles of contour points will change non-linearly among affine related images,the CDRs should be resampled and combined sequentially to build one-by-one matching pairs of the corresponding points.The core issue is how to determine the angle positions for sampling,which can be regarded as an optimization problem of path planning.An ant colony optimization（ACO）-based path planning model with some constraints is presented to address this problem.Finally,the Euclidean distance is adopted to evaluate the similarity of shape features in different images.The experimental results demonstrate the efficiency of the proposed method in shape recognition with translation,scaling,rotation and distortion.展开更多
A weapon target assignment （WTA） model satisfying expected damage probabilities with an ant colony algorithm is proposed. In order to save armament resource and attack the targets effectively, the strategy of the we...A weapon target assignment （WTA） model satisfying expected damage probabilities with an ant colony algorithm is proposed. In order to save armament resource and attack the targets effectively, the strategy of the weapon assignment is that the target with greater threat degree has higher priority to be intercepted. The effect of this WTA model is not maximizing the damage probability but satisfying the whole assignment result. Ant colony algorithm has been successfully used in many fields, especially in combination optimization. The ant colony algorithm for this WTA problem is described by analyzing path selection, pheromone update, and tabu table update. The effectiveness of the model and the algorithm is demonstrated with an example.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
In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this stud...In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.展开更多
针对传统的多重信号分类(multiple signal classification,简称MUSIC)算法定位声源位置时存在计算量大的问题,提出了一种基于宏微导向的蚁群(ant colony optimization,简称ACO)-MUSIC两级相控声源定位算法。首先,利用ACO估算出声源所在...针对传统的多重信号分类(multiple signal classification,简称MUSIC)算法定位声源位置时存在计算量大的问题,提出了一种基于宏微导向的蚁群(ant colony optimization,简称ACO)-MUSIC两级相控声源定位算法。首先,利用ACO估算出声源所在的宏观位置,再用MUSIC算法精确搜索声源所在的微观方位;其次,对提出的算法进行数值仿真,并搭建实验系统进行验证。仿真和实验结果表明,所提出的算法可以高精度、快速地定位出声源所在的位置;在搜索步距为0.05°时,算法的计算复杂度和计算时间仅为传统MUSIC算法的0.25%和2.8%。展开更多
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.
文摘An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. The results of function optimization show that the algorithm has good searching ability and high convergence speed. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due tσ multivariable inputs, a state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. The simulation results show that the designed controller can control the inverted pendulum successfully.
基金Supported by National Natural Science Foundation of China （60496322）, Natural Science Foundation of Beijing （4083034）, and Scientific Research Common Program of Beijing Municipal Commission.of Education （KM200610005020）_ _ _
基金supported by the National Aviation Science Foundation of China(20090196002)
文摘An ant colony optimization （ACO）-simulated annealing （SA）-based algorithm is developed for the target assignment problem （TAP） in the air defense （AD） command and control （C2） system of surface to air missile （SAM） tactical unit. The accomplishment process of target assignment （TA） task is analyzed. A firing advantage degree （FAD） concept of fire unit （FU） intercepting targets is put forward and its evaluation model is established by using a linear weighted synthetic method. A TA optimization model is presented and its solving algorithms are designed respectively based on ACO and SA. A hybrid optimization strategy is presented and developed synthesizing the merits of ACO and SA. The simulation examples show that the model and algorithms can meet the solving requirement of TAP in AD combat.
基金supported by the Fundamental Research Funds for the Central Universities(2015XZD15)the Soft Science Research Project of Guangdong Province(2015A070704015)+1 种基金the Guangdong Province Key Laboratory Open Foundation(2011A06090100101B)the Technology Trading System and Science&Technology Service Network Construction Project of Guangdong Province(2014A040402003)
文摘In solving small- to medium-scale travelling salesman problems （TSPs） of both symmetric and asymmetric types, the traditional ant colony optimization （ACO） algorithm could work well, providing high accuracy and satisfactory efficiency. However, when the scale of the TSP increases, ACO, a heuristic algorithm, is greatly challenged with respect to accuracy and efficiency. A novel pheromone-trail updating strategy that moderately reduces the iteration time required in real optimization problem-solving is proposed. In comparison with the traditional strategy of the ACO in several experiments, the proposed strategy shows advantages in performance. Therefore, this strategy of pheromone-trail updating is proposed as a valuable approach that reduces the time-complexity and increases its efficiency with less iteration time in real optimization applications. Moreover, this strategy is especially applicable in solving the moderate large-scale TSPs based on ACO.
基金supported by the National "111" Project of China(B08036)the Foundation for Science & Technology Research Project of Chongqing (CSTC2010AA5049)the Scientific Research Foundation of State Key Laboratory of Power Transmission Equipment and System Security (2007DA10512709213)
文摘A new approach to extraction of affine invariant features of contour image and matching strategy is proposed for shape recognition.Firstly,the centroid distance and azimuth angle of each boundary point are computed.Then,with a prior-defined angle interval,all the points in the neighbor region of the sample point are considered to calculate the average distance for eliminating noise.After that,the centroid distance ratios（CDRs） of any two opposite contour points to the barycenter are achieved as the representation of the shape,which will be invariant to affine transformation.Since the angles of contour points will change non-linearly among affine related images,the CDRs should be resampled and combined sequentially to build one-by-one matching pairs of the corresponding points.The core issue is how to determine the angle positions for sampling,which can be regarded as an optimization problem of path planning.An ant colony optimization（ACO）-based path planning model with some constraints is presented to address this problem.Finally,the Euclidean distance is adopted to evaluate the similarity of shape features in different images.The experimental results demonstrate the efficiency of the proposed method in shape recognition with translation,scaling,rotation and distortion.
文摘A weapon target assignment （WTA） model satisfying expected damage probabilities with an ant colony algorithm is proposed. In order to save armament resource and attack the targets effectively, the strategy of the weapon assignment is that the target with greater threat degree has higher priority to be intercepted. The effect of this WTA model is not maximizing the damage probability but satisfying the whole assignment result. Ant colony algorithm has been successfully used in many fields, especially in combination optimization. The ant colony algorithm for this WTA problem is described by analyzing path selection, pheromone update, and tabu table update. The effectiveness of the model and the algorithm is demonstrated with an example.
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 by China Postdoctoral Science Foundation(20090460873)
文摘In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.
文摘针对传统的多重信号分类(multiple signal classification,简称MUSIC)算法定位声源位置时存在计算量大的问题,提出了一种基于宏微导向的蚁群(ant colony optimization,简称ACO)-MUSIC两级相控声源定位算法。首先,利用ACO估算出声源所在的宏观位置,再用MUSIC算法精确搜索声源所在的微观方位;其次,对提出的算法进行数值仿真,并搭建实验系统进行验证。仿真和实验结果表明,所提出的算法可以高精度、快速地定位出声源所在的位置;在搜索步距为0.05°时,算法的计算复杂度和计算时间仅为传统MUSIC算法的0.25%和2.8%。
