When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristi...When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristics of channel in power line carrier communication of low voltage distribution grid. The algorithm is easy to fall into premature and local optimization. Proposed an automatic network algorithm based on improved transmission delay and the load factor as the evaluation factors. With the requirements of QoS, a logical topology of power line communication network is established. By the experiment of MATLAB simulation, verify that the improved Dynamic hybrid ant colony genetic algorithm (DH_ACGA) algorithm has improved the communication performance, which solved the QoS routing problems of power communication to some extent.展开更多
Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the node...Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).展开更多
The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a...The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a trust-vector, which figures the trust intensity between this entity and the others. The trust intensity is dynamic due to the time and the inter-operation between two entities, a method is proposed to quantify this change based on the mind of ant colony algorithm and then an algorithm for the transfer of trust relation is also proposed. Furthermore, this paper analyses the influence to the trust intensity among all entities that is aroused by the change of trust intensity between the two entities, and presents an algorithm to resolve the problem. Finally, we show the process of the trusts＇ change that is aroused by the time＇s lapse and the inter-operation through an instance.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
The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was ...The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.展开更多
This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satell...This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.展开更多
核事故的发生具有不可预测性和破坏性,为应急车辆制定合理的疏散计划将危险区域的人员撤离至安置点,可以有效减少人员所受到的伤害。针对核事故下应急车辆路径规划问题,以累积辐射剂量为评价指标,提出了一种基于混合蚁群算法(Hybrid ant...核事故的发生具有不可预测性和破坏性,为应急车辆制定合理的疏散计划将危险区域的人员撤离至安置点,可以有效减少人员所受到的伤害。针对核事故下应急车辆路径规划问题,以累积辐射剂量为评价指标,提出了一种基于混合蚁群算法(Hybrid ant colony algorithm,HACO)的车辆路径规划方法。首先,利用模糊网络建立了时间窗内疏散路径平均通行时间期望模型,同时结合累积辐射剂量计算模型,建立了能够随时间变化的动态累积辐射剂量计算模型。然后在蚁群算法迭代过程中引入模拟退火算法,并且在邻域搜索中引入A*算法启发式思想,提高了算法全局寻优能力。为进一步提高算法的局部搜索能力,引入帕累托排序方式,在蚁群算法信息素更新方式中加入距离对信息素增量的影响。仿真结果表明:HACO算法相较于蚁群算法平均收敛值提高了31%,稳定性提高了30%,能够为核事故下疏散路径规划预案的制定提供技术支持。展开更多
文摘When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristics of channel in power line carrier communication of low voltage distribution grid. The algorithm is easy to fall into premature and local optimization. Proposed an automatic network algorithm based on improved transmission delay and the load factor as the evaluation factors. With the requirements of QoS, a logical topology of power line communication network is established. By the experiment of MATLAB simulation, verify that the improved Dynamic hybrid ant colony genetic algorithm (DH_ACGA) algorithm has improved the communication performance, which solved the QoS routing problems of power communication to some extent.
文摘Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).
基金Supported by the National Natural Science Foun-dation of China (60403027) Natural Science Foundation of HubeiProvince (2005ABA258) Open Foundation of State Key Labora-tory of Software Engineering (SKLSE05-07)
文摘The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a trust-vector, which figures the trust intensity between this entity and the others. The trust intensity is dynamic due to the time and the inter-operation between two entities, a method is proposed to quantify this change based on the mind of ant colony algorithm and then an algorithm for the transfer of trust relation is also proposed. Furthermore, this paper analyses the influence to the trust intensity among all entities that is aroused by the change of trust intensity between the two entities, and presents an algorithm to resolve the problem. Finally, we show the process of the trusts＇ change that is aroused by the time＇s lapse and the inter-operation through an instance.
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National Natural Science Foundation of China(No.70971020)the Subject of Ministry of Education of Hunan Province,China(No.13C818)+3 种基金the Project of Industrial Science and Technology Support of Hengyang City,Hunan Province,China(No.2013KG63)the Open Project Program of Artificial Intelligence Key Laboratory of Sichuan Province,Sichuan University of Science and Engineering,China(No.2012RYJ03)the Fund Project of Humanities and Social Sciences,Ministry of Education of China(No.13YJCZH147)the Special Fund for Shanghai Colleges' Outstanding Young Teachers' Scientific Research Projects,China(No.ZZGJD12033)
文摘The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.
基金supported by the National Natural Science Foundation of China(7127106671171065+1 种基金71202168)the Natural Science Foundation of Heilongjiang Province(GC13D506)
文摘This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.
文摘核事故的发生具有不可预测性和破坏性,为应急车辆制定合理的疏散计划将危险区域的人员撤离至安置点,可以有效减少人员所受到的伤害。针对核事故下应急车辆路径规划问题,以累积辐射剂量为评价指标,提出了一种基于混合蚁群算法(Hybrid ant colony algorithm,HACO)的车辆路径规划方法。首先,利用模糊网络建立了时间窗内疏散路径平均通行时间期望模型,同时结合累积辐射剂量计算模型,建立了能够随时间变化的动态累积辐射剂量计算模型。然后在蚁群算法迭代过程中引入模拟退火算法,并且在邻域搜索中引入A*算法启发式思想,提高了算法全局寻优能力。为进一步提高算法的局部搜索能力,引入帕累托排序方式,在蚁群算法信息素更新方式中加入距离对信息素增量的影响。仿真结果表明:HACO算法相较于蚁群算法平均收敛值提高了31%,稳定性提高了30%,能够为核事故下疏散路径规划预案的制定提供技术支持。
