The knapsack problem is a well-known combinatorial optimization problem which has been proved to be NP-hard. This paper proposes a new algorithm called quantum-inspired ant algorithm （QAA） to solve the knapsack prob...The knapsack problem is a well-known combinatorial optimization problem which has been proved to be NP-hard. This paper proposes a new algorithm called quantum-inspired ant algorithm （QAA） to solve the knapsack problem. QAA takes the advantage of the principles in quantum computing, such as qubit, quantum gate, and quantum superposition of states, to get more probabilistic-based status with small colonies. By updating the pheromone in the ant algorithm and rotating the quantum gate, the algorithm can finally reach the optimal solution. The detailed steps to use QAA are presented, and by solving series of test cases of classical knapsack problems, the effectiveness and generality of the new algorithm are validated.展开更多
A novel multi-chip module(MCM) interconnect test generation scheme based on ant algorithm(AA) with mutation operator was presented.By combing the characteristics of MCM interconnect test generation,the pheromone updat...A novel multi-chip module(MCM) interconnect test generation scheme based on ant algorithm(AA) with mutation operator was presented.By combing the characteristics of MCM interconnect test generation,the pheromone updating rule and state transition rule of AA is designed.Using mutation operator,this scheme overcomes ordinary AA’s defects of slow convergence speed,easy to get stagnate,and low ability of full search.The international standard MCM benchmark circuit provided by the MCNC group was used to verify the approach.The results of simulation experiments,which compare to the results of standard ant algorithm,genetic algorithm(GA) and other deterministic interconnecting algorithms,show that the proposed scheme can achieve high fault coverage,compact test set and short CPU time,that it is a newer optimized method deserving research.展开更多
Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is amo...Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.展开更多
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
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
Based on structural surface normal vector spherical distance and the pole stereographic projection Euclidean distance,two distance functions were established.The cluster analysis of structure surface was conducted by ...Based on structural surface normal vector spherical distance and the pole stereographic projection Euclidean distance,two distance functions were established.The cluster analysis of structure surface was conducted by the use of ATTA clustering methods based on ant colony piles,and Silhouette index was introduced to evaluate the clustering effect.The clustering analysis of the measured data of Sanshandao Gold Mine shows that ant colony ATTA-based clustering method does better than K-mean clustering analysis.Meanwhile,clustering results of ATTA method based on pole Euclidean distance and ATTA method based on normal vector spherical distance have a great consistence.The clustering results are most close to the pole isopycnic graph.It can efficiently realize grouping of structural plane and determination of the dominant structural surface direction.It is made up for the defects of subjectivity and inaccuracy in icon measurement approach and has great engineering value.展开更多
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK ...A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.展开更多
To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used....To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.展开更多
In the cloud era, the control objects are becoming larger and the information processing is more complex, and it is difficult for traditional control systems to process massive data in a timely manner. In view of the ...In the cloud era, the control objects are becoming larger and the information processing is more complex, and it is difficult for traditional control systems to process massive data in a timely manner. In view of the difficulty of data processing in the cloud era, it is extremely important to perform massive data operations through cloud servers. Unmanned aeriel vehicle(UAV) control is the representative of the intelligent field. Based on the ant colony algorithm and incorporating the potential field method, an improved potential field ant colony algorithm is designed. To deal with the path planning problem of UAVs, the potential field ant colony algorithm shortens the optimal path distance by 6.7%, increases the algorithm running time by39.3%, and increases the maximum distance by 24.1% compared with the previous improvement. The cloud server is used to process the path problem of the UAV and feedback the calculation results in real time. Simulation experiments verify the effectiveness of the new algorithm in the cloud environment.展开更多
Improved traditional ant colony algorithms,a data routing model used to the data remote exchange on WAN was presented.In the model,random heuristic factors were introduced to realize multi-path search.The updating mod...Improved traditional ant colony algorithms,a data routing model used to the data remote exchange on WAN was presented.In the model,random heuristic factors were introduced to realize multi-path search.The updating model of pheromone could adjust the pheromone concentration on the optimal path according to path load dynamically to make the system keep load balance.The simulation results show that the improved model has a higher performance on convergence and load balance.展开更多
A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller...A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.展开更多
Ant colony algorithm is a novel simulated ecosystem e volutionary algorithm, which is proposed firstly by Italian scholars M.Dorigo, A . Colormi and V. Maniezzo. Enlightened by the process of ants searching for food ,...Ant colony algorithm is a novel simulated ecosystem e volutionary algorithm, which is proposed firstly by Italian scholars M.Dorigo, A . Colormi and V. Maniezzo. Enlightened by the process of ants searching for food , scholars bring forward this new evolutionary algorithm. This algorithm has sev eral characteristics such as positive feedback, distributed computing and stro nger robustness. Positive feedback and distributed computing make it easier to find better solutions. Based on these characteristics, this algorithm provides a possible way for complicated combinatorial optimization problems, especially i n the field of discrete system. After a brief review on the essential principle, the research state of ant colony algorithm, this paper especially discusses the application of ant colony algorithm in CIMS.展开更多
Travelling Salesman Problem(TSP) is a classical optimization problem and it is one of a class of NP-Problem.The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by ...Travelling Salesman Problem(TSP) is a classical optimization problem and it is one of a class of NP-Problem.The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm(ACA) performing a searching operation and to develop a rule set searcher which approximates the ACA′s searcher.An attribute-oriented induction methodology was used to explore the relationship between an operations′ sequence and its attributes and a set of rules has been developed.At the end of this paper,the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.展开更多
Ants of artificial colony are able to generate good solutions to the famous traveling salesman problem (TSP). We propose an artificial ants algorithm for solving the minimum ratio TSP, which is more general than the s...Ants of artificial colony are able to generate good solutions to the famous traveling salesman problem (TSP). We propose an artificial ants algorithm for solving the minimum ratio TSP, which is more general than the standard TSP in combinatorial optimization area. In the minimum ratio TSP, another criterion concerning each edge is added, that is, the traveling salesman can have a benefit if he travels from one city to another. The objective is to minimize the ratio between total costs or distances and total benefits. The idea of this type of optimization is in some sense quite similar to that of traditional cost-benefit analysis in management science. Computational results substantiate the solution quality and efficiency of the algorithm.展开更多
对具有NP完全难度的网络状态动态变化下的路由问题,提出了一种基于蚁群网络(A n tnet)的蚁群优化分布式Q oS路由算法.算法的主要特点是:(1)采用了动态更新的概率表替代传统的路由表;(2)采用了智能的初始化方法;(3)采用了一种新颖的信息...对具有NP完全难度的网络状态动态变化下的路由问题,提出了一种基于蚁群网络(A n tnet)的蚁群优化分布式Q oS路由算法.算法的主要特点是:(1)采用了动态更新的概率表替代传统的路由表;(2)采用了智能的初始化方法;(3)采用了一种新颖的信息素更新机制;(4)采用一种新的节点选择机制;(5)引入蚂蚁相遇机制.与标准的A n tN et相比,该算法具有更快的收敛速度和较好的吞吐能力.另外,算法同时考虑了满足Q oS度量和负载平衡等问题.展开更多
A new problem of degree-constrained Euclidean Steiner minimal tree is discussed, which is quite useful in several fields. Although it is slightly different from the traditional degree-constrained minimal spanning tree...A new problem of degree-constrained Euclidean Steiner minimal tree is discussed, which is quite useful in several fields. Although it is slightly different from the traditional degree-constrained minimal spanning tree, it is also NP-hard. Two intelligent algorithms are proposed in an attempt to solve this difficult problem. Series of numerical examples are tested, which demonstrate that the algorithms also work well in practice.展开更多
基金supported by the National Natural Science Foundation of China(70871081)the Shanghai Leading Academic Discipline Project(S30504).
文摘The knapsack problem is a well-known combinatorial optimization problem which has been proved to be NP-hard. This paper proposes a new algorithm called quantum-inspired ant algorithm （QAA） to solve the knapsack problem. QAA takes the advantage of the principles in quantum computing, such as qubit, quantum gate, and quantum superposition of states, to get more probabilistic-based status with small colonies. By updating the pheromone in the ant algorithm and rotating the quantum gate, the algorithm can finally reach the optimal solution. The detailed steps to use QAA are presented, and by solving series of test cases of classical knapsack problems, the effectiveness and generality of the new algorithm are validated.
文摘A novel multi-chip module(MCM) interconnect test generation scheme based on ant algorithm(AA) with mutation operator was presented.By combing the characteristics of MCM interconnect test generation,the pheromone updating rule and state transition rule of AA is designed.Using mutation operator,this scheme overcomes ordinary AA’s defects of slow convergence speed,easy to get stagnate,and low ability of full search.The international standard MCM benchmark circuit provided by the MCNC group was used to verify the approach.The results of simulation experiments,which compare to the results of standard ant algorithm,genetic algorithm(GA) and other deterministic interconnecting algorithms,show that the proposed scheme can achieve high fault coverage,compact test set and short CPU time,that it is a newer optimized method deserving research.
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.
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Project(41272304)supported by the National Natural Science Foundation of ChinaProject(51074177)jointly supported by the National Natural Science Foundation and Shanghai Baosteel Group Corporation,ChinaProject(CX2012B070)supported by Hunan Provincial Innovation Fund for Postgraduated Students,China
文摘Based on structural surface normal vector spherical distance and the pole stereographic projection Euclidean distance,two distance functions were established.The cluster analysis of structure surface was conducted by the use of ATTA clustering methods based on ant colony piles,and Silhouette index was introduced to evaluate the clustering effect.The clustering analysis of the measured data of Sanshandao Gold Mine shows that ant colony ATTA-based clustering method does better than K-mean clustering analysis.Meanwhile,clustering results of ATTA method based on pole Euclidean distance and ATTA method based on normal vector spherical distance have a great consistence.The clustering results are most close to the pole isopycnic graph.It can efficiently realize grouping of structural plane and determination of the dominant structural surface direction.It is made up for the defects of subjectivity and inaccuracy in icon measurement approach and has great engineering value.
文摘A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
基金Projects(41161020,41261026) supported by the National Natural Science Foundation of ChinaProject(BQD2012013) supported by the Research starting Funds for Imported Talents,Ningxia University,China+1 种基金Project(ZR1209) supported by the Natural Science Funds,Ningxia University,ChinaProject(NGY2013005) supported by the Key Science Project of Colleges and Universities in Ningxia,China
文摘To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.
基金supported by the Natural Science Foundation of Heilongjiang Province (LH2021E045)。
文摘In the cloud era, the control objects are becoming larger and the information processing is more complex, and it is difficult for traditional control systems to process massive data in a timely manner. In view of the difficulty of data processing in the cloud era, it is extremely important to perform massive data operations through cloud servers. Unmanned aeriel vehicle(UAV) control is the representative of the intelligent field. Based on the ant colony algorithm and incorporating the potential field method, an improved potential field ant colony algorithm is designed. To deal with the path planning problem of UAVs, the potential field ant colony algorithm shortens the optimal path distance by 6.7%, increases the algorithm running time by39.3%, and increases the maximum distance by 24.1% compared with the previous improvement. The cloud server is used to process the path problem of the UAV and feedback the calculation results in real time. Simulation experiments verify the effectiveness of the new algorithm in the cloud environment.
基金Sponsored by the National High Technology Research and Development Program of China(2006AA701306)the National Innovation Foundation of Enterprises(05C26212200378)
文摘Improved traditional ant colony algorithms,a data routing model used to the data remote exchange on WAN was presented.In the model,random heuristic factors were introduced to realize multi-path search.The updating model of pheromone could adjust the pheromone concentration on the optimal path according to path load dynamically to make the system keep load balance.The simulation results show that the improved model has a higher performance on convergence and load balance.
文摘A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.
文摘Ant colony algorithm is a novel simulated ecosystem e volutionary algorithm, which is proposed firstly by Italian scholars M.Dorigo, A . Colormi and V. Maniezzo. Enlightened by the process of ants searching for food , scholars bring forward this new evolutionary algorithm. This algorithm has sev eral characteristics such as positive feedback, distributed computing and stro nger robustness. Positive feedback and distributed computing make it easier to find better solutions. Based on these characteristics, this algorithm provides a possible way for complicated combinatorial optimization problems, especially i n the field of discrete system. After a brief review on the essential principle, the research state of ant colony algorithm, this paper especially discusses the application of ant colony algorithm in CIMS.
文摘Travelling Salesman Problem(TSP) is a classical optimization problem and it is one of a class of NP-Problem.The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm(ACA) performing a searching operation and to develop a rule set searcher which approximates the ACA′s searcher.An attribute-oriented induction methodology was used to explore the relationship between an operations′ sequence and its attributes and a set of rules has been developed.At the end of this paper,the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.
基金This project was supported by the Shanghai Education Development Foundation (No.2000SG30).
文摘Ants of artificial colony are able to generate good solutions to the famous traveling salesman problem (TSP). We propose an artificial ants algorithm for solving the minimum ratio TSP, which is more general than the standard TSP in combinatorial optimization area. In the minimum ratio TSP, another criterion concerning each edge is added, that is, the traveling salesman can have a benefit if he travels from one city to another. The objective is to minimize the ratio between total costs or distances and total benefits. The idea of this type of optimization is in some sense quite similar to that of traditional cost-benefit analysis in management science. Computational results substantiate the solution quality and efficiency of the algorithm.
文摘对具有NP完全难度的网络状态动态变化下的路由问题,提出了一种基于蚁群网络(A n tnet)的蚁群优化分布式Q oS路由算法.算法的主要特点是:(1)采用了动态更新的概率表替代传统的路由表;(2)采用了智能的初始化方法;(3)采用了一种新颖的信息素更新机制;(4)采用一种新的节点选择机制;(5)引入蚂蚁相遇机制.与标准的A n tN et相比,该算法具有更快的收敛速度和较好的吞吐能力.另外,算法同时考虑了满足Q oS度量和负载平衡等问题.
基金the National Natural Science Foundation of China (70471065)the Shanghai Leading Academic Discipline Project (T0502).
文摘A new problem of degree-constrained Euclidean Steiner minimal tree is discussed, which is quite useful in several fields. Although it is slightly different from the traditional degree-constrained minimal spanning tree, it is also NP-hard. Two intelligent algorithms are proposed in an attempt to solve this difficult problem. Series of numerical examples are tested, which demonstrate that the algorithms also work well in practice.
